DE102021119454A1 - MACHINE CONTROL USING A PREDICTIVE MAP - Google Patents

Abstract

One or more information cards are obtained from an agricultural work machine. The one or more information maps depict one or more agricultural characteristic values at different geographic locations of a field. An in situ sensor on the agricultural work machine senses an agricultural feature as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural feature at various locations in the field based on a relationship between the values in the one or more information maps and the agricultural feature sensed by the in situ sensor . The predictive map can be output and used in automated machine control.

Description

FIELD OF DESCRIPTION

The present description relates to agricultural machines, forestry machines, construction machines and lawn care machines.

BACKGROUND

There are many different types of agricultural machinery. Some agricultural machines include harvesters such as combine harvesters, sugar cane harvesters, cotton harvesters, forage harvesters, and windrowers. Some harvesters can also be fitted with different types of headers to harvest different types of crops.

A variety of different field conditions have a number of detrimental effects on the harvesting process. Therefore, an operator may attempt to modify the controls of the harvester when encountering such conditions during a harvesting operation.

SUMMARY

One or more information cards are obtained from an agricultural work machine. The one or more information maps depict one or more agricultural characteristic values at different geographic locations of a field. An in situ sensor on the agricultural work machine senses an agricultural feature as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural feature at various locations in the field based on a relationship between the values in the one or more information maps and the agricultural feature sensed by the in situ sensor . The predictive map can be output and used in automated machine control.

character list

• 1 12 is a partially pictorial, partially schematic representation of an example agricultural harvester.
• 2 12 is a block diagram showing in more detail some portions of an agricultural harvesting machine according to some examples of the present disclosure.
• the 3a-3b (herein together as 3 ) show a flow chart illustrating an example of the operation of an agricultural harvesting machine in generating a map.
• 4 Figure 12 is a block diagram showing an example of a predictive model generator and a predictive map generator.
• 5 12 is a flowchart showing an example of an operation of an agricultural harvester in receiving a map, detecting an in situ feature, and generating a functional predictive map for presentation or use in controlling the agricultural harvester during a harvesting operation, or both.
• 6A Figure 12 is a block diagram showing an example of a predictive model generator and a predictive map generator.
• 6B Figure 12 is a block diagram showing some examples of in situ sensors.
• 7 FIG. 14 is a flow chart illustrating an example of operating an agricultural harvester that includes generating a functional predictive map using a pre-information map and in situ sensor input.
• 8th Figure 12 is a block diagram showing an example of a control zone generator.
• 9 is a flowchart showing an example of the operation of the in 8th control zone generator shown.
• 10 Figure 12 illustrates a flow chart showing an example of operation of a control system in selecting a target setting value to control an agricultural harvester.
• 11 Fig. 12 is a block diagram showing an example of an operator interface control.
• 12 Fig. 12 is a flowchart showing an example operator interface control.
• 13 Figure 12 is a pictorial representation showing an example operator interface display.
• 14 Figure 12 is a block diagram showing an example agricultural harvester in communication with a remote server environment.
• the 15-17 show examples of mobile devices that can be used in an agricultural harvesting machine.
• 18 Figure 12 shows a block diagram of an example of a computing environment that may be used in an agricultural harvester.

DETAILED DESCRIPTION

For a better understanding of the principles of the present disclosure, reference will now be made to the examples illustrated in the drawings and specific language will be used to describe them. However, it should be understood that no limitation on the scope of the disclosure is intended. All alterations and further modifications to the described devices, systems, methods, and any further application of the principles of the present disclosure are contemplated fully as would normally occur to those skilled in the art to which the disclosure pertains. In particular, it is fully contemplated that the features, components, steps, or combination thereof, described with respect to one example may be combined with the features, components, steps, or combination thereof, described with respect to other examples present disclosure.

The present description relates to the use of in situ data collected concurrently with a farming operation in combination with previous data to generate a functional predictive map and more particularly a functional predictive crop moisture map. In some examples, the functional predictive crop moisture map may be used to control an agricultural work machine, such as an agricultural harvester. The performance of an agricultural harvester can be affected when the agricultural harvester enters areas of differing crop moisture unless the machine settings are also changed. For example, in an area of reduced crop moisture, the agricultural harvesting machine may be moving rapidly over the ground and moving material through the machine at an increased feed rate. Upon encountering an area of increased crop moisture, the above ground speed of the agricultural harvester may decrease, thereby reducing the feed rate into the agricultural harvester, or the agricultural harvester may clog, lose grain, or experience other problems. For example, areas of a field with increased crop moisture may have crop plants with different physical structures than areas of the field with reduced crop moisture. For example, in areas of increased crop moisture, some plants may have thicker stems, broader leaves, larger or more heads, etc. In other examples, areas of a field with increased crop moisture due to increased mass of crop plants may have crop plants with a higher biomass value due to their moisture content. These variations in plant structure in areas of different crop moisture may also cause the performance of the agricultural harvester to vary as the agricultural harvester moves through areas of different crop moisture.

A vegetative index map illustratively maps vegetative index values, which may be indicative of vegetative growth, across various geographic locations in a field of interest. An example of a vegetative index includes a normalized difference vegetative index (NDVI). There are many other vegetative indices and all such vegetative indices are within the scope of the present disclosure. In some examples, a vegetative index may be derived from sensor readings of one or more bands of electromagnetic radiation reflected from the plants. Without limitation, these bands can be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum.

A vegetative index map can thus be used to identify the presence and location of vegetation. In some examples, a vegetative index map allows crops to be identified and georeferenced in the presence of bare soil, crop residue, or other vegetation, including crops or weeds. For example, at the beginning of a growing season, when a crop is in a state of growth, the vegetative index can indicate the progress of development of the crop. Therefore, if a vegetative index map is made at the beginning of the growing season or in the middle of the growing season, the vegetative index map can indicate the progress of development of the crop plants. For example, the vegetative index map can indicate whether the plant is underdeveloped, whether an adequate cover has been established, or whether other plant attributes are indicative of plant development.

A topographic map illustratively maps elevations of the ground across various geographic locations in a field of interest. Because ground slope indicates a change in elevation, providing two or more elevation values allows the slope to be calculated over the areas with known elevation values. Greater granularity of slope can be achieved by providing more regions with known elevation values. As an agricultural harvester travels over the terrain in known directions, the pitch and roll of the agricultural harvester can be determined based on the slope of the ground (ie, areas of varying elevation). Topographical features, referred to below, may include, but are not limited to, elevation, slope (e.g., including machine orientation relative to the slope), and soil profile (e.g., unevenness).

A soil property map illustratively maps soil property values (which may indicate soil type, soil moisture, soil cover, soil structure, as well as various other soil properties) across various geographic locations in a field of interest. The soil property maps thus provide georeferenced soil properties over a field of interest. Soil type can refer to taxonomic units in soil science, with each soil type having defined sets of common properties. For example, soil types may include sandy soil, clay soil, silt soil, peat soil, chalky soil, clay soil, and various other soil types. Soil moisture can refer to the amount of water held or otherwise contained in the soil. Soil moisture is also known as soil wetness. Land cover may refer to the amount of objects or materials covering the ground, including vegetation material such as crop residues or cover crops, debris, as well as various other objects or materials. In agricultural terms, soil cover often includes a level of residual crop residues, such as a residual mass of plant stems, as well as a level of catch crops. Soil structure can refer to the arrangement of solid parts of the soil and the pore space located between the solid parts of the soil. Soil structure may include the manner in which individual particles, such as B. individual particles of sand, silt and clay are assembled. Soil structure can be described in terms of grade (degree of aggregation), class (average size of aggregates), and shape (types of aggregates), as well as a variety of other descriptions. These are just examples. Various other features and properties of the soil can be mapped as soil property values on a soil property map.

These soil property maps may be generated based on data collected during another operation corresponding to the field of interest, for example previous farming operations in the same season, such as planting operations or spraying operations, as well as previous farming operations performed in previous seasons , such as a previous harvest. The agricultural machines that perform these agricultural operations may have on-board sensors that detect properties indicative of soil properties, for example traits indicative of soil type, soil moisture, soil cover, soil structure, and various other traits indicative of various other soil properties. Additionally, operating characteristics, machine settings, or machine performance characteristics of the agricultural machines during previous operations, along with other data, can be used to generate a soil property map. For example, header elevation data indicating an elevation of an agricultural harvester's header over various geographic locations in the field of interest during a previous harvest operation, along with weather data reflecting weather conditions such as precipitation data or wind data during a transition period (such as the period from the time of previous harvesting process and the generation of the soil property map) can be used to generate a soil moisture map. For example, by knowing the height of the header, the amount of remaining crop residue, such as crop stalks, can be known or estimated, and together with precipitation data, the soil moisture level can be predicted. This is only an example.

In other examples, investigations of the field of interest may be performed, either by various machines with sensors, such as imaging systems, or by humans. The data collected during these surveys can be used to generate a soil property map. For example, an aerial survey of the field of interest may be performed, mapping the field and generating a soil property map from the imagery. In another example, a human may go into the field to collect various data or samples with or without the assistance of devices such as sensors, and a soil property map of the field may be generated based on the data or samples. For example, a human can take a core sample at different geographic locations across the field from gather interest. These core samples can be used to generate soil property maps of the field. In other examples, the soil property maps may be based on user or operator input, such as input from a farm manager, who may provide various data collected or observed by the user or operator.

In addition, the ground lot map may be obtained from remote sources such as third party suppliers or government agencies such as the USDA Natural Resources Conservation Service (NRCS), the United States Geological Survey (USGS), and various other remote sources.

In some examples, a ground property map may be derived from sensor readings from one or more bands of electromagnetic radiation reflected from the ground (or the surface of the field). Without limitation, these bands can be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum.

A historical crop moisture map illustratively maps crop moisture values across various geographic locations in one or more fields of interest. These historical crop moisture maps are collected from previous harvesting operations on the field or fields. A crop moisture map may display crop moisture in crop moisture value units. An example of a crop moisture value unit includes a numeric value, such as a percentage, for example. In some examples, a historical crop moisture map may be derived from sensor readings from one or more crop moisture sensors. Without limitation, these crop moisture sensors may include, but are not limited to, a capacitance sensor, a microwave sensor, or a conductivity sensor. In some examples, the crop moisture sensor may use one or more bands of electromagnetic radiation to detect crop moisture.

The present discussion thus continues with respect to examples in which a system receives one or more of a historical crop moisture map of a field, vegetative index map, a topographic map, a soil feature map, or a map generated during a preliminary operation, and also an In -situ sensor used to recognize a trait as a variable indicative of crop moisture during a harvesting operation. The system creates a model that models a relationship between the historical crop moisture values, vegetative index values, topographical feature values, or soil property values from one or more of the received maps and the in situ data from the in situ sensor. The model is used to generate a functional predictive crop moisture map that predicts crop moisture in the field. The functional predictive crop moisture map generated during the harvesting process may be presented to an operator or other user, or used to automatically control an agricultural harvesting machine during the harvesting process, or both. In some examples, the map received by the system depicts values of characteristics other than crop moisture (e.g., “non-crop moisture values”), such as vegetative index values, topographical feature values, or soil property values. In one example, the map received from the system depicts historical values of crop moisture.

1 12 is a partially pictorial, partially schematic representation of a self-propelled agricultural harvester 100. In the example illustrated, the agricultural harvester 100 is a combine harvester. Further, while combines are provided as examples throughout this disclosure, it should be understood that the present description is applicable to other types of harvesting machines, such as cotton harvesters, sugarcane harvesters, forage harvesters, windrowers, or other agricultural work machines. Accordingly, the present disclosure is intended to encompass the various types of harvesters described and is thus not limited to combines. Additionally, the present disclosure is directed to other types of work machines, such as agricultural seeders and sprayers, construction machines, forest machines, and lawn care machines, where predictive map generation may be applicable. Accordingly, the present disclosure is intended to encompass these various types of harvesters and other work machines and is thus not limited to combines.

As in 1 1, agricultural harvester 100 illustratively includes an operator's cab 101 that may include a variety of different operator interface mechanisms for controlling agricultural harvester 100 . Agricultural harvester 100 includes headers, such as a header 102 and a cutter, indicated generally at 104. Agricultural harvester 100 also includes a feeder housing 106, a feeder accelerator 108, and a thresher, indicated generally at 110. The feeder housing 106 and feed accelerator 108 form part of a material handling subsystem 125. The header 102 is pivotally coupled to a frame 103 of the agricultural header 100 along the pivot axis 105 . One or more actuators 107 power movement of header 102 about axis 105 in the direction generally indicated by arrow 109 . Thus, a vertical position of the header 102 (the header height) above the ground 111 over which the header 102 travels can be controlled by actuating the actuator 107 . Although in 1 not shown, agricultural harvesting machine 100 may also include one or more actuators operable to apply a pitch angle, a roll angle, or both to the header 102 or portions of the header 102 . Pitch refers to an angle at which the cutter assembly 104 engages the crop. The angle of inclination is increased, for example, by controlling the header 102 so that a distal edge 113 of the cutter 104 is directed more towards the ground. The angle of inclination is reduced by controlling the header 102 so that the distal edge 113 of the cutter 104 is directed further away from the ground. The roll angle refers to the orientation of the header 102 about the front-to-back longitudinal axis of the agricultural harvesting machine 100.

The thresher 110 illustratively includes a threshing rotor 112 and a set of concaves 114. Further, the agricultural harvester 100 also includes a separator 116. The agricultural harvester 100 also includes a cleaning subsystem or cleaning shoe (collectively referred to as the cleaning subsystem 118) that a cleaning fan 120, a chopper 122 and a screen 124 includes. The material handling subsystem 125 also includes a discharge drum 126, a tailings elevator 128, a clean grain elevator 130, and an unloading auger 134 and chute 136. The clean grain elevator conveys clean grain into the clean grain tank 132. The agricultural harvester 100 also includes a tailings subsystem 138 that a chopper 140 and a spreader 142. Agricultural harvester 100 also includes a propulsion subsystem that includes an engine that drives ground engaging components 144, such as wheels or tracks. In some examples, a combine may include more than one of the above subsystems within the scope of the present disclosure. In some examples, agricultural harvester 100 may include left and right hand cleaning subsystems, separators, etc., shown in 1 are not shown.

In operation and for overview, agricultural harvester 100 illustratively moves through a field in the direction indicated by arrow 147 . During movement of the agricultural harvesting machine 100, the header 102 (and the associated reel 164) engages the crop to be harvested and collects the crop towards the cutting device 104. An operator of the agricultural harvesting machine 100 can be a local human operator, a remote human operator or be an automated system. An operator command is a command from an operator. The operator of agricultural harvesting machine 100 may specify one or more height settings, pitch angle settings, or roll angle settings for header 102 . For example, the operator enters a setting or settings into a control system, described in more detail below, that controls actuator 107 . The control system may also receive a setting from the operator to set up the pitch and roll angle of the header 102 and implement the settings entered by associated actuators, not shown, that operate to change the pitch and roll angle of the header 102. The actuator 107 maintains the header 102 at a height above the ground 111 based on a height adjustment and at desired pitch and roll angles, if any. Each of the elevation, roll, and pitch settings can be implemented independently of the others. The control system responds to header errors (e.g., the difference between the elevation setting and the measured height of the header 104 above the ground 111 and, in some examples, pitch angle and roll angle errors) with a responsiveness that is determined based on a selected sensitivity level. When the sensitivity level is set to a higher sensitivity level, the control system will respond to smaller header position errors and attempt to reduce the detected errors faster than when the sensitivity is at a lower sensitivity level.

Returning to the description of the operation of agricultural harvesting machine 100, after the crop has been cut by cutter 104, the separated crop is moved by a conveyor in feed housing 106 toward feed accelerator 108, which accelerates the crop into threshing unit 110. The crop is threshed by the rotor 112 which rotates the crop against the concaves 114 . The threshed harvest Acceptable material is moved by a separator rotor in the separator 116 with a portion of the residue being moved towards the residue subsystem 138 by the discharge drum 126 . The portion of the tailings that is transferred to tailings subsystem 138 is shredded by tailings chopper 140 and spread by spreader 142 on the field. In other configurations, the residue is released from the agricultural harvester 100 in a windrow. In other examples, residue subsystem 138 may include weed control devices (not shown), such as seed baggers or other seed collectors or seed crushers or other seed destroyers.

The grain falls onto the cleaning subsystem 118. The chopper 122 separates a portion of coarser pieces of material from the grain and the screen 124 separates a portion of finer pieces of material from the clean grain. The clean grain falls onto an auger that moves the grain to an inlet end of the clean grain elevator 130 and the clean grain elevator 130 moves the clean grain up, thereby separating the clean grain in the clean grain tank 132 . Debris is removed from the cleaning subsystem 118 by the air flow of the cleaning fan 120 . The cleaning fan 120 directs air along an airflow path up through the screens and choppers. The airflow carries residue rearward in agricultural harvesting machine 100 toward residue handling subsystem 138.

The tailings elevator 128 returns the tailings to the thresher 110 where the tailings are re-threshed. Alternatively, the tailings can also be fed to a separate after-threshing mechanism by a tailings elevator or other transport device, where the tailings are also after-threshed.

1 Also shows that in one example, agricultural harvester 100 includes ground speed sensor 146, one or more separator loss sensors 148, a clean grain camera 150, a forward-looking image capture mechanism 151, which may be in the form of a stereo or mono camera, and one or more loss sensors 152 provided in the cleaning subsystem 118 is included.

The ground speed sensor 146 detects the ground speed of the agricultural harvesting machine 100 over the ground. The ground speed sensor 146 may detect the ground speed of the agricultural harvesting machine 100 by sensing the rotational speed of ground engaging components (such as wheels or tracks), a driveshaft, an axle, or other components. In some cases, vehicle speed may be sensed using a positioning system, such as a global positioning system (GPS), dead reckoning system, remote navigation system (LORAN), or a variety of other systems or sensors that provide an indication of vehicle speed.

The loss sensors 152 illustratively provide an output signal indicative of the amount of grain loss occurring on both the right and left sides of the cleaning subsystem 118 . In some examples, the sensors 152 are impact sensors that count grain impacts per unit time or per unit distance to provide an indication of grain loss occurring at the cleaning subsystem 118 . The impact sensors for the right and left sides of the shoe 118 can provide individual signals or a combined or aggregated signal. In some examples, the sensors 152 can include a single sensor, as opposed to separate sensors provided for each cleaning subsystem 118 .

The separator loss sensor 148 provides a signal indicative of grain loss in the left and right separators indicated in 1 are not shown separately. The separator loss sensors 148 may be associated with the left and right separators and may provide separate grain loss signals or a combined or aggregated signal. In some cases, detecting grain loss in the separators can also be done using a variety of different types of sensors.

Agricultural harvester 100 may also include other sensors and measurement mechanisms. For example, the agricultural harvesting machine 100 may include one or more of the following sensors: a header height sensor that detects a height of the header 102 above the ground 111; stability sensors that detect oscillatory or bouncing motion (and amplitude) of agricultural harvesting machine 100; a residue setting sensor configured to detect whether the agricultural harvesting machine 100 is configured to shred the residue, create a windrow, etc.; a shoe fan speed sensor to detect the speed of the cleaning fan 120; a concave gap sensor that senses the gap between the rotor 112 and the concaves 114; a threshing rotor speed sensor that detects a rotor speed of the rotor 112; a returns feature sensor; a chopper column sensor that senses the size of the openings in the chopper 122; a wire gap sensor that senses the size of the openings in the wire 124; a non-grain material (MOG) moisture sensor that detects a moisture content of the MOG passing through the agricultural harvesting machine 100; one or more machine setting sensors configured to sense various configurable settings of agricultural harvesting machine 100; a machine orientation sensor that detects the orientation of the agricultural harvesting machine 100; and crop property sensors that sense a variety of different types of crop properties, such as crop type, crop moisture, and other crop properties. The crop characteristic sensors can also be configured to detect the characteristics of the separated crop during processing by the agricultural harvesting machine 100 . For example, in some cases, the crop property sensors may sense grain quality, such as broken grain, MOG values; grain components such as starches and protein; and grain feed rate as the grain moves through the feeder house 106, the clean grain elevator 130, or elsewhere in the agricultural harvester 100. Characteristics of the grain can also be recorded. Grain characteristics can include grain moisture, grain size, and grain test weight. Tailing characteristics include tailings level, tailings flow, tailings volume and tailings composition. The crop property sensors may also sense the rate of advancement of biomass through the feeder house 106 , through the separator 116 , or elsewhere in the agricultural harvester 100 . The crop property sensors may also sense haul rate as a mass flow rate of grain through the elevator 130 or through other portions of the agricultural harvesting machine 100, or provide other output signals indicative of other sensed quantities. The crop property sensors may include one or more crop moisture sensors that sense moisture of crops being harvested by the agricultural harvesting machine. An internal material distribution sensor can detect the material distribution within the agricultural harvesting machine 100 .

The crop moisture sensors may include a capacitive moisture sensor. In one example, the capacitive moisture sensor may include a moisture measurement cell for receiving the crop sample and a capacitor for determining the dielectric properties of the sample. In other examples, the crop moisture sensor may be a microwave sensor or a conductivity sensor. In other examples, the crop moisture sensor may use wavelengths of electromagnetic radiation to sense the moisture content of the crop. The crop moisture sensor may be located within the feeder housing 106 (or otherwise have sensing access to crop material within the feeder housing 106 ) and configured to sense the moisture of harvested crop material passing through the feeder housing 106 . In other examples, the crop moisture sensor may be located at other locations within agricultural harvesting machine 100, such as in the clean grain elevator, in a clean grain auger, or in a grain tank. Note that these are merely examples of crop moisture sensors and that various other crop moisture sensors are contemplated.

In some examples, crop moisture is the ratio of water to other plant materials, such as dry matter of grain or total biomass. In other examples, crop moisture may be related to an amount of water on the outside of a plant, such as dew, frost, or rain. Crop moisture can be measured in absolute terms, such as water as a percentage of a mass or volume of material. In other examples, crop moisture may be reported in relative categories such as high, medium, low, wet, typical/normal, dry, etc. Crop moisture can be measured in a number of ways. In some examples, crop moisture may refer to crop color, such as the green or the distribution of brown and green areas across the plant. In one example, it may be related to the rate at which crop color changes from green to brown during aging. In other examples, crop moisture may be measured using properties where electric fields or electromagnetic waves interact with water molecules. These properties include, without limitation, dielectric constant, resonance, reflection, absorption, or transmission. In still other examples, crop moisture may relate to a plant's morphology, such as 3D leaf shape, such as "corn leaf rolling", stomata diameter, which affects plant temperature, and relative stem diameter over time.

Before describing how the agricultural harvesting machine 100 generates a functional predictive crop moisture map and uses the functional predictive crop moisture map for display or control, a brief description of some elements of the agricultural harvesting machine 100 and their operation is provided. The description of 2 and 3 describes receiving a general type of advance information card and combining transcribing information from the preliminary information map with a georeferenced sensor signal generated by an in situ sensor, the sensor signal being indicative of a feature on the field, such as features of crop or weeds present in the field. Field characteristics may include, but are not limited to, characteristics of a field such as slope, weed intensity, weed species, soil moisture, surface quality; Characteristics of crop properties, such as crop height, crop moisture, crop density, crop condition; Characteristics of grain properties, such as grain moisture, grain size, grain test weight; and machine performance characteristics such as loss levels, job quality, fuel consumption, and power utilization. A relationship between the feature values derived from in situ sensor signals and the advance information map values is identified and this relationship is used to generate a new functional predictive map. A functional predictive map predicts values at various geographic locations in a field, and one or more of these values can be used to control a machine, such as one or more subsystems of an agricultural harvester. In some cases, a functional predictive map may be presented to a user, such as an operator of an agricultural work machine, which may be an agricultural harvester. A functional predictive map may be presented to a user visually, such as via a display, haptically, or audibly. The user can interact with the functional predictive map to perform editing operations and other user interface operations. In some cases, a functional predictive map can be used for one or more of controls of an agricultural work machine, such as an agricultural harvester, presentation to an operator or other user, and presentation to an operator or user for interaction by the operator or user.

After the general approach regarding the 2 and 3 , a more specific approach to generating a functional predictive crop moisture map that can be presented to an operator or user or used to control the agricultural harvesting machine 100 or both is described with reference to FIG 4 and 5 described. Although the present discussion continues with respect to the agricultural harvester, and more particularly a combine harvester, other types of agricultural harvesters or other agricultural work machines are within the scope of the present disclosure.

2 FIG. 12 is a block diagram showing some portions of an exemplary agricultural harvesting machine 100. FIG. 2 shows that the agricultural harvester 100 illustratively includes one or more processors or servers 201, a data store 202, a geographic position sensor 204, a communication system 206 and one or more in situ sensors 208 that one or more agricultural features of a field simultaneously with record a harvesting process. An agricultural trait can include any trait that can affect the harvesting process. Some examples of agricultural characteristics include characteristics of the harvester, the field, the crops in the field, and the weather. Other types of agricultural characteristics are also considered. The in situ sensors 208 generate values corresponding to the sensed features. Agricultural harvester 100 also includes a predictive model or relationship generator (hereinafter collectively referred to as "predictive model generator 210"), a predictive map generator 212, a control zone generator 213, a control system 214, one or more controllable subsystems 216, and an operator interface mechanism 218. The Agricultural harvester 100 may also include a variety of other agricultural harvester functions 220 . The in situ sensors 208 include, for example, onboard sensors 222, remote sensors 224, and other sensors 226 that sense characteristics of a field during a farming operation. The predictive model generator 210 illustratively includes an information variable-to-in situ variable model generator 228 , and the predictive model generator 210 may include other elements 230 . The control system 214 includes the communication system control 229, the operator interface control 231, an adjustment control 232, the path planning control 234, the line speed control 236, the head and roll control 238, the draper belt control 240, the cover plate position control 242, the residue system control 244, the machine cleaning control 245, the zone control 247 and the control system 214 may include other elements 246 . The controllable subsystems 216 include machine and header actuators 248, the propulsion subsystem 250, the steering subsystem 252, the residue subsystem 138, the machine cleaning subsystem 254, and the controllable subsystems 216 may include a variety of other subsystems 256. For example, the control system 214 may generate one or more control signals to control the material handling subsystem 125 to adjust the internal material distribution within the agricultural harvesting machine 100 based on the received functional predictive map (with or without control zones) to control or compensate.

2 12 also shows that agricultural harvester 100 may receive one or more advance information cards 258 . As described below, the preliminary information map(s) includes, for example, a vegetative index map or a vegetation map from a preliminary operation in the field, a topographical map, or a soil property map. However, the prior information map(s) 258 may also include other types of data obtained prior to a harvesting operation or a map from a prior operation, such as historical crop moisture maps from years past, that include context information related to historical crop moisture. Contextual information may include, but is not limited to, one or more weather conditions over a growing season, presence of pests, geographic location, soil types, irrigation, treatment application, etc. Weather conditions may include, but are not limited to, precipitation over the season, hail which can cause crop damage, presence of strong winds, temperature over the season, etc. Some examples of pests are general, insects, fungi, weeds, bacteria, viruses, etc. Some examples of treatment applications are herbicides, pesticides, fungicides, fertilizers, mineral preparations, etc. 2 also shows that an operator 260 can operate the agricultural harvesting machine 100 . The operator 260 interacts with the operator interface mechanisms 218. In some examples, the operator interface mechanisms 218 may include joysticks, levers, a steering wheel, linkages, pedals, buttons, knobs, keypads, user actuable elements (such as icons, buttons, etc.) on a user interface display device, a microphone and speaker (if speech recognition and speech synthesis are provided), as well as a variety of other types of control devices. When a touch-sensitive display system is provided, operator 260 may interact with operator interface mechanisms 218 using touch gestures. These examples described above are provided as illustrative examples and are not intended to limit the scope of the present disclosure. Accordingly, other types of operator interface mechanisms 218 may be used and are within the scope of the present disclosure.

The preliminary information map 258 may be downloaded to the agricultural harvester 100 and stored in the data storage 202 using the communication system 206 or otherwise. In some examples, communication system 206 may be a cellular communication system, a system for communicating over a wide area network or a local area network, a system for communicating over a near field communication network, or a communication system configured to communicate over any of a variety of other networks or combinations to communicate from networks. The communication system 206 may also include a system that facilitates downloading or transferring information to and from a Secure Digital (SD) card or a universal serial bus (USB) card, or both.

The geographic position sensor 204 illustratively senses or detects the geographic position or location of the agricultural harvesting machine 100. The geographic position sensor 204 may include, but is not limited to, a global navigation satellite system (GNSS) receiver that receives signals from a GNSS satellite transmitter. The geographic position sensor 204 may also include a real-time kinematics (RTK) component configured to improve the accuracy of the position data derived from the GNSS signal. The geographic position sensor 204 may include a dead reckoning system, a cellular triangulation system, or a variety of other geographic position sensors.

The in situ sensors 208 can be any of the sensors described above with respect to FIG 1 act. The in situ sensors 208 include onboard sensors 222 mounted onboard the agricultural harvester 100 . Such sensors may include, for example, a crash pad sensor, a radiation attenuation sensor, or an image sensor located within agricultural harvesting machine 100 (such as a clean grain camera). The in situ sensors 208 may also include remote in situ sensors 224 that collect in situ information. In situ data includes data collected by a sensor onboard the agricultural harvester or any sensor where the data is detected during the harvesting process.

Upon retrieval by agricultural harvester 100 , advance information card selector 209 may filter or select one or more specific advance information card(s) 258 for use by predictive model generator 210 . In one example, the prior information card selector 209 selects a card based on a comparison of the context information in the prior information card with the present context information. For example, a histo Irish crop moisture map from one of the recent years where the weather conditions during the growing season were similar to the weather conditions of the current year. Or, for example, a historical crop moisture map from one of the last few years can be selected if the context information is not similar. For example, a historical crop moisture map may be selected for a previous year that was "dry" (ie, exhibited drought or reduced rainfall) while the current year is "wet" (ie, exhibited increased rainfall or flood conditions). There can still be a useful historical relationship, but the relationship can be the other way around. For example, areas that are dry in a drought year may be areas of higher crop moisture in a wet year because these areas can store more water in wet years. Current context information may include context information that goes beyond immediate context information. For example, current context information may include, among other things, a set of information corresponding to the current growing season, a set of dates corresponding to a winter prior to the current growing season, or a set of dates corresponding to multiple years in the past.

The context information can also be used for correlations between areas with similar context properties, regardless of whether the geographic location corresponds to the same location on the prior information map 258 . For example, historical crop moisture values from an area with similar topography or soil characteristics, or both, in other fields can be used as the pre-information map 258 to create the predictive crop moisture map. For example, the contextual feature information associated with a different location may be applied to the location on the prior information map 258 that has similar feature information.

The predictive model generator 210 generates a model that indicates a relationship between the values sensed by the in situ sensor 208 and a feature mapped by the prior information map 258 onto the field. For example, if the prior information map 258 maps a vegetative index value to various locations in the field and the in situ sensor 208 senses a value indicative of crop moisture, then the prior information variable-to-in situ variable model generator 228 generates a predictive Crop moisture model that models the relationship between vegetative index values and crop moisture values. Then, the predictive map generator 212 uses the predictive crop moisture model generated by the predictive model generator 210 to generate a functional predictive crop moisture map that predicts the value of crop moisture at various locations in the field based on the preliminary information map 258 . Or, for example, if the pre-information map 258 maps a historical crop moisture value to various locations in the field and the in situ sensor 208 senses a value indicative of crop moisture, then the pre-information variable-to-in situ variable model generator 228 generates a predictive crop moisture model that models the relationship between historical crop moisture values (with or without contextual information) and in situ crop moisture values. Then, the predictive map generator 212 uses the predictive crop moisture model generated by the predictive model generator 210 to generate a functional predictive crop moisture map that predicts the value of crop moisture at various locations in the field based on the preliminary information map 258 . Or, for example, if the pre-information map 258 maps a topographical feature value to various locations in the field and the in situ sensor 208 detects a value indicative of crop moisture, then the pre-information variable-to-in situ variable model generator 228 generates a predictive crop moisture model that models the relationship between topographical feature values and crop moisture values. Then, the predictive map generator 212 uses the predictive crop moisture model generated by the predictive model generator 210 to generate a functional predictive crop moisture map that predicts the value of crop moisture at various locations in the field based on the preliminary information map 258 . Or, for example, if the pre-information map 258 maps a soil property value to various locations in the field and the in situ sensor 208 detects a value indicative of crop moisture, then the pre-information variable-to-in situ variable model generator 228 generates a predictive crop moisture model that models the relationship between soil property values and crop moisture values. Then, the predictive map generator 212 uses the predictive crop moisture model generated by the predictive model generator 210 to generate a functional predictive crop moisture map that predicts the value of crop moisture at various locations in the field based on the preliminary information map 258 . In some examples, the type of data in functional predictive map 263 may be the same as be the in situ data type sensed by the in situ sensors 208 . In some cases, the type of data in the functional predictive map 263 may have different entities than the data collected from the in situ sensors 208 . In some examples, the type of data in functional predictive map 263 may differ from the type of data sensed by in situ sensors 208 but is related to the type of data sensed by in situ sensors 208 . For example, the in situ data type may indicate the data type in the functional predictive map 263 in some examples. In some examples, the data type in functional predictive map 263 may differ from the data type in advance information map 258 . In some cases, the type of data in functional predictive map 263 may have different units than the data in advance information map 258 . In some examples, the data type in functional predictive map 263 may be different than the data type in pre-information map 258, but has a relationship to the data type in pre-information map 258. For example, in some examples, the data type in pre-information map 258 may be in the functional predictive map 263 indicate the type of data in the functional predictive map 263 in some examples. In some examples, the data type in the functional predictive map 263 differs from one or both of the in situ data type sensed by the in situ sensors 208 and the data type in the advance information map 258. In some examples, the data type is in the functional predictive map 263 is the same as one or both of the in situ data type sensed by the in situ sensors 208 and the data type in the advance information map 258. In some examples, the data type in the functional predictive map 263 is the same as that of the In situ sensors 208 detected in situ data type or the data type in the advance information card 258 and is different from the other.

Continuing with the previous examples, the predictive map generator 212 may use the values in the pre-information map 258 and the model generated by the predictive model generator 210 to generate a functional predictive map 263 that predicts crop moisture at various locations in the field. The predictive map generator 212 thus outputs the predictive map 264 .

As in 2 As shown, predictive map 264 predicts the value of a feature that may be the same feature sensed by in situ sensor(s) 208 or a feature related to the feature sensed by in situ sensor(s) 208 at various positions across the field based on a prior information value in the prior information map 258 at those positions (or positions with similar context information even if they are in a different field) and using the predictive model. For example, if the predictive model generator 210 has generated a predictive model indicating a relationship between a vegetative index value and crop moisture, then given the vegetative index value at various positions across the field, the predictive map generator 212 generates a predictive map 264 that indicates the value of the Predicts crop moisture at various locations across the field. The vegetative index value obtained from the preliminary information map 258 at these locations and the relationship between the vegetative index value and crop moisture obtained from the predictive model are used to generate the predictive map 264 . Or, for example, if the predictive model generator 210 has generated a predictive model that indicates a relationship between a historical crop moisture value and crop moisture, then given the historical crop moisture value at various locations across the field, the predictive map generator 212 generates a predictive map 264 that shows the Predicts value of crop moisture at various positions across the field. The historical crop moisture value at these locations obtained from the preliminary information map 258 and the relationship between the historical crop moisture value and the crop moisture obtained from the predictive model are used to generate the predictive map 264 . Or, for example, if the predictive model generator 210 has generated a predictive model indicative of a relationship between a topographical feature value and crop moisture, then given the topographical feature value at various locations across the field, the predictive map generator 212 generates a predictive map 264 indicative of the value of crop moisture at various locations across the field. The topographic feature value obtained from the pre-information map 258 at these locations and the relationship between the topographic feature value and crop moisture obtained from the predictive model are used to generate the predictive map 264 . Or, for example, if the predictive model generator 210 has generated a predictive model indicating a relationship between a soil property value and crop moisture, then given the soil property value at various locations across the field, the predictive map generator 212 generates a predictive map 264 indicative of the crop moisture value predicted at various positions across the field. The soil property value obtained from the preliminary information map 258 at these positions and the relationship between the soil property value and the crop moisture obtained from the predictive model are used to generate the predictive map 264 .

Some variations in the types of data depicted in the preliminary information map 258, the types of data collected by in situ sensors 208, and the types of data predicted on the predictive map 264 will now be described.

In some examples, the type of data in advance information map 258 differs from the type of data captured by in situ sensors 208, yet the type of data in predictive map 264 is the same as the type of data captured by in situ sensors 208 will. For example, the preliminary information map 258 may be a vegetative index map and the variable sensed by the in situ sensors 208 may be crop moisture. The predictive map 264 may then be a predictive crop moisture map that maps predicted crop moisture values to various geographic locations in the field. In another example, the preliminary information map 258 may be a vegetative index map and the variable sensed by the in situ sensors 208 may be crop height. The predictive map 264 may then be a predictive crop height map that maps predicted crop height values to various geographic locations in the field.

Also, in some examples, the type of data in the advance information map 258 differs from the type of data sensed by in situ sensors 208, and the type of data in the predictive map 264 differs from both the type of data in the advance information map 258 and the type of data , which is detected by the in situ sensors 208 . For example, the preliminary information map 258 may be a vegetative index map and the variable sensed by the in situ sensors 208 may be crop moisture. The predictive map 264 can then be a predictive biomass map that maps predicted biomass values to various geographic locations in the field. In another example, the preliminary information map 258 may be a vegetative index map and the variable sensed by the in situ sensors 208 may be a yield. The predictive map 264 may then be a predictive speed map that maps predicted harvester speed values to various geographic locations in the field.

In some examples, the preliminary information map 258 is from a previous pass through the field during a preliminary operation and the type of data is different than the type of data captured by in situ sensors 208, however the type of data in the predictive map 264 is the same as that Type of data collected by the in situ sensors 208. For example, the preliminary information map 258 may be a topographical map generated during planting and the variable sensed by the in situ sensors 208 may be crop moisture. The predictive map 264 may then be a predictive crop moisture map that maps predicted crop moisture values to various geographic locations in the field in the predictive map 264 .

In some examples, the advance information map 258 is from a previous pass through the field during an advance operation and the type of data is the same as the type of data captured by in situ sensors 208 and the type of data in the predictive map 264 is also the same such as the type of data captured by the in situ sensors 208 . For example, the preliminary information map 258 may be a crop moisture map generated during a previous year and the variable sensed by the in situ sensors 208 may be crop moisture. The predictive map 264 may then be a predictive crop moisture map that maps predicted crop moisture values to various geographic locations in the field. In such an example, the relative crop moisture differences in the georeferenced advance information map 258 from the previous year may be used by the predictive model generator 210 to generate a predictive model that models a relationship between the relative crop moisture differences on the advance information map 258 and the crop moisture values determined by In -situ sensors 208 are detected during the current harvesting process. The predictive model is then used by the predictive map generator 212 to create a predictive yield map.

In some examples, a predictive map 264 may be provided to the control zone generator 213 . The control zone generator 213 groups adjacent sections of an area into one or more control zones based on predictive map 264 data values associated with those adjacent sections. A control zone may include two or more contiguous sections of an area, such as a field, for which a control parameter corresponding to the control zone for controlling a controllable subsystem is constant. For example, a response time for changing a setting of controllable subsystems 216 may be insufficient to respond satisfactorily to changes in values contained in a map, such as such as the predictive map 264. In this case, the control zone generator 213 analyzes the map and identifies control zones that are of a defined size to account for the response time of the controllable subsystems 216. In another example, the control zones may be sized to reduce wear from excessive actuator movement resulting from continuous adjustment. In some examples, there may be a different set of control zones for each controllable subsystem 216 or for groups of controllable subsystems 216. The control zones can be added to the predictive map 264 to obtain a predictive control zone map 265 . Thus, the predictive control zone map 265 may be similar to the predictive map 264, except that the predictive control zone map 265 includes control zone information defining the control zones. Thus, a functional predictive map 263 as described herein may include control zones. Both predictive map 264 and predictive control zone map 265 are functional predictive maps 263. In one example, functional predictive map 263 does not include control zones, such as predictive map 264. In another example, functional predictive map 263 includes control zones, such as the predictive control zone map 265. In some examples, multiple crops may be present in a field at the same time when a mixed crop production system is implemented. In this case, the predictive map generator 212 and the control zone generator 213 are able to identify the position and characteristic of the two or more crops and then generate the predictive map 264 and the predictive control zone map 265 with control zones accordingly.

It should also be noted that the control zone generator 213 can group values to generate control zones and the control zones can be added to a predictive tax zone map 265 or a separate map showing only the generated control zones. In some examples, the control zones may be used to control or calibrate agricultural harvester 100, or both. In other examples, the control zones may be displayed to operator 260 and used to control or calibrate agricultural harvester 100, and in other examples, the control zones may be displayed to operator 260 or another user, or saved for later use.

The predictive map 264 or the predictive control zone map 265 or both are provided to the control system 214, which generates control signals based on the predictive map 264 or the predictive control zone map 265 or both. In some examples, the communication system controller 229 controls the communication system 206 to communicate the predictive map 264 or the predictive control zone map 265 or control signals based on the predictive map 264 or the predictive control zone map 265 to other agricultural harvesting machines harvesting in the same field. In some examples, communication system controller 229 controls communication system 206 to send predictive map 264, predictive control zone map 265, or both to other remote systems.

Operator interface controller 231 is operable to generate control signals to control operator interface mechanisms 218 . Operator interface control 231 is also operable to present operator 260 with predictive map 264 or predictive control zone map 265 or other information derived from or based on predictive map 264, predictive control zone map 265, or both. The operator 260 can be a local operator or a remote operator. As an example, controller 231 generates control signals to control a display mechanism to display one or both of predictive map 264 and predictive control zone map 265 to operator 260 . Controller 231 may create operator actuable mechanisms that are displayed and operable by the operator to interact with the displayed map. The operator may edit the map, for example by correcting a crop moisture value displayed on the map based on the operator's observation. The adjustment controller 232 may generate control signals to control various adjustments on the agricultural harvesting machine 100 based on the predictive map 264, the predictive control zone map 265, or both. For example, the adjustment controller 232 may generate control signals to control the machine and header actuators 248 . In response to the generated control signals, the machine and header actuators 248 operate to adjust, for example, one or more of the sieve and chopper settings, concave clearance, rotor settings, cleaning fan speed settings, header height, header functionality, reel speed, reel position, to control the draper functionality (where the agricultural harvester 100 is coupled to a draperband header), the corn header functionality, the internal distribution control, and other actuators 248 that affect the other functions of the agricultural harvester 100. Path planning controller 234 illustratively generates control signals to direct routing subsystem 252 control to steer the agricultural harvesting machine 100 according to a desired path. Path planning controller 234 may control a path planning system to generate a route for agricultural harvester 100 and may control propulsion subsystem 250 and steering subsystem 252 to steer agricultural harvester 100 along that route. Feedrate controller 236 may control various subsystems, such as propulsion subsystem 250 and machine actuators 248, to control feedrate based on predictive map 264 or predictive control zone map 265, or both. For example, as agricultural harvester 100 approaches an area having crop moisture above a selected threshold, feed rate controller 236 may reduce the speed of agricultural harvester 100 to maintain a constant feed rate of grain or biomass through the machine. The header and reel controller 238 may generate control signals to control a header or reel or other header functionality. The draper band controller 240 may generate control signals to control a draper band or other draper functionality based on the predictive map 264, the predictive control zone map 265, or both. Deck position controller 242 may generate control signals to control a position of a deck included in a header based on predictive map 264 or predictive control zone map 265, or both, and tailing system controller 244 may generate control signals to position a tailing subsystem 138 based on a predictive map 264 or a predictive control zone map 265 or both. The machine cleaning controller 245 may generate control signals to control the machine cleaning subsystem 254 . For example, based on the different types of seeds or weeds being passed through the agricultural harvesting machine 100, a particular type of machine cleaning operation or a frequency at which a cleaning operation is performed may be controlled. Other controls included in agricultural harvesting machine 100 may also control other subsystems based on predictive map 264 or predictive control zone map 265 or both.

the 3A and 3B (herein together as 3 ) show a flow chart illustrating an example of the operation of the agricultural harvesting machine 100 in generating a predictive map 264 and a predictive control zone map 265 based on the preliminary information map 258 .

At 280, agricultural harvester 100 receives the preliminary information card 258. Examples of the preliminary information card 258 or receiving the preliminary information card 258 are discussed with respect to blocks 282, 284, and 286. FIG. As discussed above, the preliminary information map 258 maps values of a variable corresponding to a first feature to various positions in the field, as indicated at block 282 . For example, a preliminary information map may be a map generated during a preliminary operation or based on data from a preliminary operation on the field, such as a preliminary spray operation performed by a sprayer. The data for the previous information card 258 can also be collected in other ways. For example, the data may be collected based on aerial photographs or measurements taken during a previous year or earlier in the current growing season or at other times. The information may also be based on data detected or collected in other ways (other than aerial photography). For example, the data for the advance information card 258 may be transmitted to the agricultural harvester 100 via the communication system 206 and stored in the data store 202 . The data for the preliminary information card 258 can also be provided in other ways using the communication system 206 of the agricultural harvesting machine 100, as indicated by block 286 in the flow chart of FIG 3 is shown. In some examples, advance information card 258 may be received by communication system 206 .

At block 287 , the prior information card selector 209 may select one or more cards from the plurality of candidate prior information cards received in block 280 . For example, historical crop moisture maps of several years may be received as candidate advance information maps. Each of these maps may contain contextual information, such as weather patterns over a period of time, such as a year, pest infestations over a period of time, such as a year, soil properties, topographical features, etc. Contextual information may be used to select which historical crop moisture map to select. For example, the weather conditions over a period of time, such as a current year, or the soil properties for the current field can be compared to the weather conditions and soil properties in the context information for each candidate advance information map. The results of such Comparisons can be used to select which historical crop moisture map to select. For example, years with similar weather conditions may generally result in similar crop moisture or crop moisture trends in a field. In some cases, years with opposite weather patterns can also be useful to predict crop moisture based on historical crop moisture. For example, an area with low crop moisture in a dry year may have high crop moisture in a wet year because the area can store more moisture. The process of selecting one or more advance notice cards by the advance notice card selector 209 may be manual, semi-automated, or automated. In some examples, the advance information card selector 209 may continuously or intermittently determine during a harvesting operation whether another advance information card has a better relationship to the in situ sensor value. If another advance information map correlates more closely with the in situ data, the advance information map selector 209 may replace the currently selected advance information map with the more correlative advance information map.

At the beginning of a harvesting operation, the in situ sensors 208 generate sensor signals indicative of one or more in situ data values indicative of a plant trait, such as crop moisture, as indicated by block 288 . Examples of in situ sensors 288 are discussed with respect to blocks 222, 290 and 226. As discussed above, in situ sensors 208 include onboard sensors 222; remote in situ sensors 224, such as UAV-based sensors flown at a time to collect in situ data, as shown in block 290; or other types of in situ sensors, denoted by in situ sensors 226 . In some examples, data from onboard sensors is georeferenced using position heading or speed data from geographic position sensor 204 .

The predictive model generator 210 controls the prior information variable-to-in situ variable model generator 228 to generate a model representing a relationship between the mapped values contained in the prior information map 258 and the in situ values that by the in situ sensors 208 are modeled as indicated by block 292 . The features or data types represented by the mapped values in preliminary information map 258 and the in situ values sensed by in situ sensors 208 may be the same features or data types or different features or data types.

The relationship or model generated by the predictive model generator 210 is provided to the predictive map generator 212 . The predictive map generator 212 generates a predictive map 264 that represents a value of the feature sensed by the in situ sensors 208 at various geographic locations in a field to be harvested or another feature that is consistent with the sensed by the in situ sensors 208 feature is predicted using the predictive model and the advance information card 258 as indicated by block 294 .

Note that in some examples, the preliminary information card 258 may include two or more different cards or two or more different card layers of a single card. Each map layer may represent a different type of data than the data type of another map layer, or the map layers may have the same type of data obtained at different points in time. Each map in the two or more different maps, or each layer in the two or more different map layers of a map, maps a different type of variable to the geographic locations in the field. In such an example, the predictive model generator 210 generates a predictive model that models the relationship between the in situ data and each of the various variables depicted by the two or more different maps or the two or more different map layers. Likewise, the in situ sensors 208 may include two or more sensors, each sensing a different type of variable. Thus, the predictive model generator 210 creates a predictive model that models the relationships between each variable type represented by the prior information map 258 and each variable type sensed by the in situ sensors 208 . The predictive map generator 212 may generate a functional predictive map 263 that includes a value for each sensed feature sensed by the in situ sensors 208 (or a feature related to the sensed feature) at different locations in the Field that will be harvested using the predictive model and each of the maps or map layers in the advance information map 258 predicts.

The predictive map generator 212 configures the predictive map 264 such that the predictive map 264 is actionable (or consumable) by the control system 214 . Predictive map generator 212 may provide predictive map 264 to control system 214 or control zone generator 213, or both. Some examples of different ways in which predictive map 264 may be configured or issued are described with respect to blocks 296, 295, 299, and 297. For example, the predik tive map generator 212 includes the predictive map 264 such that the predictive map 264 includes values that can be read by the control system 214 and used as a basis for generating control signals for one or more of the various controllable subsystems of the agricultural harvesting machine 100, as by block 296 displayed.

The control zone generator 213 may divide the predictive map 264 into control zones based on the values on the predictive map 264 . Continuously geolocated values that are within a threshold of each other can be grouped into a tax zone. The threshold may be a default threshold, or the threshold may be set based on operator input, based on input from an automated system, or based on other criteria. A size of the zones may be based on a responsiveness of the control system 214, the controllable subsystems 216 based on wear considerations, or other criteria, as indicated by block 295. The predictive map generator 212 configures the predictive map 264 for presentation to an operator or other user. The control zone generator 213 can configure a predictive control zone map 265 for presentation to an operator or other user. Block 299 indicates this. When presented to an operator or other user, the presentation of predictive map 264 or predictive control zone map 265, or both, may include one or more of the predictive values on predictive map 264 that are correlated to the geographic location, the control zones on the predictive Control zone map 265 correlated to geographic location and containing adjustment values or control parameters used based on the predicted values on predictive map 264 or the zones on predictive control zone map 265. In another example, the presentation may include more abstracted information or more detailed information. The representation may also include a confidence level that indicates an accuracy with which the predicted values on the predictive map 264 or the zones on the predictive control zone map 265 match measured values that can be measured by sensors on the agricultural harvesting machine 100 when moving the agricultural harvesting machine 100 through the field. Furthermore, an authentication/authorization system can be provided that implements authentication and authorization processes when information is presented at multiple locations. For example, there may be a hierarchy of people who are authorized to view and change maps and other presented information. For example, an onboard display device can display the maps locally on the machine in near real time, or the maps can also be generated at one or more remote locations. In some examples, each physical display device at each location may be associated with a person or user permission level. The user permission level can be used to determine what indicator marks are visible on the physical display device and what values that person can change. For example, a local operator of agricultural harvesting machine 100 may not be able to view the information corresponding to predictive map 264 or make changes to machine operation. However, a supervisor at a remote location can see the predictive map 264 on the display but not make any changes to it. A manager, who may be at a separate remote location, may be able to see all of the items on the predictive map 264 and also be able to change the predictive map 264 used in machine control . This is an example of an authorization hierarchy that can be implemented. The predictive map 264 or the predictive control zone map 265 or both can also be configured in other ways, as indicated by block 297 .

At block 298, inputs from the geographic position sensor 204 and other in situ sensors 208 are received by the control system. Block 300 represents the control system 214 receiving an input from the geographic position sensor 204 identifying a geographic position of the agricultural harvesting machine 100 . Block 302 represents the receipt by the control system 214 of sensor inputs indicative of the trajectory or heading of the agricultural harvester 100, and Block 304 represents the receipt by the control system 214 of a speed of the agricultural harvester 100. Block 306 represents the receipt of other information from various in situ sensors 208 by the control system 214.

At block 308 the control system 214 generates control signals to control the controllable subsystems 216 based on the predictive map 264 or the predictive control zone map 265 or both and the input from the geographic position sensor 204 and any other in situ sensors 208 . At block 310, the control system 214 applies the control signals to the controllable subsystems. It will be appreciated that the particular control signals that are generated and the particular controllable subsystems 216 that are controlled based on one or more different things can vary. For example, the control signals generated and the controllable subsystems 216 that are controlled may be based on the type of predictive map 264 or predictive control zone map 265 or both used. Likewise, the control signals generated and the controllable subsystems 216 that are controlled and the timing of the control signals may be based on various latencies of crop flow through the agricultural harvesting machine 100 and the responsiveness of the controllable subsystems 216.

For example, a generated predictive map 264 in the form of a predictive crop moisture map may be used to control one or more controllable subsystems 216 . For example, the functional predictive crop moisture map may include predictive values of crop moisture georeferenced to locations within the field to be harvested. The functional predictive crop moisture map can be extracted and used to control the steering and propulsion subsystems 252 and 250, respectively. By controlling the steering and propulsion subsystems 252 and 250, a rate of travel of material or grain moving through the agricultural harvesting machine 100 can be controlled. Similarly, the height of the header can be controlled to accommodate more or less material, and thus the height of the header can also be controlled to control the rate of advance of material through the agricultural harvester 100 . In other examples, when the predictive map 264 maps a predictive value of crop moisture in front of the machine that is higher on one portion of the header than another portion of the header, resulting in a different biomass in one side of the header than the other Side enters, the control of the header can be implemented. For example, a draper speed on one side of the header may be increased or decreased relative to the draper speed on the other side of the header to account for the additional biomass. Thus, the header and reel controller 238 may be controlled using georeferenced predictive values present in the predictive crop moisture map to control the draper speeds of the draper bands on the header. The preceding example of haul speed and header control using a functional predictive crop moisture map is provided by way of example only. Consequently, a variety of other control signals may be generated using predictive values obtained from a predictive crop moisture map or other type of functional predictive map 263 to control one or more of the controllable subsystems 216 .

At block 312, it is determined whether the harvesting operation has been completed. If harvesting is not complete, processing continues at block 314 where in situ sensor data from geographic location sensor 204 and in situ sensors 208 (and possibly other sensors) continue to be read.

In some examples, at block 316, agricultural harvester 100 may also recognize learning trigger criteria to perform machine learning on one or more of predictive map 264, predictive control zone map 265, the model generated by predictive model generator 210, the zones generated by generated by control zone generator 213, one or more control algorithms implemented by controllers in control system 214, and other triggered learning.

The learning trigger criteria can contain a large number of different criteria. Some examples of detecting trigger criteria are discussed with respect to blocks 318, 320, 321, 322, and 324. For example, in some examples, the triggered learning may include restoring a relationship used to generate a predictive model when a threshold amount of in situ sensor data is obtained from the in situ sensors 208 . In such examples, receipt of an amount of in situ sensor data from in situ sensors 208 that exceeds a threshold triggers or causes predictive model generator 210 to generate a new predictive model used by predictive map generator 212 will. Thus, as agricultural harvester 100 continues a harvesting operation, receipt of the threshold amount of in situ sensor data from in situ sensors 208 triggers the generation of a new relationship represented by a predictive model generated by predictive model generator 210 is produced. Furthermore, the new predictive map 264, the predictive control zone map 265, or both can be regenerated using the new predictive model. Block 318 represents detecting a threshold set of in situ sensor data used to trigger creation of a new predictive model.

In other examples, the learning trigger criteria may be based on how much the in situ sensor data from the in situ sensors 208 is changing, such as over time or compared to previous values. For example, if discrepancies within the in situ sensor data (or the rel (The correlation between the in situ sensor data and the information in the preliminary information map 258) is within a selected range, or is less than a defined amount, or is below a threshold, then no new predictive model is generated by the predictive model generator 210. As a result, the predictive map generator 212 does not generate a new predictive map 264, predictive control zone map 265, or both. However, if deviations within the in situ sensor data are outside the selected range, are greater than the defined amount, or are above the threshold, for example, the predictive model generator 210 generates a new predictive model using all or part of the newly received in situ Sensor data that the predictive map generator 212 uses to generate a new predictive map 264 . At block 320, variations in the in situ sensor data, such as a magnitude of an amount by which the data exceeds the selected range or a magnitude of variation in the relationship between the in situ sensor data and the information in the preliminary information map 258, used as a trigger to cause the generation of a predictive model and map. Keeping the examples described above, the threshold, range and defined amount can be set to default values; set by an operator or user interaction via a user interface; set by an automated system; or adjusted in any other way.

Other learning trigger criteria can also be used. For example, if the predictive model generator 210 switches to a different advance information map (different from the originally selected advance information map 258), switching to the different advance information map may require re-learning by the predictive model generator 210, the predictive map generator 212, the control zone generator 213, the Trigger control system 214 or other elements. In a further example, the transition of the agricultural harvesting machine 100 to a different topography or to a different control zone can also be used as learning trigger criteria.

In some cases, operator 260 may also edit predictive map 264 or predictive control zone map 265, or both. The edits may change a value on predictive map 264, a size, shape, location, or presence of a control zone on predictive control zone map 265, or both. Block 321 shows that processed information can be used as learning trigger criteria.

Also, in some cases, the operator 260 may observe that automated control of a controllable subsystem is not what the operator desires. In such cases, the operator 260 may provide the controllable sub-system with a manual adjustment that reflects the operator 260 wanting the controllable sub-system to operate differently than is commanded by the control system 214 . Thus, a manual change of a setting by the operator 260 may cause one or more of the predictive model generator 210 to relearn a model, the predictive map generator 212 to regenerate the map 264, the control zone generator 213 to create one or more control zones on the regenerate predictive control zone map 265, and control system 214 to relearn a control algorithm or perform machine learning on one or more of control components 232-246 in control system 214 based on adjustment by operator 260, as shown in block 322. Block 324 represents the use of other triggered learning criteria.

In other examples, relearning may be performed periodically or intermittently, for example based on a selected time interval, such as a discrete time interval or a variable time interval, as indicated by block 326 .

When relearning is triggered, whether based on learning trigger criteria or based on the passage of a time interval, as indicated by block 326, one or more of the predictive model generator 210, the predictive map generator 212, the control zone generator 213 and the control system 214 performs machine learning to generate a new predictive model, map, control zone, or control algorithm based on the learning trigger criteria. The new predictive model, map and control algorithm are generated using additional data collected since the last learning. Performing the relearn is indicated by block 328 .

When the harvesting process has been completed, the process proceeds from block 312 to block 330 where one or more of the predictive map 264, the predictive control zone map 265, and the predictive model generated by the predictive model generator 210 are stored. The predictive map 264, the predictive control zone map 265 and the predictive model can be stored locally on the data storage 202 or using the communication system 206 for later be sent to a remote system for use.

It should be noted that while some examples herein describe predictive model generator 210 and predictive map generator 212 using a prior information map when generating a predictive model and receiving a functional predictive map, respectively, predictive model generator 210 and predictive map generator 212 in In other examples, when generating a predictive model and a functional predictive map, each may receive other types of maps including predictive maps, such as a functional predictive map generated during the harvesting process.

4 is a block diagram of a portion of the in 1 shown agricultural harvesting machine 100. In particular 4 among other things, examples of the predictive model generator 210 and the predictive map generator 212 in more detail. 4 also illustrates the flow of information between the various components shown therein. As shown, the predictive model generator 210 receives one or more of a vegetative index map 332, a historical crop moisture map 333, a topographical map 341, or a soil property map 343, or a preliminary operation map 400 as a preliminary information map. Historical crop moisture map 333 includes historical crop moisture values 335 indicative of crop moisture values in the field during a previous harvest. The historical crop moisture map 333 also includes contextual data 337 indicative of the context or conditions that may have affected the crop moisture value for the previous year(s). For example, contextual data 337 may include soil properties such as soil type, soil moisture, soil cover or soil structure, topographical features such as elevation or slope, planting date, harvest date, fertilizer application, seed type (hybrid, etc.), a measure of weed presence, a measure of pest presence, weather conditions, e.g. e.g. rain, snow cover, hail, wind, temperature etc. Historical crop moisture map 333 may also include other elements as indicated by block 339 . As shown in the illustrated example, the vegetative index map 332, the topographic map 341, and the soil feature map 343 do not contain any additional information. However, in other examples, the vegetative index map 332, the topographical map 341, the soil feature map 343, and the preliminary operation map 400 may also include other elements. For example, weed growth affects a vegetative index value. Thus, the herbicide application in temporal relation to the vegetative index measurement used to generate the vegetative index map 332 may be context information included in the vegetative index map 332 to provide context for the vegetative index values. Various other types of information may also be included in the vegetative index map 332, the topographic map 341, or the soil feature map 343.

In addition to receiving one or more of a vegetative index map 332, a historical crop moisture map 333, a topographical map 341, or a soil feature map 343, or a pre-operational map 400 as a pre-information map, the predictive model generator 210 also receives a geographic position 334, or an indication of a geographic position from the geographic position sensor 204. The in situ sensors 208 include, by way of example, a crop moisture sensor 336 and a processing system 338. The processing system 338 processes sensor data generated by the crop moisture sensors 336. In some examples, crop moisture sensor 336 may be onboard agricultural harvester 100 .

In some examples, crop moisture sensor 336 may include a capacitive moisture sensor. In one example, the capacitive moisture sensor may include a moisture measurement cell for receiving the crop sample and a capacitor for determining the dielectric properties of the sample. In other examples, the crop moisture sensor may be a microwave sensor or a conductivity sensor. In other examples, the crop moisture sensor may use wavelengths of electromagnetic radiation to sense the moisture content of the crop. The crop moisture sensor may be located within the feeder housing 106 (or otherwise have sensing access to crop material within the feeder housing 106 ) and configured to sense the moisture of harvested crop material passing through the feeder housing 106 . In other examples, the crop moisture sensor may be located at other locations within agricultural harvesting machine 100, such as in the clean grain elevator, in a clean grain auger, or in a grain tank. Note that these are merely examples of crop moisture sensors and that various other crop moisture sensors are contemplated. The processing system 338 processes one or more sensor signals generated by the crop moisture sensor 336 to generate processed sensor data representing one or more crop moisture values identify. The processing system 338 can also geolocate the values received from the in situ sensor 208 . For example, the position of the agricultural harvester at the time a signal is received from the in situ sensor 208 may not be the exact position of the crop moisture. This is because there is a time lag between the time the agricultural harvester makes initial contact with the crop plant and the time the crop plant material is sensed by the crop moisture sensor 336 or other in situ sensor 208 . Thus, when georeferencing the collected data, a transition time between the initial encounter of a plant and the collection of the plant material within the agricultural harvester is taken into account. This allows the moisture level of the crop to be georeferenced to its exact position in the field. Due to the travel of separated crop along a header in a direction that is transverse to a direction of travel of the agricultural harvester, crop moisture values typically geolocate to a V-shaped area behind the agricultural harvester as the agricultural harvester moves in a forward direction.

The processing system 338 ranks an aggregate crop moisture detected by a crop moisture sensor during each time point or measurement interval based on the travel times of the crop from different sections of the agricultural harvesting machine, such as different lateral positions along a width of a header of the agricultural harvesting machine and the ground speed of the Harvester, previous georeferenced regions too. For example, the processing system 338 maps a measured aggregate crop moisture back from a measurement interval or time to georeferenced areas traversed by a header of the agricultural harvesting machine during different measurement intervals or times. The processing system 338 distributes or maps the aggregated crop moisture from a particular measurement interval or time previously traversed through georeferenced regions that are part of the V-shaped region.

In some examples, the crop moisture sensor 336 may rely on various types of radiation and the manner in which radiation is reflected, absorbed, attenuated, or transmitted through the crop material. Crop moisture sensor 336 may sense other electromagnetic properties of crop material, such as electrical permittivity, as the material passes between two capacitive plates. Other material properties and sensors can also be used. In some examples, raw or processed data from crop moisture sensor 336 may be presented to operator 260 via operator interface mechanism 218 . The operator 260 may be onboard the agricultural harvester 100 or at a remote location.

The present discussion continues with respect to an example where crop moisture sensor 336 senses a value indicative of crop moisture. It should be understood that this is just an example and the above sensors are contemplated as other examples of the crop moisture sensor 336 herein. As in 4 shown, the predictive model generator 210 includes a vegetative index-to-crop moisture model generator 342, a historical crop moisture-to-crop moisture model generator 344, a soil properties-to-crop moisture model generator 345, a topographical feature-to-crop moisture model generator 346, and a pre-operation-to-crop moisture model generator 348. In other examples, the predictive model generator 210 may include additional, fewer, or different components than those in the example of FIG 4 shown. Accordingly, in some examples, the predictive model generator 210 may also include other elements 349, which may include other types of predictive model generators to generate other types of crop moisture models. For example, other model generators 349 may include specific traits, for example specific vegetative index traits, such as crop growth or crop health; specific soil property characteristics, such as soil type, soil moisture, soil cover or soil structure; or specific topographical features, such as slope or elevation.

The vegetative index-to-crop moisture model generator 342 identifies a relationship between in situ crop moisture data 340 at a geographic location corresponding to where the in situ crop moisture data 340 was geolocated and vegetative index values from the vegetative index map 332, corresponding to the same position in the field where the in situ crop moisture data 340 was geolocated. Based on this relationship established by the vegetative index-to-crop moisture model generator 342, the vegetative index-to-crop moisture model generator 342 generates a predictive crop moisture model. The predictive crop moisture model is driven by the pre dictive map generator 212 is used to predict crop moisture at various locations based on the georeferenced vegetative index value contained in vegetative index map 332 at the same locations in the field.

The historical crop moisture-to-crop moisture model generator 344 identifies a relationship between crop moisture represented in the in situ crop moisture data 340 at a geographic location corresponding to the location at which the in situ crop moisture data 340 was geolocated, and the historical crop moisture at the same location (or a location in a historical crop moisture map 333 with similar contextual data 337 as the current area or year). The historical crop moisture value 335 is the georeferenced and contextual value contained in the historical crop moisture map 333 . The historical crop moisture-to-crop moisture model generator 344 then creates a predictive crop moisture model that is used by the map generator 212 to predict the crop moisture at a location based on the historical crop moisture value 335 .

Soil property-to-crop moisture model generator 345 identifies a relationship between in situ crop moisture data 340 at a geographic location corresponding to where in situ crop moisture data 340 was geolocated and soil property values from soil property map 343 corresponding to the same location correspond to the field where in situ crop moisture data 340 was geolocated. Based on this relationship established by the soil properties-to-crop moisture model generator 345, the soil properties-to-crop moisture model generator 345 generates a predictive crop moisture model. The predictive crop moisture model is used by the predictive map generator 212 to predict crop moisture at various locations based on the georeferenced soil property value contained in the soil property map 343 at the same locations in the field.

Topographical feature-to-crop moisture model generator 346 identifies a relationship between in situ crop moisture data 340 at a geographic location corresponding to the location at which in situ crop moisture data 340 was geolocated and topographical feature values from topographical map 341, corresponding to the same position in the field where in situ crop moisture data 340 was geolocated. Based on this relationship established by topographical feature-to-crop moisture model generator 346, topographical feature-to-crop moisture model generator 346 generates a predictive crop moisture model. The predictive crop moisture model is used by the predictive map generator 212 to predict crop moisture at various locations based on the georeferenced topographic feature value contained in the topographic map 341 at the same locations in the field.

The pre-operation-to-crop moisture model generator 348 identifies a relationship between in-situ crop moisture data 340 at a geographic location corresponding to the location where in-situ crop moisture data 340 was geolocated and pre-operation feature values from pre-operation map 400 that correspond to the same position in the field where in situ crop moisture 340 was geolocated. Based on this relationship established by the pre-operation to crop moisture model generator 348, the pre-operation to crop moisture model generator 348 generates a predictive crop moisture model. The predictive crop moisture model is used by the predictive map generator 212 to predict crop moisture at various locations based on the georeferenced pre-operation feature value contained in the pre-operation map 400 at the same location in the field.

In view of the foregoing, predictive model generator 210 is operable to generate a variety of predictive crop moisture models, such as one or more of the predictive crop moisture models generated by model generators 342, 344, 345, 346, 348, and 349. In another example, two or more of the predictive crop moisture models described above can be combined into a single predictive crop moisture model that calculates crop moisture based on the vegetative index value, the historical crop moisture value, the soil property value, the topographical feature value, or the pre-operational feature value at different locations in the field or both predicted. Each of these crop moisture models, or combinations thereof, are collectively represented by crop moisture model 350 in 4 represented.

The predictive crop moisture model 350 is provided to the predictive map generator 212 . In the example of 4 includes the predictive map generator 212 includes a crop moisture map generator 352. In other examples, predictive map generator 212 may include additional, fewer, or different map generators. The crop moisture map generator 352 receives the predictive crop moisture model 350 that predicts crop moisture based on in situ data 340 along with one or more of the vegetative index map 332, the historical crop moisture map 333, the topographical map 341, or the soil property map 343.

The crop moisture map generator 352 may generate a functional predictive crop condition map 360 that shows crop condition at various locations in the field based on one or more of the vegetative index value, the historical crop condition value, the topographical feature value, or the soil property value at those locations in the field and the predictive crop moisture model 350 predicted. The generated functional predictive crop moisture map 360 (with or without control zones) may be provided to control zone generator 213, control system 214, or both. The control zone generator 213 generates control zones and integrates these control zones into the functional predictive map, i. H. the predictive map 360 to generate the predictive control zone map 265. Either or both of the functional predictive maps 264 or the predictive control zone map 265 may be presented to the operator 260 or other user or provided to the control system 214, which generates control signals to control one or more of the controllable subsystems 216 based on the predictive map 264, the predictive control zone card 265 or both.

5 is a flow chart of an example of the operation of the predictive model generator 210 and the predictive map generator 212 in generating the predictive crop moisture model 350 and the functional predictive crop moisture map 360. At block 362, the predictive model generator 210 and the predictive map generator 212 receive one or more previous vegetative index maps 332 , one or more historical crop moisture maps 333, one or more previous topographical maps 341, one or more soil feature maps 434, or one or more preliminary operation maps 400, or a combination thereof. At block 362, an in situ sensor signal is received from an in situ sensor, such as a crop moisture sensor signal from a crop moisture sensor 336.

At block 363 , advance information card selector 209 selects one or more specific advance information card(s) 250 for use by predictive model generator 210 . In one example, the preliminary information card selector 209 selects a card from a plurality of candidate cards based on a comparison of the context information in the candidate cards with the current context information. For example, a candidate historical crop moisture map may be selected from a previous year in which weather conditions over the growing season were similar to the current year's weather conditions. Or, for example, a candidate historical crop moisture map from a previous year with a below-average rainfall level may be selected while the current year has an average or above-average rainfall level, since the historical crop moisture map associated with a previous year with below-average rainfall is still a useful one crop moisture-to-crop moisture relationship as discussed above. In some examples, the advance information map selector 209 may change which advance information map is used if it is determined that one of the other candidate advance information maps correlates more closely with the in situ sensed crop moisture.

At block 372, the processing system 338 processes the one or more received sensor signals received from the in situ sensors 208, such as the one or more received sensor signals from crop moisture sensors 336, to generate a crop moisture value indicative of a moisture content of the crop indicates.

At block 382, the predictive model generator 210 also obtains the geographic position corresponding to the sensor signal. For example, the predictive model generator 210 may obtain the geographic location from the geographic location sensor 204 and determine an accurate geographic location to associate the in situ sensed crop moisture with based on machine delays (e.g., machine processing speed) and machine speed. For example, the exact time that a crop moisture sensor signal is detected may not correspond to a time that the crop was separated from the ground. Thus, a position of the agricultural harvesting machine 100 when the crop moisture sensor signal is received may not correspond to the position where the crop was planted. Instead, the current in situ crop moisture sensor signal corresponds to a position in the field behind the agricultural harvesting machine 100, since a period of time between when the initial contact between the crop and the agricultural harvester occurs and the time the crop reaches the crop moisture sensor 336 elapses.

At block 384, the predictive model generator 210 generates one or more predictive crop moisture models, such as crop moisture model 350, that establish a relationship between at least one of a vegetative index value, a historical crop moisture value, a topographical feature value, or a soil property value derived from a preliminary information map, such as preliminary information map 258 , obtained, and a crop moisture detected by the in situ sensor 208 . For example, the predictive model generator 210 may generate a predictive crop moisture model based on a vegetative index value, a historical crop moisture value, a topographical feature value, or a soil property value and a sensed crop moisture indicated by the sensor signal obtained from the in situ sensor 208.

At block 386, the predictive crop moisture model, such as the predictive crop moisture model 350, is provided to the predictive map generator 212, which generates a functional predictive crop moisture map that includes a predictive crop moisture based on the vegetative index map, the historical crop moisture map 333, the topographic map 341, or the soil property map 343 and the predictive crop moisture model 350 to various geographic locations in the field. For example, in some examples, the functional predictive crop moisture map 360 predicts crop moisture. In other examples, the functional predictive crop moisture map 360 predicts other items. Further, the functional predictive crop moisture map 360 may be generated during the course of an agricultural harvesting operation. Thus, as an agricultural harvester moves through a field in which an agricultural harvesting operation is being performed, the functional predictive crop moisture map 360 is generated.

At block 394 , the predictive map generator 212 outputs the functional predictive crop moisture map 360 . At block 393, the predictive map generator 212 configures the functional predictive crop moisture map 360 for consumption by the control system 214. At block 395, the predictive map generator 212 may also provide the control zone generator 213 with the map 360 for generation and integration of control zones. At block 397, predictive map generator 212 configures map 360 in other ways as well. The functional predictive crop moisture map 360 (with or without the control zones) is provided to the control system 214 . At block 396, the control system 214 generates control signals to control the controllable subsystems 216 based on the functional predictive crop moisture map 360 (with or without control zones).

The control system 214 may generate control signals to control the header or other machine actuators 248, such as to control a position or spacing of the deck plates. Control system 214 may generate control signals to control powertrain subsystem 250 . The control system 214 can generate control signals to control the steering subsystem 252 . Control system 214 may generate control signals to control residue subsystem 138 . The control system 214 can generate control signals to control the machine cleaning subsystem 254 . The control system 214 can generate control signals to control the harvester 110 . Control system 214 may generate control signals to control material handling subsystem 125 . The control system 214 may generate control signals to control the crop cleaning subsystem 118 . The control system 214 can generate control signals to control the communication system 206 . Control system 214 may generate control signals to control operator interface mechanisms 218 . The control system 214 can generate control signals to control various other controllable subsystems 256 .

In an example where the control system 214 receives a functional predictive map or a functional predictive map with added control zones, the header/reel controller 238 controls the header or other machine actuators 248 to control a height, pitch or roll of the header 102. In an example where the control system 214 receives a functional predictive map or a functional predictive map with control zones added, the line speed controller 236 controls the propulsion subsystem 250 to control a ground speed of the agricultural harvesting machine 100 . In an example where control system 214 receives a functional predictive map or a functional predictive map with added control zones, path planning controller 234 controls steering subsystem 252 to control agricultural harvester 100 . In another example where the control system 214 receives a functional predictive map or a functional predictive map with control zones added, the backlog system controller 244 controls the backlog portion system 138. In another example where control system 214 receives a functional predictive map or a functional predictive map with added control zones, adjustment controller 232 controls combiner settings of combiner 110. In another example where control system 214 receives a functional predictive map or receives a functional predictive map with added control zones, the adjustment controller 232 or another controller 246 controls the material handling subsystem 125. In another example, where the control system 214 receives a functional predictive map or a functional predictive map with added control zones, controls adjustment controller 232 controls crop cleaning subsystem 118. In another example, where control system 214 receives a functional predictive map or a functional predictive map with added control zones, machine cleaning controller 24 controls 5 the machine cleaning subsystem 254 on the agricultural harvesting machine 100. In another example in which the control system 214 receives a functional predictive map or a functional predictive map with added control zones, the communication system controller 229 controls the communication system 206. In another example, in where the control system 214 receives a functional predictive map or a functional predictive map with added control zones, the operator interface controller 231 controls operator interface mechanisms 218 on the agricultural harvesting machine 100. In another example where the control system 214 uses the functional predictive map or the functional predictive map receives added control zones, deck position controller 242 controls machine/header actuators 248 to control a deck on agricultural harvesting machine 100 . In another example where the control system 214 receives the functional predictive map or the functional predictive map with control zones added, the draper belt controller 240 controls machine/header actuators 248 to control a draper belt on the agricultural harvesting machine 100 . In another example where the control system 214 receives the functional predictive map or the functional predictive map with control zones added, the other controllers 246 control other controllable subsystems 256 on the agricultural harvesting machine 100.

Thus, it can be seen that the present system creates a preliminary information map that maps a trait, such as a vegetative index value, a historical crop moisture value, a topographical trait value, or a soil property value, to various locations in a field. The present system also uses one or more in situ sensors that acquire in situ sensor data indicative of a characteristic, such as crop moisture, and creates a model that represents a relationship between the in situ sensed crop moisture using the in -situ sensor and the feature depicted in the preliminary information map. Thus, the present system generates a functional predictive map using a model and a prior information map, and can configure the generated functional predictive map for consumption by a control system or for presentation to a local or remote operator or other user. For example, the control system can use the map to control one or more systems of a combine.

6A 1 is a block diagram of an exemplary portion of FIG 1 shown agricultural harvesting machine 100. In particular 6A examples of predictive model generator 210 and predictive map generator 212, among others. In the illustrated example, preliminary information map 258 is a preliminary operation map 400. The preliminary operation map 400 may include crop moisture values at various locations in the field from a preliminary operation in the field. 6A Also shows that the predictive model generator 210 and the predictive map generator may receive a predictive crop moisture map, such as the functional predictive crop moisture map 360, alternatively or in addition to the advance information map 258 . The functional predictive crop moisture map 360 can be used similarly to the advance information map 258 in this model generator 210 that models a relationship between information provided by the functional predictive crop moisture map 360 and features sensed by in situ sensors 208. and the map generator 212 can thus use the model to generate a functional predictive map showing the features detected by the in situ sensors 208, or a feature indicative of the detected feature, at various locations in the field based on one or more of the values in the functional predictive crop moisture map 360 at those locations in the field and based on the predictive model. As in 6A shown, the predictive model generator 210 and the predictive map generator 212 can also receive other maps 401, for example other advance information maps or other predictive maps, such as other predictive crop moisture maps that are generated in a different way than the functional predictive crop moisture map 360. In another example, other maps 401 may include a historical crop moisture map, such as historical crop moisture map 333 or other historical crop moisture map generated during a harvest operation in the previous season and including historical values of crop moisture corresponding to various locations in the field.

Also in the in 6A For example, as shown, in situ sensor 208 may include one or more of an agricultural feature sensor 402 , an operator input sensor 404 , and a processing system 406 . The in situ sensors 208 may include other sensors 408 as well.

Farming trait sensor 402 captures values indicative of farming traits. Operator input sensor 404 senses various operator inputs. The inputs may be adjustment inputs for controlling adjustments on the agricultural harvesting machine 100 or other control inputs such as steering inputs and other inputs. Thus, when operator 260 changes a setting or provides a commanded input via operator interface mechanism 218, such input is detected by operator input sensor 404, which provides a sensor signal indicative of that sensed operator input.

The processing system 406 may receive the sensor signals from one or more of the agricultural feature sensor 402 and the operator input sensor 404 and generate an output indicative of the sensed feature. For example, processing system 406 may receive sensor input from farming feature sensor 402 and generate an output indicative of a farming feature. Processing system 406 may also receive input from operator input sensor 404 and generate an output indicative of the sensed operator input.

The predictive model generator 210 may include a crop moisture-to-farm trait model generator 410 and a crop moisture-to-command model generator 414 . In other examples, the predictive model generator 210 may include additional, fewer, or different model generators 415 . For example, the predictive model generator 210 may include specific agricultural traits or specific operator command model generators, such as model generators using specific agricultural traits, for example biomass or biomass traits, yield or yield traits, as well as various other agricultural traits, or model generators using specific operator commands, for Example header height settings, speed settings, threshing rotor settings as well as various other machine settings. The predictive model generator 210 may receive a geographic location 334 or an indication of a geographic location from the geographic location sensor 204 and generate a predictive model 426 that represents a relationship between the information in one or more of the preliminary information maps 258 or the information in the functional predictive crop moisture map 360 and models one or more of: the agricultural trait sensed by agricultural trait sensor 402; and operator input commands sensed by operator input sensor 404 .

The crop moisture-to-agricultural trait model generator 410 creates a model that models a relationship between crop moisture values (which may be on the predictive crop moisture map 360, the pre-operational map 400, or another map 401) and the agricultural trait sensed by the agricultural trait sensor 402 is detected. The crop moisture-to-farm trait model generator 410 generates a predictive model 426 that conforms to this relationship.

The crop moisture-to-operator command model generator 414 creates a model that models a relationship between crop moisture values as reflected on the predictive crop moisture map 360, the preliminary operation map 400 or another map 401, and operator input commands that are detected by the operator input sensor 404. The crop moisture-to-operator command model generator 414 creates a predictive model 426 that conforms to this relationship.

Other model generators 415 may include, for example, specific agricultural traits or specific operator command model generators, such as model generators using specific agricultural traits, for example biomass or biomass characteristics, yield or yield characteristics, as well as various other agricultural characteristics, or model generators using specific operator commands, for example header height settings , speed settings, threshing rotor settings as well as various other machine settings.

The predictive model 426 generated by the predictive model generator 210 may include one or more of the predictive models generated by the crop moisture-to-farm trait model generator 410 and the crop moisture capability-to-operator-command model generator 414 and other model generators that may be included as part of other elements 415.

In the example of 6A For example, predictive map generator 212 includes agricultural feature predictive map generator 416 and operator command predictive map generator 422. In other examples, predictive map generator 212 may include additional, fewer, or different map generators 424.

The predictive agricultural trait map generator 416 receives a predictive model 426 representing the relationship between crop moisture and an agricultural trait sensed by the agricultural trait 402 (such as a predictive model generated by the crop moisture-to-agricultural trait model generator 410 ), and one or more of the advance information maps 258 that models the functional predictive crop moisture map 360 or other maps 401. The predictive agricultural trait map generator 416 generates a functional predictive agricultural trait map 427 that includes agricultural trait values (or the agricultural traits to which the values are indicative) at various locations in the field based on one or more of the crop moisture values in one or more of the preliminary information maps 258, the functional predictive crop moisture map 360 or other map 401 at those positions in the field and based on the predictive model 426.

The predictive operator command map generator 422 receives a predictive model 426 representing the relationship between crop moisture and operator command inputs sensed by the operator input sensor 404 (such as a predictive model generated by the crop moisture-to-command model generator 414) and one or more of the previous information map 258, the functional predictive crop moisture map 360, or the other map 401 is modeled. The predictive operator command map generator 422 generates a functional predictive operator command map 440 that predicts operator command inputs at various locations based on the crop moisture values from the advance information map 258, the functional predictive crop moisture map 360 or another map at those locations in the field and based on the predictive model 426.

The predictive map generator 212 outputs one or more of the functional predictive maps 427 and 440 . Each of functional predictive maps 427 and 440 may be provided to control zone generator 213, control system 214, or both. The control zone generator 213 generates and integrates control zones to provide a functional predictive map 427 with control zones, or a functional predictive map 440 with control zones, or both. Any or all of functional predictive maps 427 and 440 (with or without control zones) may be provided to control system 214, which generates control signals to control one or more of controllable subsystems 216 based on any or all of functional predictive maps 427 and 440 (with or without control zones). Any or all of maps 427 and 440 (with or without control zones) may be presented to operator 260 or another user.

6B FIG. 12 is a block diagram showing some examples of real-time (in situ) sensors 208. FIG. Some of the in 6B The sensors shown, or various combinations thereof, may include both a sensor 402 and a processing system 406, while others may function as the sensor 402 described with respect to FIG 6A and 7 described with the processing system 406 being separate. Some of the possible in situ sensors 208 used in 6B are shown and described above with respect to the previous figures and are similarly numbered. 6B FIG. 1 shows that the in situ sensors 208 may include operator input sensors 480, machine sensors 482, crop property sensors 484, field and soil property sensors 485, environmental feature sensors 487, and they may include a variety of other sensors 226. Operator input sensors 480 may be sensors that detect operator inputs via operator interface mechanisms 218 . Therefore, operator input sensors 480 can detect user movement of linkages, joysticks, a steering wheel, buttons, knobs, or pedals. Operator input sensors 480 may also detect user interactions with other operator input mechanisms, such as a touch-sensitive screen, a microphone that uses speech recognition, or a variety of other operator input mechanisms.

The machine sensors 482 can sense various characteristics of the agricultural harvesting machine 100 . As discussed above, the machine sensors 482 may include, for example, machine speed sensors 146, a separator loss sensor 148, a clean grain camera 150, a forward-looking imaging mechanism 151, loss sensors 152, or a geographic position sensor 204, examples of which are described above are. Machine sensors 482 may also include machine setting sensors 491 that sense machine settings. Some examples of machine settings have been given above in relation to 1 described. Header (e.g., header) position sensor 493 may sense the position of the header 102 , reel 164 , cutter 104 , or other header relative to the agricultural harvester 100 frame. For example, the sensors 493 can detect the height of the header 102 above the ground. Machine sensors 482 may also include header (e.g., header) orientation sensors 495 . The sensors 495 can detect the orientation of the header 102 relative to the agricultural harvester 100 or relative to the ground. Machine sensors 482 may include stability sensors 497 . The stability sensors 497 detect an oscillation or bouncing movement (and amplitude) of the agricultural harvesting machine 100. The machine sensors 482 can also include residue adjustment sensors 499 that are configured to detect whether the agricultural harvesting machine 100 is configured to shred the residue to form a windrow generate or otherwise treat the residue. The machine sensors 482 may include a shoe fan speed sensor 551 that senses the speed of the cleaning fan 120 . The machine sensors 482 may include concave distance sensors 553 that sense the distance between the rotor 112 and the concaves 114 on the agricultural harvesting machine 100 . Machine sensors 482 may include chopper gap sensors 555 that sense the size of the openings in chopper 122 . The machine sensors 482 may include a threshing rotor speed sensor 557 that senses a rotor speed of the rotor 112 . The engine sensors 482 may include a rotor pressure sensor 559 that senses the pressure used to drive the rotor 112 . The machine sensors 482 may include a wire gap sensor 561 that senses the size of the openings in the wire 124 . Machine sensors 482 may include a MOG moisture sensor 563 that senses a moisture content of MOG that passes agricultural harvester 100 . The machine sensors 482 may include a machine orientation sensor 565 that senses the orientation of the agricultural harvesting machine 100 . Machine sensors 482 may include material feed rate sensors 567 that sense material feed rate as material moves through feeder housing 106, clean grain elevator 130, or elsewhere in agricultural harvesting machine 100. Machine sensors 482 may include biomass sensors 569 that sense biomass moving through feeder housing 106 , through separator 116 , or elsewhere in agricultural harvesting machine 100 . The machine sensors 482 may include a fuel consumption sensor 571 that senses fuel consumption rate of the agricultural harvesting machine 100 over time. Machine sensors 482 may include a power usage sensor 573 that senses power usage in agricultural harvesting machine 100, such as which subsystems are using power, or the rate at which subsystems are using power, or the distribution of power among the subsystems in agricultural harvesting machine 100. Machine sensors 482 may include tire pressure sensors 577 that sense air pressure in tires 144 of agricultural harvesting machine 100 . Machine sensor 482 may include a variety of other machine performance sensors or machine attribute sensors, which are indicated by block 575 . The machine performance sensors and the machine feature sensors 575 may sense machine performance or features of the agricultural harvesting machine 100 .

The crop characteristic sensors 484 may detect characteristics of the separated crop during processing of the crop by the agricultural harvesting machine 100 . Crop properties can include things such as crop type, crop moisture, grain quality (such as broken grain), MOG values, grain components such as starches and protein, MOG moisture, and other properties of the crop. Other sensors could detect straw "toughness", corn adhesion to heads, and other characteristics that could be advantageously used to control processing for better grain detection, reduced grain damage, reduced power consumption, reduced grain loss, etc.

Field and soil feature sensors 485 can detect the characteristics of the field and soil. The field and soil properties can include soil moisture, soil compactness, the presence and location of standing water, soil type, and other soil and field characteristics.

Environmental feature sensors 487 may sense one or more environmental features. The environmental features can include things like wind direction and speed, precipitation, fog, dust levels or other pollution, or other environmental features.

7 12 is a flow chart illustrating an example of the operation of a predictive model generator 210 and a predictive map generator 212 in generating one or more predictive models 426 and one or more functional predictive maps 427 and 440. At block 442, the predictive model generator 210 and the predictive map generator 212 receive a map. The map received from a predictive model generator 210 or a predictive map generator in the generation of one or more predictive models 426 and one or more functional predictive maps 427 and 440 may be a pre-information map 258, such as a pre-operation map 400, generated using data obtained during a preliminary operation in a field. The map received from a predictive model generator 210 or a predictive map generator in generating one or more predictive models 426 and one or more functional predictive maps 427 and 440 may be a functional predictive crop moisture map 360 . Other maps may also be received, as indicated by block 401, such as other advance information maps or other predictive maps, for example other predictive crop moisture maps.

At block 444 , the predictive model generator 210 receives a sensor signal that includes sensor data from an in situ sensor 208 . The in situ sensor may be one or more of an agricultural feature sensor 402 and an operator input sensor 404 . Farming trait sensor 402 senses a farming trait. The operator input sensor 404 detects an operator input command. The predictive model generator 210 may also receive other in situ sensor inputs, as indicated by block 408 .

At block 454, the processing system 406 processes the data contained in the sensor signal or signals received from the in situ sensor or sensors 208 to obtain processed data 409, shown in FIG 6A input signal shown. The data contained in the sensor signal or signals may be in a raw format that is processed to receive processed data 409 . For example, a temperature sensor signal contains electrical resistance data that can be processed into temperature data. In other examples, processing may include digitizing, encoding, formatting, scaling, filtering, or classifying data. Processed data 409 may indicate one or more agricultural characteristics or operator input commands. The processed data 409 is provided to the predictive model generator 210 .

Coming back to 7 at block 456, the predictive model generator 210 also receives a geographic position 334 from the geographic position sensor 204, as in FIG 6A shown. The geographic location 334 may correlate to the geographic location from which the sensed variable(s) sensed by in situ sensors 208 were taken.

For example, the predictive model generator 210 may receive the geographic position 334 from the geographic position sensor 204 and determine an accurate geographic position based on machine delays, machine speed, etc. from which the processed data 409 was derived.

At block 458, the predictive model generator 210 generates one or more predictive models 426 that model a relationship between a mapped value in a received map and a feature represented in the processed data 409. For example, in some cases, the mapped value in a received map may be a crop moisture value, and the predictive model generator 210 generates a predictive model using the mapped value of a received map and a feature sensed by in situ sensors 208, as in the processed data 409 1 , or a related feature, such as a feature that correlates to the feature sensed by in situ sensors 208 .

The one or more predictive models 426 are provided to the predictive map generator 212 . At block 466, the predictive map generator 212 generates one or more functional predictive maps. The functional predictive maps may be a functional predictive agricultural feature map 427 and a functional predictive operator command map 440, or any combination of these maps. The functional predictive agricultural trait map 427 predicts agricultural trait values (or agricultural traits indicated by the values) at various locations in the field. The functional predictive operator command map 440 predicts desired or likely operator command inputs at various locations in the field. Furthermore, one or more of functional predictive maps 427 and 440 may be generated during a farming operation. Thus, when the agricultural harvesting machine 100 moves through a field in performing an agricultural operation, the one or more predictions are made Active maps 427 and 440 are generated as the farming operation is performed.

At block 468 the predictive map generator 212 outputs the one or more functional predictive maps 427 and 440 . At block 470, the predictive map generator 212 may configure the map for presentation to and possible interaction by an operator 260 or other user. At block 472, the predictive map generator 212 may configure the map for the control system 214 to consume. At block 474, the predictive map generator 212 may provide the one or more predictive maps 427 and 440 to the control zone generator 213 for generation and integration of control zones. At block 476, predictive map generator 212 configures one or more predictive maps 427 and 440 in a different manner. The one or more functional predictive maps 427 (with or without control zones) and 440 (with or without control zones) may be presented to operator 260 or another user, or provided to control system 214 as well.

At block 478, the control system 214 then generates control signals to control the controllable subsystems based on the one or more functional predictive maps 427 and 440 (or the functional predictive maps 427 and 440 having control zones) and an input from the geographic position sensor 204 .

The control system 214 may generate control signals to control the header or other machine actuators 248, such as to control a position or spacing of the deck plates. Control system 214 may generate control signals to control powertrain subsystem 250 . The control system 214 can generate control signals to control the steering subsystem 252 . Control system 214 may generate control signals to control residue subsystem 138 . The control system 214 can generate control signals to control the machine cleaning subsystem 254 . The control system 214 can generate control signals to control the harvester 110 . Control system 214 may generate control signals to control material handling subsystem 125 . The control system 214 may generate control signals to control the crop cleaning subsystem 118 . The control system 214 can generate control signals to control the communication system 206 . Control system 214 may generate control signals to control operator interface mechanisms 218 . The control system 214 can generate control signals to control various other controllable subsystems 256 .

In an example where the control system 214 receives a functional predictive map or a functional predictive map with added control zones, the header/reel controller 238 controls the header or other machine actuators 248 to control a height, pitch or roll of the header 102. In an example where the control system 214 receives a functional predictive map or a functional predictive map with control zones added, the line speed controller 236 controls the propulsion subsystem 250 to control a ground speed of the agricultural harvesting machine 100 . In an example where control system 214 receives a functional predictive map or a functional predictive map with added control zones, path planning controller 234 controls steering subsystem 252 to control agricultural harvester 100 . In another example where the control system 214 receives a functional predictive map or a functional predictive map with added control zones, the backlog system controller 244 controls the backlog subsystem 138. In another example where the control system 214 receives a functional predictive map or a receives a functional predictive map with added control zones, the adjustment controller 232 controls harvester settings of the harvester 110. In another example, in which the control system 214 receives a functional predictive map or a functional predictive map with added control zones, the adjustment controller 232 or another controller controls 246 the material handling subsystem 125. In another example where the control system 214 receives a functional predictive map or a functional predictive map with added control zones, the setup controller 232 controls crop cleaning s subsystem 118. In another example in which the control system 214 receives a functional predictive map or a functional predictive map with added control zones, the machine cleaning controller 245 controls the machine cleaning subsystem 254 on the agricultural harvesting machine 100. In another example, in where the control system 214 receives a functional predictive map or a functional predictive map with added control zones, the communication system controller 229 controls the communication system 206. In another example where the control system 214 receives a functional predictive map or a functional predictive map with added control zones, For example, operator interface controller 231 controls operator interface mechanisms 218 on agricultural harvesting machine 100. In another example, where control system 214 includes functional predictive map or functional predictive map added control zones receives, deck position controller 242 controls machine/header actuators 248 to control a deck on agricultural harvesting machine 100 . In another example where the control system 214 receives the functional predictive map or the functional predictive map with control zones added, the draper belt controller 240 controls machine/header actuators 248 to control a draper belt on the agricultural harvesting machine 100 . In another example where the control system 214 receives the functional predictive map or the functional predictive map with control zones added, the other controllers 246 control other controllable subsystems 256 on the agricultural harvesting machine 100.

8th FIG. 12 is a block diagram illustrating an example control zone generator 213. FIG. The control zone generator 213 includes a work machine actuator (WMA) selector 486, a control zone generation system 488, and a regime zone generation system 490. The control zone generator 213 may include other elements 492 as well. The control zone generation system 488 includes the control zone criteria identification component 494, the control zone boundary definition component 496, the target setting identification component 498 and other elements 520. The regime zone generation system 490 includes the regime zone criteria identification component 522, the regime zone boundary definition component 524, the setting resolver identification component 526 and other elements 528. Before describing the overall operation of the control zone generator 213 in more detail , a brief description of some of the elements of the control zone generator 213 and their respective operation is first provided.

The agricultural harvesting machine 100 or other work machine may include a variety of different types of controllable actuators that perform different functions. The controllable actuators on agricultural harvesting machine 100 or other work machines are collectively referred to as work machine actuators (WMAs). Each WMA can be independently controllable based on values on a functional predictive map, or the WMAs can be controlled as sets based on one or more values on a functional predictive map. Therefore, the control zone generator 213 can generate control zones corresponding to each individually controllable WMA or to the sets of WMAs that are controlled in concert with one another.

The WMA selector 486 selects a WMA or set of WMAs for which to create corresponding control zones. The control zone creation system 488 then creates the control zones for the selected WMA or set of WMAs. Different criteria may be used in identifying control zones for each WMA or group of WMAs. For example, for a WMA, the WMA response time can be used as a criterion for defining the boundaries of the control zones. In another example, wear characteristics (e.g., how badly a particular actuator or mechanism wears as a result of its movement) may be used as a criterion for identifying control zone boundaries. The control zone criteria identification component 494 identifies certain criteria to be used in defining control zones for the selected WMA or set of WMAs. The control zone boundary definition component 496 processes the values on a functional predictive map being analyzed to define the boundaries of the control zones on that functional predictive map based on the values in the functional predictive map being analyzed and based on the control zone criteria for the selected WMA or define a set of WMAs.

The target setting identification component 498 sets a value of the target setting used to control the WMA or set of WMAs in different control zones. For example, if the selected WMA is the header or other machine actuators 248 and the analyzed functional predictive map is a functional predictive crop moisture map 360, the target setting in each control zone may be a target speed setting based on crop moisture values contained in the functional predictive crop moisture map 360 .

In some examples where the agricultural harvester 100 is to be controlled based on a current or future position of the agricultural harvester 100, multiple target settings for a WMA at a particular position may be possible. In this case, the target settings can have different values and compete. Therefore, the target settings must be resolved so that only a single target setting can be used to control the WMA. For example, if the WMA is an actuator in drive system 250 that is being controlled to control the speed of agricultural harvesting machine 100, there may be several different competing sets of criteria used by control zone generation system 488 in identifying the control zones and target settings for the selected one WMA are taken into account in the control zones. at For example, different target settings for controlling the speed of the agricultural harvester can be based on, for example, a recognized or predicted crop moisture value, a historical crop moisture value, a recognized or predicted agricultural feature value, a recognized or predicted vegetative index value, a recognized or predicted soil feature value, a recognized or predicted topographical feature value , a detected or predicted feedrate value, a detected or predicted fuel efficiency value, a detected or predicted grain loss value, or a combination thereof. It should be understood that these are just an example and the target settings for different WMAs can be based on various other values or combinations of values. However, the agricultural harvester 100 cannot travel across the ground at multiple speeds simultaneously at any given time. Rather, the agricultural harvesting machine 100 travels at a single speed at all times. Thus, one of the competing target settings is selected to control the speed of the agricultural harvesting machine 100 .

In some examples, regime zone generation system 490 generates regime zones to resolve multiple different competing target settings. The regimen zone criteria identification component 522 identifies the criteria used to identify regimen zones for the selected WMA or set of WMAs on the functional predictive map to be analyzed. Some criteria that can be used to identify or define regime zones include, for example, crop moisture, agricultural traits, topographical traits, vegetative index traits, soil properties, operator command inputs, crop type or crop variety (e.g., based on a stand map or other source of crop type or crop locations), weed type, weed intensity, or crop condition (such as whether the crop is tipped, partially tipped, or standing). These are just a few examples of the criteria that can be used to identify or define regime zones. Just as each WMA or group of WMAs may have a corresponding control zone, different WMAs or groups of WMAs may have a corresponding regime zone. The regime zone boundary definition component 524 identifies the boundaries of regime zones on the functional predictive map being analyzed based on the regime zone criteria identified by the regime zone criteria identification component 522 .

In some examples, regime zones may overlap. For example, a crop moisture regime zone may overlap with some or all of a crop moisture regime zone. In such an example, the various regime zones may be assigned a precedent hierarchy such that when two or more regime zones overlap, the regime zone assigned a greater hierarchical position or importance in the precedent hierarchy takes precedence over the regime zones assigned a lower hierarchical one position or importance in the precedent hierarchy. The regime zone priority hierarchy may be set manually or automatically using a rule-based system, a model-based system, or some other system. As an example, if a kinked crop regime zone overlaps a crop moisture regime zone, the kinked crop regime zone may be assigned greater importance in the hierarchy of precedence than the crop moisture regime zone, such that the kinked crop regime zone takes precedence.

In addition, each regime zone may have a unique setting resolver for a specific WMA or set of WMAs. The adjustment resolver identification component 526 identifies a particular adjustment resolver for each regimen zone identified on the functional predictive map to be analyzed and a particular adjustment resolver for the selected WMA or set of WMAs.

• Wenn vorhergesagte Erntegutfeuchtigkeitswerte innerhalb von 6 Metern (20 Fuß) vom Erntevorsatz der landwirtschaftlichen Erntemaschine 100 größer als x sind (wobei x ein ausgewählter oder vorbestimmter Wert ist), dann ist der Zieleinstellwert zu verwenden, der auf Grundlage der Vorschubgeschwindigkeit gegenüber anderen konkurrierenden Zieleinstellungen ausgewählt wird, andernfalls ist der Zieleinstellwert auf Grundlage des Kornverlusts gegenüber anderen konkurrierenden Zieleinstellwerten zu verwenden.

Once the attitude resolver is identified for a particular regime zone, that attitude resolver can be used to resolve competing target attitudes, identifying more than one target attitude based on the control zones. The different types of setting resolvers can have different shapes. For example, the setting resolvers identified for each regime zone may include a human choice resolver in which the competing target settings are presented to an operator or other user for resolution. In another example, the attitude resolver may include a neural network or other artificial intelligence or machine learning system. In such cases, the setting resolvers may resolve the competing target settings based on a predicted or historical quality metric corresponding to each of the different target settings. For example, an increased vehicle speed setting can reduce the time to harvest a field and corresponding reduce time-based labor costs and equipment costs, but may increase grain losses. A reduced vehicle speed setting can increase the time to harvest a field and increase the corresponding time-based labor and equipment costs, but can reduce grain losses. If grain loss or harvest time is selected as the quality metric, given the two competing vehicle speed setting values, the predicted or historical value for the selected quality metric can be used to resolve the speed setting. In some cases, the adjustment resolvers can be a set of threshold rules that can be used instead of or in addition to the regime zones. An example of a threshold rule can be expressed as follows:

• If predicted crop moisture values within 6 meters (20 feet) of the header of agricultural harvesting machine 100 are greater than x (where x is a selected or predetermined value), then use the target setting selected based on feed rate versus other competing target settings , otherwise use target setting based on grain loss versus other competing target setting.

The attitude resolvers can be logical components that execute logical rules in identifying a target attitude. For example, the attitude resolver may resolve target settings while trying to minimize harvest time, or minimize total harvest cost, or maximize grain harvested, or based on other variables calculated depending on the various target settings in question. Harvest time can be minimized when an amount to complete a harvest is reduced to a selected threshold or below. Total harvesting costs can be minimized when the total harvesting costs are reduced to or below a selected threshold. Crop can be maximized when the amount of crop is increased to or above a selected threshold.

9 12 is a flowchart illustrating an example of the operation of control zone generator 213 in generating control zones and regime zones for a map that control zone generator 213 receives for zone processing (e.g., for a map that is being analyzed).

At block 530, the control zone generator 213 receives a map that is analyzed for processing. In one example, as shown at block 532, the map analyzed is a functional predictive map. For example, the map analyzed may be one of functional predictive maps 360, 427, or 440. Block 534 indicates that the card to be analyzed can be other cards.

At block 536, the WMA selector 486 selects a WMA or set of WMAs for which to create control zones on the analyzed map. At block 538, the control zone criteria identification component 494 obtains control zone definition criteria for the selected WMAs or set of WMAs. Block 540 provides an example where the control zone criteria is or includes wear characteristics of the selected WMA or set of WMAs. Block 542 provides an example where the control zone definition criteria is or includes magnitude and variation of input source data, such as magnitude and variation of values on the analyzed map or magnitude and variation of inputs from various in situ sensors 208. Block 544 provides an example where the control zone definition criteria are or include physical machine characteristics, such as the physical dimensions of the machine, a speed at which various subsystems operate, or other physical machine characteristics. Block 546 provides an example where the control zone definition criteria is or includes a responsiveness of the selected WMA or set of WMAs in meeting newly commanded adjustment values. Block 548 provides an example where the control zone definition criteria are or include machine performance metrics. Block 550 provides an example where the control zone definition criteria is or includes operator preferences. Block 552 provides an example where the tax zone definition criteria is or includes other elements. Block 549 provides an example where the tax zone definition criteria are time-based, meaning that agricultural harvester 100 does not cross the boundary of a tax zone until a selected period of time has elapsed since agricultural harvester 100 entered a particular tax zone. In some cases, the selected length of time may be a minimum length of time. Thus, in some cases, the control zone definition criteria may prevent agricultural harvester 100 from crossing a boundary of a control zone until at least the selected period of time has elapsed. Block 551 provides an example where the tax zone definition criteria is based on a selected size value. For example, tax zone definition criteria based on a selected size value may preclude definition of a tax zone that is smaller than the selected size. In some cases, the size selected may be a minimum size.

At block 554, regimen zone criteria identification component 522 obtains regimen zone definition criteria for the selected WMA or set of WMAs. Block 556 provides an example where the regime zone definition criteria are based on manual input from operator 260 or another user. Block 558 illustrates an example where the regime zone definition criteria are based on crop moisture, including recognized, predicted, or historical crop moisture. Block 559 illustrates an example where the regimen zone definition criteria are based on vegetative index traits. Block 560 illustrates an example where the regimen zone definition criteria are based on topographical features. Block 561 illustrates an example where the regime zone definition criteria are based on soil properties. Block 564 provides an example where the regime zone definition criteria is or includes other criteria.

At block 566, the tax zone boundary definition component 496 creates the boundaries of tax zones on the map to be analyzed based on the tax zone criteria. The regime zone boundary definition component 524 creates the boundaries of regime zones on the map to be analyzed based on the regime zone criteria. Block 568 provides an example in which the zone boundaries for the control zones and regime zones are identified. Block 570 shows that the target setting identification component 498 identifies the target settings for each of the zones of control. The control zones and regime zones may be created in other ways, as indicated by block 572.

At block 574 , the adjustment resolver identification component 526 identifies the adjustment resolver for the selected WMAs in each regime zone defined by the regime zone boundary definition component 524 . As discussed above, the regimen zone resolver may be a human resolver 576, an artificial intelligence or machine learning system resolver 578, a predicted or historical quality-based resolver 580 for each competing target setting, a rule-based resolver 582, a performance criteria-based resolver 584, or other resolver 586 be.

At block 588, the WMA selector 486 determines whether there are more WMAs or sets of WMAs to process. If additional WMAs or sets of WMAs remain to be processed, processing returns to block 436 where the next WMA or set of WMAs for which control zones and regime zones are to be defined is selected. If no additional WMAs or sets of WMAs remain for which to generate control zones or regime zones, processing moves to block 590 where the control zone generator 213 outputs a map of control zones, target settings, regime zones, and setting resolvers for each of the WMAs or sets of WMAs . As discussed above, the issued card may be presented to operator 260 or another user; the card issued may be provided to the control system 214; or the issued card may be otherwise issued.

10 12 illustrates an example of the operation of the control system 214 in controlling the agricultural harvesting machine 100 based on a map output from the control zone generator 213 . Thus, at block 592, the control system 214 receives a map of the work site. In some cases, as represented by block 594, the map may be a functional predictive map that may include control zones and regime zones. In some cases, the received map may be a functional predictive map excluding tax zones and regime zones. Block 596 provides an example where the received worksite map may be a pre-information map with tax zones and regime zones identified thereon. Block 598 provides an example where the received map may include multiple different maps or multiple different map layers. Block 610 provides an example where the received map may take other forms as well.

At block 612, the control system 214 receives a sensor signal from the geographic position sensor 204. The sensor signal from the geographic position sensor 204 may include data indicating the geographical position 614 of the agricultural harvester 100, the speed 616 of the agricultural harvester 100, the heading 618 of the agricultural harvester 100 or provide other information 620 . At block 622, the zone controller 247 selects a regime zone, and at block 624, the zone controller 247 selects a control zone on the map based on the geographic position sensor signal. At block 626, the zone controller 247 selects a WMA or set of WMAs to control. At block 628, the zone controller 247 obtains one or more target settings for the selected WMA or set of WMAs. The target settings obtained for the selected WMA or group of WMAs can come from different sources. For example, block 630 shows an example where one or more of the target settings for the selected WMA or set of WMAs are based on input from the control zones on the map job based. Block 632 depicts an example where one or more of the target settings are obtained from human input from the operator 260 or another user. Block 634 shows an example where the target settings are obtained from an in situ sensor 208 . Block 636 depicts an example where the one or more target settings are obtained from one or more sensors on other machines operating in the same field, either concurrently with agricultural harvesting machine 100 or from one or more sensors on machines operating in the past worked in the same field. Block 638 shows an example where the target settings are also obtained from other sources.

At block 640, the zone controller 247 accesses the adjustment resolver for the selected regimen zone and controls the adjustment resolver to resolve competing target adjustments into a resolved target adjustment. As discussed above, in some cases, the setting resolver may be a human resolver, with zone controller 247 controlling operator interface mechanisms 218 to present the competing target settings to operator 260 or another user for resolution. In some cases, the attitude resolver may be a neural network or other artificial intelligence or machine learning system, and the zone controller 247 communicates the competing target attitudes to the neural network, artificial intelligence or machine learning system for selection. In some cases, the attitude resolver may be based on a predicted or historical quality metric, threshold rules, or logical components. In one of these latter examples, the zone controller 247 executes the adjustment resolver to obtain a resolved target adjustment based on the predicted or historical quality metric, based on the threshold rules, or using the logical components.

At block 642, if the zone controller 247 has identified the resolved target setting, the zone controller 247 provides the resolved target setting to other controllers in the control system 214, which generate and apply control signals based on the resolved target setting to the selected WMA or set of WMAs. For example, if the selected WMA is a machine or header actuator 248, the zone controller 247 provides the resolved target setting to the adjustment controller 232 or the header/reel controller 238 or both to generate control signals based on the resolved target setting, and these generate control signals are applied to the machine or header actuators 248. If at block 644 additional WMAs or additional sets of WMAs are to be controlled at the current geographic position of agricultural harvester 100 (as detected at block 612), processing returns to block 626 where the next WMA or set of WMAs is selected. The processes represented by blocks 626 through 644 continue until all WMAs or sets of WMAs that are to be controlled at the current geographic position of agricultural harvesting machine 100 have been addressed. If there are no additional WMAs or sets of WMAs to control at the current geographic location of agricultural harvester 100, processing proceeds to block 646 where zone controller 247 determines whether there are additional control zones to be considered in the selected regime zone. If there are additional tax zones to consider, processing returns to block 624 where a next tax zone is selected. If there are no more additional control zones to consider, processing transfers to block 648 which determines whether there are any additional regime zones to consider. The zone controller 247 determines whether additional regimen zones are to be considered. If there are more regimen zones to consider, processing returns to block 622 where a next regimen zone is selected.

At block 650, zone controller 247 determines whether the operation that agricultural harvester 100 is performing is complete. If not, as indicated by block 652, the zone controller 247 determines whether a control zone criteria has been met to continue processing. For example, as noted above, the control zone definition criteria may include criteria that define when a control zone boundary may be crossed by agricultural harvesting machine 100 . For example, a selected period of time may define whether a control zone boundary may be crossed by agricultural harvester 100, meaning that agricultural harvester 100 is prevented from crossing a zone boundary until a selected period of time has elapsed. In this case, at block 652, the zone controller 247 determines whether the selected time period has expired. In addition, the zone controller 247 can perform the processing continuously. Thus, the zone controller 247 does not wait a certain period of time before proceeding to determine whether operation of the agricultural harvesting machine 100 is complete. At block 652, the zone controller 247 determines that it is time to continue processing, and then continues processing at block 612 where zone controller 247 again receives input from geographic position sensor 204 . It should also be noted that the zone controller 247 may control the WMAs and sets of WMAs simultaneously using a multiple-input, multiple-output controller, rather than sequentially controlling the WMAs and sets of WMAs.

11 FIG. 14 is a block diagram showing an example of an operator interface controller 231. FIG. In an illustrated example, the operator interface control 231 includes an operator input command processing system 654, another control interaction system 656, a voice processing system 658, and an action signal generator 660. The operator input command processing system 654 includes the voice processing system 662, the touch gesture manipulation system 664, and other elements 666. The other control interaction system 656 includes the control input processing system 668 and the control output generator 670. The speech processing system 658 includes the trigger detector 672, the recognition component 674, the synthesis component 676, the natural language understanding system 678, the dialogue management system 680 and other elements 682. The action signal generator 660 includes the visual control signal generator 684, the audio control signal generator 686, the haptic control signal generator 688 and other elements 690. Before operation de r in FIG. While the example operator interface control 231 shown in the illustration will describe how to handle various operator interface actions, a brief description of some of the elements of the operator interface control 231 and the operation associated therewith will first be provided.

Operator input command processing system 654 recognizes operator inputs at operator interface mechanisms 218 and processes those inputs for commands. Speech processing system 662 recognizes speech input and processes the interactions with speech processing system 658 to process the speech input for commands. Touch gesture handler system 664 recognizes touch gestures on touch-sensitive elements in operator interface mechanisms 218 and processes those inputs for commands.

The other controller interaction system 656 handles interactions with other controllers of the control system 214. The controller input processing system 668 recognizes and processes inputs from other controllers in the control system 214, and the controller output generator 670 generates outputs and provides those outputs to other controllers in the control system 214. Speech processing system 658 recognizes speech inputs, determines the meaning of those inputs, and provides an output that indicates the meaning of the spoken inputs. For example, voice processing system 658 may recognize voice input from operator 260 as a setting change command, in which operator 260 commands control system 214 to change a setting for controllable subsystem 216 . In such an example, voice processing system 658 recognizes the content of the spoken command, identifies the meaning of that command as a setting change command, and provides the meaning of that input back to voice processing system 662. Voice processing system 662, in turn, interacts with control output generator 670 to provide the commanded output to the provide appropriate control in control system 214 to comply with the spoken setting change command.

The language processing system 658 can be invoked in a number of ways. For example, voice processing system 662 continuously provides input from a microphone (which is one of operator interface mechanisms 218) to voice processing system 658 in one example. The microphone detects speech from the operator 260 and the speech processing system 662 provides the speech processing system 658 with the recognized speech. Trigger detector 672 detects a trigger indicating that language processing system 658 is invoked. In some cases, when speech processing system 658 receives continuous speech input from speech processing system 662, speech recognition component 674 performs continuous speech recognition on all speech spoken by operator 260.

In some cases, the language processing system 658 is configured to be invoked using a wake-up word. That is, in some cases, operation of the speech processing system 658 may be initiated based on recognition of a selected spoken word, referred to as a wake-up word. In such an example, where the detection component 674 detects the wakeup word, the detection component 674 provides an indication that the wakeup word was detected to trigger the detector 672 . The trigger detector 672 detects that the voice processing system 658 has been invoked or triggered by the wake-up word. In another example, the language processing system 658 may be invoked by an operator 260 manipulating an actuator on a user interface mechanism, such as by Touching an actuator on a touch-sensitive display screen, pressing a button, or providing another trigger input. In such an example, trigger detector 672 may detect that language processing system 658 has been invoked when a trigger input is detected via a user interface mechanism. Trigger detector 672 may also detect that speech processing system 658 has been invoked in other ways.

Once the speech processing system 658 is invoked, the speech input from the operator 260 is provided to the speech recognition component 674 . Speech recognition component 674 recognizes linguistic elements in speech input, such as words, phrases, or other linguistic units. The natural language understanding system 678 identifies a meaning of the recognized language. The meaning can be a natural language output, a command output identifying a command reflected in the recognized language, a value output identifying a value in the recognized language, or a variety of other outputs reflecting understanding of the recognized language. For example, the natural language understanding system 678 and the language processing system 568 can more generally understand the meaning of the recognized language in the context of the agricultural harvesting machine 100 .

In some examples, voice processing system 658 may also generate outputs that navigate operator 260 through a user experience based on voice input. For example, the dialog management system 680 can create and manage a dialog with the user to identify what the user wants to do. Dialogue can make a user's command unmistakable; identify one or more specific values required to execute the user's command; or receive other information from the user or provide other information to the user or both. Synthesis component 676 may generate speech synthesis that may be presented to the user through an audio operator interface mechanism, such as a speaker. Thus, the dialogue managed by dialogue management system 680 may be spoken dialogue only or a combination of both visual dialogue and spoken dialogue.

Action signal generator 660 generates action signals to control operator interface mechanisms 218 based on outputs from one or more of operator input command processing system 654, other control interaction system 656, and language processing system 658. Visual control signal generator 684 generates control signals to control visual elements in operator interface mechanisms 218 . The visual elements can be lights, a display screen, warning indicators, or other visual elements. Audio control signal generator 686 generates outputs that control audio elements of operator interface mechanisms 218 . The audio elements include a speaker, audible alarm mechanisms, horns, or other audible elements. The haptic control signal generator 688 generates control signals that are output to control haptic elements of the operator interface mechanisms 218 . The haptic elements include vibration elements that can be used to vibrate, for example, the operator's seat, steering wheel, pedals or joysticks used by the operator. The haptics may include tactile feedback or force feedback elements that provide a tactile feedback or force feedback signal to the operator via operator interface mechanisms. The haptics may also include a variety of other haptics.

12 12 is a flow chart illustrating an example of the operation of operator interface controller 231 in generating an operator interface display on operator interface mechanism 218, which may include a touch-sensitive display screen. 12 also illustrates an example of how operator interface controller 231 may recognize and process operator interactions with the touch-sensitive display screen.

At block 692, operator interface controller 231 receives a card. Block 694 gives an example where the map is a functional predictive map and block 696 gives an example where the map is another map type. At block 698 , operator interface controller 231 receives input from geographic position sensor 204 identifying the geographic position of agricultural harvester 100 . As indicated in block 700 , the input from geographic position sensor 204 may include heading along with the position of agricultural harvester 100 . Block 702 provides an example where the input from geographic location sensor 204 includes the speed of agricultural harvester 100, and block 704 provides an example where the input from geographic location sensor 204 includes other items.

At block 706, visual control signal generator 684 in operator interface controller 231 controls the touch-sensitive display screen in operator interface mechanisms 218 to generate a display showing all or part of a field represented by the received card. Block 708 indicates that the displayed field may include a current position marker showing a current position of agricultural harvesting machine 100 relative to the field. Block 710 provides an example where the displayed field includes a next unit of work marker that identifies a next unit of work (or area in the field) in which agricultural harvesting machine 100 is operating. Block 712 provides an example in which the displayed field includes an upcoming area display section indicating areas that are yet to be processed by the agricultural harvesting machine 100, and Block 714 provides an example in which the displayed field includes previously visited display sections. represent the areas of the field that the agricultural harvesting machine 100 has already worked. Block 716 provides an example in which the displayed panel displays various features of the georeferenced location panel on the map. For example, if the received map is a crop moisture map, such as predictive crop moisture map 360, the displayed field may indicate the various crop moisture values present in the field that are georeferenced within the displayed field. The mapped features may be shown in the previously visited areas (as shown in block 714), in the upcoming areas (as shown in block 712), and in the next work unit (as shown in block 710). Block 718 provides an example where the displayed field also includes other items.

13 FIG. 7 is a pictorial representation showing an example of a user interface display 720 that may be generated on a touch-sensitive display screen. In other implementations, user interface display 720 may be generated on other types of displays. The touch-sensitive screen can be mounted in the cab of the agricultural harvesting machine 100 or on the mobile device or elsewhere. The user interface display 720 will be described prior to the description of the in 12 shown flowchart is continued.

in the in 13 In the example shown, the user interface display 720 illustrates that the touch-sensitive display screen includes a display feature for operating a microphone 722 and a speaker 724 . Thus, the touch-sensitive display may be communicably coupled to microphone 722 and speaker 724 . Block 726 indicates that the touch-sensitive display screen may include a variety of operator interface control actuators, such as buttons, keyboards, soft keyboards, links, icons, switches, etc. The operator 260 may manipulate the operator interface control actuators to perform various functions.

in the in 14 In the example shown, user interface display 720 includes a field display portion 728 that displays at least a portion of the field in which agricultural harvesting machine 100 is operating. Field display portion 728 is shown with a current position marker 708 corresponding to a current position of agricultural harvesting machine 100 in the portion of the field shown in field display portion 728 . In one example, the operator can control the touch-sensitive display to zoom into portions of panel display portion 728 or to pan or scroll panel display portion 728 to view different portions of the panel. A next unit of work 730 is shown as the area of the field just ahead of the current position marker 708 of the agricultural harvester 100 . The current position marker 708 may also be configured to identify the direction of travel of the agricultural harvesting machine 100, a travel speed of the agricultural harvesting machine 100, or both. In 14 the shape of the current position marker 708 provides an indication of the orientation of the agricultural harvester 100 within the field, which can be used as an indication of a direction of travel of the agricultural harvester 100 .

The size of the next work unit 730 highlighted on the panel display portion 728 may vary based on a variety of different criteria. For example, the size of the next work unit 730 may vary depending on the ground speed of the agricultural harvester 100 . Thus, if the agricultural harvester 100 is moving faster, the range of the next working unit 730 may be larger than the range of the next working unit 730 if the agricultural harvester 100 is moving slower. In another example, the size of the next working unit 730 may vary depending on the dimensions of the agricultural harvester 100, including equipment on the agricultural harvester 100 (such as the header 102). For example, the width of the next working unit 730 may vary depending on a width of the header 102 . Of the Panel display portion 728 is also shown displaying previously visited area 714 and upcoming areas 712 . Previously visited areas 714 represent areas that have already been harvested, while upcoming areas 712 represent areas that have yet to be harvested. The panel display portion 728 is also shown, displaying various features of the panel. in the in 13 In the example shown, the map displayed is a predictive crop moisture map, such as functional predictive crop moisture map 360 . There is a set of crop moisture indicator markers 732 that are displayed in the areas 714 already visited. There is also a set of crop moisture indicator markings 732 shown in the upcoming areas 712 and there is a set of crop moisture indicator markings 732 shown in the next working unit 730 . 14 12 shows that the crop moisture indicator markings 732 consist of various symbols indicating an area of similar crop moisture. in the in 14 In the example shown, the ! symbol represents areas of high crop moisture; the * symbol represents areas of ideal crop moisture; and the # symbol represents an area of low crop moisture. Thus, the field display section 728 displays various measured or predicted values (or characteristics indicated by the values) located at various areas within the field and presents these measured or predicted values (or characteristics indicated by or derived from the values) with a plurality of indicator markings 732. As shown, field display portion 728 includes indicator markings, particularly crop moisture indicator markings 732 in the illustrated example of FIG 13 at specific positions associated with specific positions on the indicated field. In some cases, each position of the field may have an indicator mark associated with it. Thus, in some cases, an indicator mark may be provided at each location of the tile display portion 728 to identify the type of feature being imaged for any particular location of the tile. Accordingly, the present disclosure contemplates providing an indicator mark, such as crop moisture indicator mark 732 (as in the context of the present example of FIG 13 ) at one or more positions on the field display portion 728 to identify the type, degree, etc. of the displayed feature, thereby identifying the feature at the corresponding position in the displayed field. As previously described, the indicator markings 732 may consist of various symbols, and as described below, the symbols may be any display feature, such as different colors, shapes, patterns, intensities, text, symbols, or other display features. In some cases, each position of the field may have an indicator mark associated with it. Thus, in some cases, an indicator mark may be provided at each location of the tile display portion 728 to identify the type of feature being imaged for any particular location of the tile. Accordingly, the present disclosure contemplates providing an indication marker, such as loss level indicator marker 732 (as described in the context of the present example in FIG 11 ) at one or more positions on the field display portion 728 to identify the type, degree, etc. of the displayed feature, thereby identifying the feature at the corresponding position in the displayed field.

In other examples, the map displayed may be one or more of the maps described herein, including information maps, pre-information maps, the functional predictive maps such as predictive maps or predictive control zone maps, or a combination thereof. Thus, the displayed markers and features correlate with the information, data, features, and values provided by the one or more displayed cards.

In the example of 13 the user interface display 720 also has a control display section 738 . Control display portion 738 allows the operator to display information and interact with the display of user interface 720 in various ways.

For example, the actuators and indicator markers in section 738 may be displayed as individual items, fixed lists, scrollable lists, drop-down menus, or drop-down lists. in the in 13 In the example shown, the display section 738 displays information for the three different stem diameter categories corresponding to the three symbols mentioned above. The display portion 738 also includes a set of touch-sensitive actuators that the operator 260 can interact with by touch. For example, the operator 260 may touch the touch-sensitive actuators with a finger to activate the respective touch-sensitive actuator. As shown, the display portion 238 also includes a number of interactive tabs, such as a "crop moisture" tab 762 and an "other" tab 770. By activating one of the Tabs can change what values are displayed in sections 728 and 738. For example, as shown, crop moisture tab 762 is activated and thus the values mapped to section 728 and shown in section 738 correspond to crop moisture values. When operator 260 touches tab 770, touch gesture handler 664 updates sections 728 and 738 to display other farming features, such as operator commands.

As in 14 As shown, display section 738 includes an interactive flag display section indicated generally at 741 . The interactive flag display section 741 includes a flag column 739 showing flags that have been set automatically or manually. The flag actuator 740 allows the operator 260 to mark a position, such as the current position of the agricultural harvester, or another position on the field designated by the operator, and add information indicative of the attribute, such as crop moisture , which is found at the current position. For example, when the operator 260 operates the flag actuator 740 by touching the flag actuator 740, the touch gesture handling system 664 in the operator interface controller 231 identifies the current location as a location where the agricultural harvesting machine 100 has encountered high crop moisture. When the operator 260 touches the button 742, the touch gesture handler system 664 identifies the current location as a location where the agricultural harvesting machine 100 has encountered ideal crop moisture. When the operator 260 touches the button 744, the touch gesture handler system 664 identifies the current location as a location where the agricultural harvesting machine 100 encountered low crop moisture. Upon actuation of one of flag actuators 740, 742, or 744, touch gesture handler system 664 can control visual control signal generator 684 to add an icon corresponding to the identified feature on panel display portion 728 at a location identified by the user. This allows areas of the field where the predicted value did not accurately represent an actual value to be flagged for later analysis and also used in machine learning. In other examples, the operator may designate areas in front of or around agricultural harvester 100 by manipulating one of flag actuators 740, 742, or 744 such that control of agricultural harvester 100 may be performed based on the value determined by operator 260.

The display portion 738 also includes an interactive marker display portion indicated generally at 743 . The interactive marker display section 743 includes a symbol column 746 that displays the symbols corresponding to each category of values or attributes (in the case of 13 crop moisture) tracked on the field display section 728. The display section 738 also includes an interactive identifier display section, indicated generally at 745 . The interactive identifier display section 745 includes an identifier column 748 showing the identifier (which may be a textual identifier or other identifier) representing the category of values or attributes (in the case of 13 , crop moisture) identified. Without limitation, the icons in icon column 746 and the identifiers in identifier column 748 may include any display feature, such as different colors, shapes, patterns, intensities, text, symbols, or other display features, and may be customizable through interaction of an operator of agricultural harvesting machine 100.

The display portion 738 also includes an interactive value display portion indicated generally at 747 . The interactive value display section 747 includes a value display column 750 that displays selected values. The selected values correspond to the attributes or values being tracked or displayed on panel display portion 728, or both. The selected values can be selected by an operator of the agricultural harvesting machine 100 . The selected values in the value display column 750 define a range of values or a value by which other values, such as predicted values, are to be classified. So in the example in 13 a predicted or measured crop moisture that reaches 16% or more is classified as "high crop moisture" and a predicted or measured crop moisture that reaches 12% or less is classified as "low crop moisture". In some examples, the selected values may include a range such that a predicted or measured value that is within the range of the selected value is classified under the corresponding identifier. As in 13 As shown, “ideal crop moisture” includes a range of 13%-15%, such that a measured or predicted crop moisture value falling within the range of 13%-15% is classified as “ideal crop moisture”. The selected values in value display column 750 are adjustable by an operator of agricultural harvesting machine 100 . In one example, the operator 260 can select the particular portion of the panel display portion 728 for which the values should be displayed in column 750. Thus, the values in column 750 may correspond to values in display sections 712, 714, or 730.

The display section 738 also includes an interactive threshold display section, indicated generally at 749 . The interactive threshold display section 749 includes a threshold display column 752 that displays action thresholds. The action thresholds in column 752 may be thresholds corresponding to the selected values in value display column 750 . If the predicted or measured values of features being tracked or indicated, or both, meet the appropriate action thresholds in threshold indication column 752 , then control system 214 takes one or more actions identified in column 754 . In some cases, a measured or predicted value may satisfy a corresponding action threshold by meeting or exceeding the corresponding action threshold. For example, in one example, operator 260 may select a threshold to change the threshold by touching the threshold in threshold display column 752 . Once selected, the operator 260 can change the threshold. The thresholds in column 752 may be configured to take the designated action when the measured or predicted value of the feature exceeds, equals, or is below the threshold. In some cases, the threshold may represent a range of values or a range of deviations from the selected values in the value display column 750 such that a predicted or measured feature value that meets or falls within the threshold satisfies the threshold. For example, in the crop moisture example, a predicted crop moisture falling within 5% of 16% moisture will meet the appropriate action threshold (within 5% of 16% moisture) and an action such as adjusting the speed of the agricultural harvester or the Adjusting the header is taken by the control system 214 . In other examples, the thresholds in the Threshold Display Column 752 are separate from the selected values in the Value Display column 750, such that the values in the Value Display column 750 define the classification and display of predicted or measured values, while the Action Thresholds define when an action is taken based on the measured or predicted values. For example, while a predicted or measured crop moisture of 16% may be referred to as "high crop moisture" for classification and display purposes, the action threshold may be 17% such that no action is taken until the crop moisture meets the threshold. In other examples, the thresholds in threshold display column 752 may include distances or times. For example, in the example of a distance, the threshold may be a threshold distance from the area of the field where the measured or predicted value is georeferenced that the agricultural harvesting machine 100 must have before any action is taken. For example, a Threshold Distance value of 1.52 m (5 feet) would mean that an action would be taken if the agricultural harvester is in or within 1.52 m (5 feet) of the area of the field where the measured or predicted value is georeferenced. In an example where the threshold is time, the threshold may be a threshold time for the agricultural harvester 100 to reach the area of the field where the measured or predicted value is georeferenced. For example, a threshold of 5 seconds would mean that an action is taken when the agricultural harvesting machine 100 is still 5 seconds away from the area of the field where the measured or predicted value is georeferenced. In such an example, the current position and the driving speed of the agricultural harvesting machine can be taken into account.

The display portion 738 also includes an interactive action display portion indicated generally at 751 . Interactive action display section 751 includes an action display column 754 that displays action identifiers that indicate actions to be taken when a predicted or measured value meets an action threshold in threshold display column 752 . The operator 260 can touch the action identifiers in column 754 to change the action to be taken. When a threshold is met, action can be taken. For example, at the bottom of column 754, an "increase header" action, a "lower header" action, an "increase speed" action, and a "decrease speed" action are identified as actions to be performed when the measured or predicted value meets the threshold in column 752. In some examples, when a threshold is met, multiple actions can be taken. For example, a position of the header (e.g., height, tilt, or roll) and a speed of the agricultural machine can be adjusted. These are just some examples.

The actions that can be specified in column 754 can be any of a variety of different types of actions. For example, the actions may include a keep away action that, when performed, prevents the agricultural harvester 100 from continuing to harvest in an area. The actions may include a “change speed” action that, when performed, changes the ground speed of the agricultural harvesting machine 100 through the field. The actions may include a change setting action to change a setting of an internal actuator or another WMA or set of WMAs, or to implement a setting change action that changes a setting, such as a header position adjustment (e.g., header height adjustment, header tilt adjustment, or a header roller setting) along with various other settings. These are just examples, and a variety of other actions are contemplated here.

The elements shown on the user interface display 720 can be controlled visually. Visually controlling the interface display 720 may be performed to capture the operator's 260 attention. For example, the elements can be controlled to modify the intensity, color, or pattern with which the elements are displayed. In addition, the elements can be controlled so that they blink. The changes described in the visual appearance of the elements are provided as examples. Consequently, other aspects of the visual appearance of the elements can be altered. Therefore, the elements may be modified in a desired manner in various circumstances, for example, to attract the operator's 260 attention. While a certain number of items are shown on user interface display 720, they need not be. In other examples, user interface display 720 may include more or fewer items, including more or less of a particular item.

Returning to the flow chart of 12 the description of the operation of the operator interface controller 231 continues. At block 760 , operator interface controller 231 recognizes an input setting of a flag and controls touch-sensitive user interface display 720 to display the flag on panel display portion 728 . The recognized input may be operator input, as indicated at 762 , or input from another controller, as indicated at 764 . At block 766 , operator interface controller 231 recognizes an in situ sensor input indicative of a measured feature of the field from one of in situ sensors 208 . At block 768, visual control signal generator 684 generates control signals to control user interface display 720 to display actuators, to modify user interface display 720, and to modify machine controls. For example, block 770 represents that one or more of the actuators for setting or modifying the values in columns 739, 746, and 748 may be displayed. Thus, the user can set flags and change the properties of those flags. Block 772 represents that action thresholds in column 752 are displayed. Block 776 represents the actions in column 754 being displayed and block 778 represents the selected value in column 750 being displayed. Block 780 indicates that a variety of other information and actuators may be displayed on user interface display 720 as well.

At block 782 , operator input command processing system 654 captures and processes operator inputs corresponding to interactions with user interface display 720 performed by operator 260 . When the user interface mechanism on which user interface display 720 is displayed is a touch-sensitive display screen, interaction inputs with the touch-sensitive display screen by operator 260 may be touch gestures 784 . In some cases, the operator interaction inputs may be inputs using a point and click device 786 or other operator interaction inputs 788 .

At block 790, operator interface controller 231 receives signals indicative of an alarm condition. For example, block 792 indicates that signals may be received from control input processing system 668 indicating that detected or predicted values meet threshold conditions present in column 752. As previously explained, the threshold conditions may include values that are below a threshold, at a threshold, or above a threshold. Block 794 shows that in response to receiving an alarm condition, the action signal generator 660 can alert the operator 260 using the visual control signal generator 684 to generate visual alarms, using the audio control signal generator 686 to generate audio alarms, using the haptic control signal generator 688 to generate haptic alerts, or by using any combination thereof. Likewise, the control output generator 670, such as indicated by block 796, generate outputs to other controls in control system 214 so that those controls perform the appropriate action identified in column 754. Block 798 indicates that operator interface controller 231 may recognize and process alarm conditions in other ways.

Block 900 shows that language processing system 662 can recognize and process input that calls into language processing system 658 . Block 902 shows that performing language processing may include using dialog management system 680 to perform a dialog with operator 260 . Block 904 shows that speech processing may include providing signals to control output generator 670 so that control operations are performed automatically based on the speech inputs.

Table 1 below shows an example dialog between operator interface control 231 and operator 260 . In the example shown in Table 1, the wake-up word is "Johnny".

Table 1

Operator: "Johnny, update me on the current crop moisture."

Operator Interface Control: "Crop moisture is currently high."

Operator: "Johnny, what should I do about crop moisture?"

Operator interface control: "Reduce ground speed by 2 km/h to achieve desired feed rate."

Table 2 shows an example where speech synthesis component 676 provides an output to audio control signal generator 686 to provide audible updates on an intermittent or periodic basis. The interval between updates may be time based, such as every five minutes, or coverage or distance based, such as every two hectares, or exception based, such as when a measured value is greater than a threshold.

Table 2

Operator Interface Control: “Crop moisture has been high for the last 10 minutes.”

Operator interface control: "Predicted crop moisture for the next 1 hectare is medium."

Operator interface controls: "Caution: upcoming crop moisture change, ground speed increased by 2 km/h."

The example shown in Table 3 illustrates that some actuators or user input mechanisms on touch-sensitive display 720 may be supplemented with voice dialogue. The example in Table 3 illustrates that the action signal generator 660 can generate action signals to automatically mark a crop moisture range in the field to be harvested.

Table 3

Human: "Johnny, mark the area with high crop moisture."

Operator interface control: "High crop moisture area is highlighted."

The example presented in Table 4 illustrates that the action signal generator 660 may communicate with the operator 260 to start and stop marking a crop moisture area.

Table 4

Human: "Johnny, start marking the area with high crop moisture."

Operator interface control: "Mark area of high crop moisture."

Human: "Johnny, stop marking the area with high crop moisture."

Operator interface control: "High crop moisture area marking stopped."

The example shown in Table 5 illustrates that the action signal generator 160 can generate signals to mark an area of crop moisture in a manner different than those shown in Tables 3 and 4.

Table 5

Human: "Johnny, mark the next 30 meters as a low crop moisture area."

Operator Interface Control: "The next 30 meters is marked as a low crop moisture area."

Referring again to 12 Block 906 illustrates that operator interface controller 231 may otherwise detect and process conditions for outputting a message or other information. For example, the other controller interaction system 656 may recognize input from other controllers indicating that the operator 260 should be presented with alarms or output messages. Block 908 shows that the outputs can be audio messages. Block 910 shows that the outputs can be visual messages and block 912 shows that the outputs can be haptic messages. Until the operator interface controller 231 determines that the current harvesting operation is complete, as indicated by block 914, processing returns to block 698 where the geographical position of the harvester 100 is updated and processing continues as described above to the user interface display 720 To update.

Once the process is complete, any desired values that are displayed or have been displayed on the user interface display 720 can be saved. These values can also be used in machine learning to improve various sections of the predictive model generator 210, predictive map generator 212, control zone generator 213, control algorithms, or other elements. Block 916 indicates storage of the desired values. The values can be stored locally on the agricultural harvester 100 or the values can be stored at a remote server location or sent to another remote system.

Thus, it can be seen that one or more maps are obtained from an agricultural harvester showing agricultural feature values, such as predicted or historical crop moisture values, topographical feature values, vegetative index values, or soil property values, at various geographic locations of a field to be harvested. An in situ sensor on the harvester senses a trait having values indicative of an agricultural trait as the agricultural harvester moves through the field. A predictive map generator generates a predictive map that predicts control values for various locations based on the values of the agricultural feature in the map and the agricultural feature sensed by the in situ sensor. A control system controls the controllable subsystem based on the control values in the predictive map.

A control value is a value on which an action can be based. A control value, as described herein, may include any value (or characteristics indicated by or derived from the value) that can be used in controlling agricultural harvesting machine 100 . A control value can be any value that indicates an agricultural characteristic. A control value can be a predictive value, a measured value, or a recognized value. A control value may include any of the values provided by a card, such as any of the cards described herein, for example, a control value may be a value provided by an information card, a value provided by a preliminary information card, or a Value provided by a predictive map, such as a functional predictive map. A control value may also include any of the characteristics indicated by or derived from the values sensed by any of the sensors described herein. In other examples, a control value may be provided by an operator of the agricultural machine, such as a command input by an operator of the agricultural machine.

Processors and servers have been mentioned in this discussion. In one example, the processors and servers include computer processors with associated memory and timing circuitry, not shown separately. The processors and servers are functional parts of the systems or devices that the processors and servers belong to and are activated by, and facilitate the functionality of the other components or elements in those systems.

A number of user interface displays were also discussed. The displays can take a number of different forms and can have a number of different user controlled operator interface mechanisms thereon. For example, the user-actuable operator interface mechanisms may include text boxes, check boxes, icons, links, drop-down menus, search boxes, and so on. The user actuable control interface mecha nisms can also be actuated in a wide variety of ways. For example, the user actuable operator interface mechanisms can be operator interface mechanisms such as a point-and-click device, a trackball or mouse, hardware buttons, switches, a joystick or keyboard, thumb switches or thumbpads, etc., a virtual keyboard or other virtual Actuators are actuated. In addition, when the screen on which the user actuable operator interface mechanisms are displayed is a touch-sensitive screen, the user actuable operator interface mechanisms can be actuated with touch gestures. User actuatable operator interface mechanisms can also be actuated using voice commands with speech recognition functionality. Speech recognition can be implemented using a speech recognition device, such as a microphone, and software operable to recognize speech and execute commands based on the received speech.

A number of data stores were also discussed. It is pointed out that the data stores can each be divided into several data stores. In some examples, one or more of the data stores may be local to systems accessing the data stores, one or more of the data stores may be remote from a system using the data store, or one or more data stores may be local while others are remote. All of these configurations are contemplated by the present disclosure.

In addition, the figures show a series of blocks with functions associated with each block. It is noted that fewer blocks may be used to illustrate that functionality assigned to multiple different blocks is performed by fewer components. More blocks can also be used, illustrating that functionality can be distributed across multiple components. In different examples, some functionality may be added and some may be removed.

It should be noted that the discussion above has described a variety of different systems, components, logic and interactions. It is understood that any or all such systems, components, logic, and interactions are powered by hardware elements, such as processors, memory, or other processing components, including but not limited to artificial intelligence components, such as neural networks, some of which are described below can be implemented that perform the functions associated with those systems, components, logics or interactions. In addition, any or all of the systems, components, logic, and interactions may be implemented by software loaded into memory and then executed by a processor or server or other computing component, as described below. Any or all of the systems, components, logic, and interactions may also be implemented through various combinations of hardware, software, firmware, etc., some examples of which are described below. These are some examples of different structures that can be used to implement any or all of the systems, components, logic, and interactions described above. Other structures can also be used.

14 12 is a block diagram of agricultural harvester 600 shown in FIG 2 shown agricultural harvesting machine 100 may be similar. Agricultural harvester 600 communicates with elements in a remote server architecture 500. In some examples, remote server architecture 500 provides computing, software, data access, and storage services that do not require end-user knowledge of the physical location or configuration of the system that provides the Services. In various examples, remote servers may provide the services over a wide area network, such as the Internet, using appropriate protocols. For example, remote server applications can be deployed over a wide area network and accessed through a web browser or other computer component. Software or components included in 2 shown and related data may be stored on servers at a remote location. The computing resources in a remote server environment can be consolidated at a remote data center site or distributed to a variety of remote data centers. Remote server infrastructures can provide services across shared data centers, although the services appear to the user as a single point of entry. Thus, the components and functions described herein can be provided from a remote server at a remote site via a remote server architecture. Alternatively, the components and functions may be provided by a server, or the components and functions may be installed directly or otherwise on end devices.

in the in 14 example shown are some elements those in 2 similar and these elements are numbered similarly. 14 FIG. 12 specifically shows that the predictive model generator 210 or the predictive map generator 212 or both may be located at a server location 502 that is remote from the agricultural harvester 600. FIG. Therefore engages in the in 14 For example, as shown, agricultural harvester 600 approaches systems via remote server location 502 .

14 also illustrates another example of a remote server architecture. 14 shows that some elements of 2 may be located at a remote server location 502, while others may be located at a different location. For example, data store 202 may be located at a separate site from site 502 and accessed through the remote server at site 502 . Regardless of where the elements are located, the elements can be accessed directly by the agricultural harvester 600 over a network, such as a wide area network or a local area network; the items may be hosted at a remote location by a service; or the items may be provided as a service or accessed via a connection service located at a remote location. In addition, data can be stored at any location and the stored data can be retrieved or forwarded to operators, users or systems. For example, physical carriers can be used instead of or in addition to electromagnetic radiation carriers. In some examples where network coverage is poor or non-existent, another machine, e.g. a tank truck or other mobile machine or vehicle, have an automatic, semi-automatic or manual information collection system. When the combine harvester apparatus 600 gets close to the machine containing the information gathering system, such as a tanker truck, prior to refueling, the information gathering system collects the information from the combine harvester 600 via any ad hoc wireless connection. The information collected may then be relayed to another network when the machine containing the received information reaches a location where wireless telecommunications service coverage or other wireless coverage is available. For example, a fuel truck may enter an area that has wireless communications coverage when it is traveling to a location to fuel other machines, or when it is at a main fuel depot. All of these architectures are considered herein. Furthermore, the information may be stored in the agricultural harvester 600 until the agricultural harvester 600 reaches an area of wireless communication coverage. The agricultural harvester 600 itself can send the information to another network.

It is also noted that the elements of 2 or portions thereof may be located on a variety of different devices. One or more of these devices may include an on-board computer, electronic control unit, display unit, server, desktop computer, laptop computer, tablet computer, or other mobile device, such as a palmtop computer, a mobile phone, a smartphone , a multimedia player, a personal digital assistant, etc.

In some examples, the remote server architecture 500 may include cyber security measures. Without limitation, these measures may include encryption of data on storage devices, encryption of data sent between network nodes, authentication of individuals or processes accessing data, and use of ledgers to record metadata, data, data transfers, data access and involve data transformations. In some examples, the ledgers may be distributed and immutable (eg, implemented as a blockchain).

15 1 is a simplified block diagram of an illustrative example of a portable or mobile computing device that may be used as a handheld device 16 of a user or customer in which the present system (or portions thereof) may be deployed. For example, a mobile device may be deployed in the cab of agricultural harvester 100 to generate, process, or display the maps discussed above. the 16-17 are examples of portable or mobile devices.

15 shows a general block diagram of the components of a terminal 16, showing some of the 2 can run the components shown, can interact with them, or both. A communications link 13 is provided in device 16, enabling the portable device to communicate with other computing devices and, in some examples, providing a channel for automatically receiving information, such as by scanning. Examples of communication links 13 include allowing communication over one or more communication protocols, such as wireless services used to provide cellular access to a network, and protocols that provide local wireless connections to networks.

In other examples, applications may be received on a removable Secure Digital (SD) card connected to an interface 15 . Interface 15 and communications links 13 communicate with a processor 17 (which may also embody the processors or servers of the other FIGS.) via a bus 19 which is also connected to memory 21 and input/output (I/O) components 23 and the clock 25 and the locating system 27 is connected.

I/O components 23 are provided in one example to facilitate input and output operations. I/O components 23 for various examples of terminal device 16 may include input components such as buttons, tactile sensors, optical sensors, microphones, touch screens, proximity sensors, accelerometer sensors, orientation sensors, and output components such as a display, speaker, and/or printer port . Other I/O components 23 can also be used.

Clock 25 illustratively includes a real-time clock component that outputs a time and date. This may also provide timing functions for processor 17, illustratively.

The location system 27 illustratively includes a component that outputs a current geographic position of the device 16 . This may include, for example, a global positioning system (GPS) receiver, a LORAN system, a dead reckoning system, a cellular triangulation system, or other positioning system. For example, location system 27 may also include mapping or navigation software that generates desired maps, navigation routes, and other geographic features.

Memory 21 stores operating system 29, network settings 31, applications 33, application configuration settings 35, data storage 37, communication drivers 39, and communication configuration settings 41. Memory 21 may include all types of tangible volatile and non-volatile computer-readable storage devices. Memory 21 may also include computer storage media (see below). Memory 21 stores computer-readable instructions that, when executed by processor 17, cause the processor to perform computer-implemented steps or functions in accordance with the instructions. The processor 17 can be activated by other components to enhance their functionality as well.

16 FIG. 12 shows an example where the device 16 is a tablet computer 600. FIG. In 16 the computer 601 with the user interface screen 602 is shown. The screen 602 can be a touch screen or a pen-enabled interface that receives input from a pen or stylus. The tablet computer 600 can also use a virtual on-screen keyboard. Of course, the computer 601 can also be connected to a keyboard or other user input device via any suitable attachment mechanism, such as a wireless connection or a USB port. Computer 601 may illustratively also receive voice input.

17 is similar to that 16 except that the device is a smartphone 71 . The smartphone 71 has a touch-sensitive display 73 that displays icons or graphics or other user input mechanisms 75 . The mechanisms 75 can be used by a user to run applications, make calls, perform data transfer operations, etc. In general, the smartphone 71 is built on top of a mobile operating system and offers more advanced computing power and connectivity than a feature phone.

It should be noted that other shapes of devices 16 are possible.

18 is an example of a computing environment in which elements of 2 can be used. With reference to 18 An exemplary system for implementing some embodiments includes a computing device in the form of a computer 810 programmed to operate as discussed above. The components of the computer 810 may include, but are not limited to, a processing unit 820 (which may include processors or servers from the preceding FIGURES), a system memory 830, and a system bus 821 that connects various system components including the system memory to the processing unit 820 couple. The system bus 821 can be any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, and a local bus with a variety of bus architectures. Memory and programs related to 2 may be described in appropriate parts of 18 be used.

Computer 810 typically includes a variety of computer-readable media. Computer-readable media can be any available media that can be accessed by the computer 810 and includes both volatile and non-volatile media, removable and non-removable media. By way of example and not limitation, computer-readable media can include computer storage media and communications media. Computer storage media is distinct from and does not include a modulated data signal or carrier wave. Computer-readable media includes hardware storage media, having volatile and non-volatile, removable and non-removable media, implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. Computational storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other storage technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cartridge, tape, disk storage or other magnetic storage device, or any other medium that can be used to store the desired information accessible via computer 810. Communication media may contain computer-readable instructions, data structures, program modules, or other data in a transport mechanism and includes any information delivery media. The term "modulated data signal" means a signal that has one or more of its characteristics adjusted or altered to encode information in the signal.

System memory 830 includes computer storage media in the form of volatile and/or non-volatile memory, or both, such as read-only memory (ROM) 831 and random access memory (RAM) 832. A basic input/output system 833 (BIOS) that contains the basic programs that help To transfer information between elements within the computer 810, such as at startup, is typically stored in ROM 831. RAM 832 typically contains data or program modules, or both, that are immediately accessible to processing unit 820 and/or are currently operating. Illustrated by way of example and not limitation 18 the operating system 834, the application programs 835, further program modules 836 and the program data 837.

Computer 810 may also include other removable/non-removable volatile/non-volatile computer storage media. For example, in 18 illustrates a hard disk drive 841 reading from or writing to non-removable, non-volatile magnetic media, an optical disk drive 855, and a non-volatile optical disk 856, by way of example only. Hard disk drive 841 is typically connected to system bus 821 via a non-removable storage interface, such as interface 840, and optical disk drive 855 is typically connected to system bus 821 via a removable storage interface, such as interface 850.

Alternatively or additionally, the functionality described herein may be performed at least in part by one or more hardware logic components. Illustrative types of hardware logic components that may be used include, for example, field-programmable gate arrays (FPGAs), application-specific integrated circuits (e.g., ASICs), off-the-shelf application-specific products (e.g., ASSPs), system -on-a-chip systems (SOCs), "Complex Programmable Logic Devices" (CPLDs), etc.

The drives and associated computer storage media discussed above and in 18 , provide storage of computer-readable instructions, data structures, program modules, and other data for computer 810. In 18 For example, the hard disk 841 is shown as the storing operating system 844, application programs 845, other program modules 846 and program data 847. It should be noted that these components can be either the same as or different from the operating system 834, the application programs 835, the other program modules 836 and the program data 837.

A user may enter commands and information into the computer 810 through input devices such as a keyboard 862, a microphone 863, and a pointing device 861 such as a mouse, trackball, or touchpad. Other input devices (not shown) may include a joystick, gamepad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit 820 via a user input interface 860, which is coupled to the system bus, but may also be connected via other interface and bus structures. A visual display 891 or other type of display device is also connected to the system bus 821 via an interface such as a video interface 890 . In addition to the monitor, computers also include other peripheral output devices such as speakers 897 and printer 896 that can be connected via an output peripheral interface 895 .

Computer 810 operates in a network environment via logical connections (such as CAN, LAN, or WAN) to one or more remote computers, such as remote computer 880.

When used in a LAN network environment, the computer 810 is connected to the LAN 871 via a network interface or adapter 870 . When used in a WAN network environment, the computer 810 typically includes a modem 872 or other means for establishing communication over the WAN 873, such as the Internet. In a networked environment, program modules may be stored on an external storage device. 18 illustrates, for example, that remote application programs 885 may reside on remote computer 880. FIG.

It should also be noted that the various examples described here can be combined in various ways. That is, portions of one or more examples may be combined with portions of one or more other examples. All of these are considered herein.

• ein Kommunikationssystem, das eine Karte empfängt, die Werte über die Erntegutfeuchtigkeit beinhaltet, die verschiedenen geografischen Positionen in einem Feld entsprechen;
• einen geografischen Positionssensor, der eine geografische Position der landwirtschaftlichen Arbeitsmaschine erkennt;
• einen In-situ-Sensor, der einen Wert eines landwirtschaftlichen Merkmals erkennt, der der geografischen Position entspricht;
• einen prädiktiven Kartengenerator, der eine funktionelle prädiktive Karte des Feldes erzeugt, die prädiktive Steuerwerte auf Grundlage der Werte der Erntegutsfeuchtigkeit in der Karte und auf Grundlage des Werts des landwirtschaftlichen Merkmals auf die verschiedenen geografischen Positionen in dem Feld abbildet;
• ein steuerbares Teilsystem; und
• ein Steuersystem, das ein Steuersignal erzeugt, um das steuerbare Teilsystem auf Grundlage der geografischen Position der landwirtschaftlichen Arbeitsmaschine und auf Grundlage der Steuerwerte in der funktionellen prädiktiven landwirtschaftlichen Karte zu steuern.

Example 1 is an agricultural working machine comprising:

• a communication system that receives a map that includes crop moisture values corresponding to various geographic locations in a field;
• a geographic position sensor that detects a geographic position of the agricultural working machine;
• an in situ sensor that detects a value of an agricultural feature that corresponds to the geographic location;
• a predictive map generator that generates a functional predictive map of the field that maps predictive control values to the various geographic locations in the field based on the crop moisture values in the map and based on the value of the agricultural characteristic;
• a controllable subsystem; and
• a control system that generates a control signal to control the controllable subsystem based on the geographical position of the agricultural work machine and based on the control values in the functional predictive agricultural map.

Example 2 is the agricultural work machine of any or all of the preceding examples, wherein the map is a predictive crop moisture map generated based on values from a previous map and values of crop moisture sensed in situ.

• einen prädiktiven landwirtschaftlichen Merkmalskartengenerator, der als die funktionelle prädiktive landwirtschaftliche Karte eine funktionelle prädiktive landwirtschaftliche Merkmalskarte erzeugt, die prädiktive Werte des landwirtschaftlichen Merkmals auf die verschiedenen geografischen Positionen in dem Feld abbildet.

Example 3 is the agricultural work machine of any or all of the preceding examples, where the predictive map generator includes:

• a predictive agricultural feature map generator that generates, as the functional predictive agricultural map, a functional predictive agricultural feature map that maps predictive values of the agricultural feature to the different geographic locations in the field.

Example 4 is the agricultural work machine of any or all of the preceding examples, wherein the in situ sensor recognizes as a value of an agricultural characteristic a value of an operator command indicative of a commanded action of the agricultural work machine.

• eine prädiktive Bedienerbefehlskarte, die als die funktionelle prädiktive landwirtschaftliche Karte eine funktionelle prädiktive Bedienerbefehlskarte erzeugt, die prädiktive Bedienerbefehlswerte auf die verschiedenen geografischen Positionen in dem Feld abbildet.

Example 5 is the agricultural work machine of any or all of the preceding examples, where the predictive map generator includes:

• a predictive operator command map that generates, as the functional predictive agricultural map, a functional predictive operator command map that maps predictive operator command values to the various geographic locations in the field.

• eine Einstellungssteuerung, die ein Bedienerbefehlssteuersignal erzeugt, das einen Bedienerbefehl auf Grundlage der erkannten geografischen Position und der funktionellen prädiktiven Bedienerbefehlskarte anzeigt, und das steuerbare Teilsystem auf Grundlage des Bedienerbefehlssteuersignals steuert, um den Bedienerbefehl auszuführen.

Example 6 is the agricultural work machine of any or all of the preceding examples, wherein the control system includes:

• an adjustment controller that generates an operator command control signal indicative of an operator command based on the detected geographic location and the functional predictive operator command map, and controls the controllable subsystem based on the operator command control signal to execute the operator command.

Example 7 is the agricultural work machine of any or all of the preceding examples, wherein the operator command control signal controls the control subsystem to provide a material feed rate adjusted by the agricultural machine.

• einen prädiktiven Modellgenerator, der ein prädiktives landwirtschaftliches Modell erzeugt, das eine Beziehung zwischen Erntegutfeuchtigkeit und dem landwirtschaftlichen Merkmal auf Grundlage eines Werts der Erntegutfeuchtigkeit in der Karte an der geografischen Position und des Werts des landwirtschaftlichen Merkmals, der durch den In-situ-Sensor erkannt wird, der der geografischen Position entspricht, modelliert, wobei der prädiktive Kartengenerator die funktionelle prädiktive landwirtschaftliche Karte auf Grundlage der Werte von Erntegutfeuchtigkeit in der Karte und auf Grundlage des prädiktiven landwirtschaftlichen Modells erzeugt.

Example 8 is the agricultural work machine of any or all of the preceding examples and further includes:

• a predictive model generator that generates a predictive agricultural model that shows a relationship between crop moisture and the agricultural trait based on a value of the crop moisture in the map at the geographic location and the value of the agricultural trait detected by the in situ sensor corresponding to the geographic position, wherein the predictive map generator generates the functional predictive agricultural map based on the values of crop moisture in the map and based on the predictive agricultural model.

• eine Bedienerschnittstellensteuerung, die eine Benutzerschnittstellenkartendarstellung der funktionellen prädiktiven landwirtschaftlichen Karte erzeugt, wobei die Benutzerschnittstellenkartendarstellung einen Feldabschnitt mit einer oder mehreren Markierungen umfasst, die die prädiktiven Steuerwerte an einer oder mehreren geografischen Positionen auf dem Feldabschnitt angeben.

Example 9 is the agricultural work machine of any or all of the preceding examples, the control system further comprising:

• an operator interface controller that generates a user interface map representation of the functional predictive agricultural map, the user interface map representation including a field portion with one or more markers indicating the predictive control values at one or more geographic locations on the field portion.

Example 10 is the agricultural work machine of any or all of the preceding examples, wherein the operator interface control generates the representation of the user interface map to include an interactive display section displaying a value display section indicating a selected value, an interactive threshold display section indicating an action threshold, and a an interactive action display section indicating a control action to be taken if one of the predictive control values meets the action threshold relative to the selected value, wherein the control system generates the control signal to control the controllable subsystem based on the control action.

• Erhalten einer Karte, die Werte von Erntegutfeuchtigkeit beinhaltet, die verschiedenen geografischen Positionen in einem Feld entsprechen;
• Erkennen einer geografischen Position der landwirtschaftlichen Arbeitsmaschine;
• Erkennen eines Werts eines landwirtschaftlichen Merkmals mit einem In-situ-Sensor, der der geografischen Position entspricht;
• Erzeugen einer funktionellen prädiktiven landwirtschaftlichen Karte des Feldes, die prädiktive Steuerwerte auf Grundlage der Werte von Erntegutfeuchtigkeit in der Karte und auf Grundlage des Werts des landwirtschaftlichen Merkmals auf die verschiedenen geografischen Positionen in dem Feld abbildet; und
• Steuern eines steuerbaren Teilsystems auf Grundlage der geografischen Position der landwirtschaftlichen Arbeitsmaschine und auf Grundlage der Steuerwerte in der funktionellen prädiktiven landwirtschaftlichen Karte.

Example 11 is a computer-implemented method for controlling an agricultural work machine, comprising:

• obtaining a map that includes crop moisture values corresponding to various geographic locations in a field;
• detecting a geographic location of the agricultural work machine;
• detecting a value of an agricultural feature with an in situ sensor that corresponds to the geographic location;
• generating a functional predictive agricultural map of the field that maps predictive control values to the various geographic locations in the field based on the values of crop moisture in the map and based on the value of the agricultural characteristic; and
• Controlling a controllable subsystem based on the geographical position of the agricultural work machine and based on the control values in the functional predictive agricultural map.

• Erhalten einer prädiktiven Erntegutfeuchtigkeitskarte, die als die Werte der Erntegutfeuchtigkeit prädiktive Werte der Erntegutfeuchtigkeit beinhaltet, die verschiedenen geografischen Positionen in dem Feld entsprechen.

Example 12 is the computer-implemented method of any or all of the preceding examples, wherein obtaining the map comprises:

• obtaining a predictive crop moisture map including as the crop moisture values predictive crop moisture values corresponding to different geographic locations in the field.

• Erzeugen einer funktionellen prädiktiven landwirtschaftlichen Merkmalskarte, die als prädiktive Steuerwerte prädiktive landwirtschaftliche Merkmalswerte auf die verschiedenen geografischen Positionen in dem Feld abbildet.

Example 13 is the computer-implemented method of any or all of the preceding examples, wherein generating the functional predictive agricultural map comprises:

• generating a functional predictive agricultural feature map that maps predictive control values of predictive agricultural feature values to the various geographic locations in the field.

• Erfassen eines Bedienerbefehls, der eine Befehlsaktion der landwirtschaftlichen Arbeitsmaschine anzeigt, mit dem In-situ-Sensor als den Wert des landwirtschaftlichen Merkmals.

Example 14 is the computer-implemented method of any or all of the preceding examples, wherein detecting the value of an agricultural characteristic with an in situ sensor comprises:

• detecting an operator command indicative of a command action of the agricultural work machine with the in situ sensor as the value of the agricultural characteristic.

• Erzeugen einer funktionellen prädiktiven Bedienerbefehlskarte, die als prädiktive Steuerwerte prädiktive Bedienerbefehlswerte auf die verschiedenen geografischen Positionen in dem Feld abbildet.

Example 15 is the computer-implemented method of any or all of the preceding examples, wherein generating the functional predictive agricultural map comprises:

• creating a functional predictive operator command map that maps predictive control values of operator command values to the various geographic locations in the field.

• Erzeugen eines Bedienerbefehlssteuersignals, das einen Bedienerbefehl auf Grundlage der erkannten geografischen Position und der funktionellen prädiktiven Bedienerbefehlskarte anzeigt; und
• Steuern des steuerbaren Teilsystems auf Grundlage des Bedienerbefehlssteuersignals, um den Bedienerbefehl auszuführen.

Example 16 is the computer-implemented method of any or all of the preceding examples, wherein controlling the controllable subsystem comprises:

• generating an operator command control signal indicative of an operator command based on the detected geographic location and the functional predictive operator command map; and
• controlling the controllable subsystem based on the operator command control signal to execute the operator command.

• Steuern des steuerbaren Teilsystems, um eine Vorschubgeschwindigkeit von Material durch die landwirtschaftliche Arbeitsmaschine einzustellen.

Example 17 is the computer-implemented method of any or all of the preceding examples, wherein controlling the controllable subsystem based on the operator command control signal to execute the operator command comprises:

• Controlling the controllable subsystem to adjust a rate of advancement of material through the agricultural work machine.

• Erzeugen eines prädiktiven landwirtschaftlichen Modells, das eine Beziehung zwischen der Erntegutfeuchtigkeit und dem landwirtschaftlichen Merkmal auf Grundlage eines Werts der Erntegutfeuchtigkeit in der Karte an der geografischen Position und des Werts des landwirtschaftlichen Merkmals, der durch den In-situ-Sensor erkannt wird, der der geografischen Position entspricht, modelliert, wobei das Erzeugen der funktionellen prädiktiven landwirtschaftlichen Karte das Erzeugen der funktionellen prädiktiven landwirtschaftlichen Karte auf Grundlage der Werte von Erntegutfeuchtigkeit in der Karte und auf Grundlage des prädiktiven landwirtschaftlichen Modells umfasst.

Example 18 is the computer-implemented method of any or all of the preceding examples and further comprising:

• Generating a predictive agricultural model that establishes a relationship between crop moisture and the agricultural trait based on a value of crop moisture in the map at the geographic location and the value of the agricultural trait detected by the in situ sensor corresponding to the geographic position corresponds, wherein generating the functional predictive agricultural map comprises generating the functional predictive agricultural map based on the values of crop moisture in the map and based on the predictive agricultural model.

• ein Kommunikationssystem, das eine Karte empfängt, die Werte über die Erntegutfeuchtigkeit beinhaltet, die verschiedenen geografischen Positionen in einem Feld entsprechen;
• einen geografischen Positionssensor, der einen Wert eines landwirtschaftlichen Merkmals erkennt, der einer geografischen Position entspricht;
• einen prädiktiven Modellgenerator, der ein prädiktives landwirtschaftliches Modell erzeugt, das eine Beziehung zwischen Erntegutfeuchtigkeit und dem landwirtschaftlichen Merkmal auf Grundlage eines Werts von Erntegutfeuchtigkeit in der Karte an der geografischen Position und des Werts des landwirtschaftlichen Merkmals modelliert, der durch den In-situ-Sensor erkannt wird, der der geografischen Position entspricht;
• einen prädiktiven Kartengenerator, der eine funktionelle prädiktive landwirtschaftliche Karte des Feldes erzeugt, die prädiktive Steuerwerte auf Grundlage der Werte von Erntegutfeuchtigkeit in der Karte und auf Grundlage des prädiktiven landwirtschaftlichen Modells auf die verschiedenen geografischen Positionen in dem Feld abbildet;
• ein steuerbares Teilsystem; und
• ein Steuersystem, das ein Steuersignal erzeugt, um das steuerbare Teilsystem auf Grundlage der geografischen Position der landwirtschaftlichen Arbeitsmaschine und auf Grundlage der Steuerwerte in der funktionellen prädiktiven landwirtschaftlichen Karte zu steuern.

Example 19 is an agricultural work machine comprising:

• a communication system that receives a map that includes crop moisture values corresponding to various geographic locations in a field;
• a geographic position sensor that detects a value of an agricultural feature that corresponds to a geographic position;
• a predictive model generator that generates a predictive agricultural model that models a relationship between crop moisture and the agricultural trait based on a value of crop moisture in the map at the geographic location and the value of the agricultural trait detected by the in situ sensor corresponding to the geographic position;
• a predictive map generator that generates a functional predictive agricultural map of the field that maps predictive control values to the various geographic locations in the field based on the values of crop moisture in the map and based on the predictive agricultural model;
• a controllable subsystem; and
• a control system that generates a control signal to control the controllable subsystem based on the geographical position of the agricultural work machine and based on the control values in the functional predictive agricultural map.

• eine Vorschubgeschwindigkeitssteuerung, die ein Vorschubgeschwindigkeitssteuersignal auf der Grundlage der erkannten geografischen Position und der funktionellen prädiktiven landwirtschaftlichen Karte erzeugt und das steuerbare Teilsystem auf der Grundlage des Vorschubgeschwindigkeitssteuersignals steuert, um eine Vorschubgeschwindigkeit des Materials durch die landwirtschaftliche Arbeitsmaschine zu steuern;
• eine Einstellungssteuerung, die ein Geschwindigkeitssteuersignal auf Grundlage der erkannten geografischen Position und der funktionellen prädiktiven landwirtschaftlichen Karte erzeugt und das steuerbare Teilsystem auf Grundlage des Geschwindigkeitssteuersignals steuert, um eine Geschwindigkeit der landwirtschaftlichen Arbeitsmaschine zu steuern;
• eine Erntevorsatzsteuerung, die ein Erntevorsatz-Steuersignal auf Grundlage der erkannten geografischen Position und der funktionellen prädiktiven landwirtschaftlichen Karte erzeugt und das steuerbare Teilsystem auf Grundlage des Erntevorsatz-Steuersignals steuert, um einen Erntevorsatz an der landwirtschaftlichen Arbeitsmaschine zu steuern; und
• eine Einstellungssteuerung, die ein Bedienerbefehlssteuersignal erzeugt, das einen Bedienerbefehl auf Grundlage der erkannten geografischen Position und der funktionellen prädiktiven landwirtschaftlichen Karte anzeigt, und das steuerbare Teilsystem auf Grundlage des Bedienerbefehlssteuersignals steuert, um den Bedienerbefehl auszuführen.

Example 20 is the agricultural work machine of any or all of the preceding examples, wherein the control system includes at least one of the following:

• a feed rate controller that generates a feed rate control signal based on the detected geographic location and the functional predictive agricultural map and controls the controllable subsystem based on the feed rate control signal to control a feed rate of material through the agricultural work machine;
• an adjustment controller that generates a speed control signal based on the detected geographic position and the functional predictive agricultural map and controls the controllable subsystem based on the speed control signal to control a speed of the agricultural work machine;
• a header controller that generates a header control signal based on the detected geographic location and the functional predictive agricultural map and controls the controllable subsystem based on the header control signal to control a header on the agricultural work machine; and
• an adjustment controller that generates an operator command control signal indicative of an operator command based on the detected geographic position and the functional predictive agricultural map, and the controllable subsystem based on the Operator command control signal controls to execute the operator command.

Although the subject matter has been described in language specific to structural features or methodical acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the features and acts described above are disclosed as example forms of the claims

Claims (15)

Agricultural working machine (100), comprising: a communication system (206) that receives a map including crop moisture values corresponding to various geographic locations in a field; a geographic position sensor (204) that detects a geographic position of the agricultural work machine; an in situ sensor (208) that detects a value of an agricultural feature that corresponds to the geographic location; a predictive map generator (212) that generates a functional predictive map of the field that maps predictive control values to the various geographic locations in the field based on the crop moisture values in the map and based on the value of the agricultural characteristic; a controllable subsystem (216); and a control system (214) that generates a control signal to control the controllable subsystem (216) based on the geographical position of the agricultural work machine (100) and based on the control values in the functional predictive agricultural map. Agricultural working machine after claim 1 wherein the map is a predictive crop moisture map generated based on values from a previous map and values of the in-situ detected crop moisture. Agricultural working machine after claim 1 wherein the predictive map generator comprises: a predictive agricultural feature map generator that generates, as the functional predictive agricultural map, a functional predictive agricultural feature map that maps predictive values of the agricultural feature to the different geographic locations in the field. Agricultural working machine after claim 1 wherein the in situ sensor recognizes as a value of an agricultural characteristic a value of an operator command indicative of a commanded action of the agricultural work machine. Agricultural working machine after claim 4 wherein the predictive map generator comprises: an operator command predictive map generating, as the agricultural predictive functional map, an operator command functional predictive map mapping predictive operator command values to the various geographic locations in the field. Agricultural working machine after claim 5 , wherein the control system comprises: an adjustment controller that generates an operator command control signal indicative of an operator command based on the detected geographic location and the functional predictive operator command map, and controls the controllable subsystem based on the operator command control signal to execute the operator command. Agricultural working machine after claim 6 wherein the operator command control signal controls the control subsystem to adjust a material feed rate through the agricultural work machine. Agricultural working machine after claim 1 and further comprising: a predictive model generator that generates a predictive agricultural model that generates a relationship between crop moisture and the agricultural trait based on a value of the crop moisture in the map at the geographic location and the value of the agricultural trait determined by the in situ sensor corresponding to the geographical position is modeled, wherein the predictive map generator generates the functional predictive agricultural map based on the values about crop moisture in the map and based on the predictive agricultural model. A computer-implemented method for controlling an agricultural work machine (100), comprising: obtaining a map (258) that includes crop moisture values corresponding to various geographic locations in a field; detecting a geographic position of the agricultural work machine (100); detecting a value of an agricultural characteristic with an in situ sensor (208) comprising the corresponds to geographical position; generating a functional predictive agricultural map of the field that maps predictive control values to the various geographic locations in the field based on the values of crop moisture in the map and based on the value of the agricultural characteristic; and controlling a controllable subsystem (216) based on the geographical position of the agricultural work machine (100) and based on the control values in the functional predictive agricultural map. Computer-implemented method claim 9 wherein obtaining the map comprises: obtaining a predictive crop moisture map including, as the crop moisture values, predictive crop moisture values corresponding to different geographic locations in the field. Computer-implemented method claim 9 wherein generating the functional predictive agricultural map comprises: generating a functional predictive agricultural feature map mapping as predictive control values predictive agricultural feature values to the various geographic locations in the field. Computer-implemented method claim 9 wherein detecting the value of an agricultural attribute with an in situ sensor comprises: detecting an operator command indicative of a commanded action of the agricultural work machine with the in situ sensor as the value of the agricultural attribute. Computer-implemented method claim 12 wherein generating the functional predictive agricultural map comprises: generating a functional predictive operator command map mapping as predictive control values predictive operator command values to the various geographic locations in the field. Computer-implemented method Claim 13 wherein controlling the controllable subsystem comprises: generating an operator command control signal indicative of an operator command based on the recognized geographic location and the functional predictive operator command map; and controlling the controllable subsystem based on the operator command control signal to execute the operator command. Agricultural working machine (100), comprising: a communication system (206) that receives a map including crop moisture values corresponding to various geographic locations in a field; a geographic position sensor (204) that detects a value of an agricultural feature corresponding to a geographic position; a predictive model generator (210) that generates a predictive agricultural model that models a relationship between crop moisture and the agricultural trait based on a value of the crop moisture in the map at the geographic location and the value of the agricultural trait obtained by the in situ - detecting a sensor (208) corresponding to the geographic location; a predictive map generator (212) that generates a functional predictive agricultural map of the field that maps predictive control values to the various geographic locations in the field based on the crop moisture values in the map and based on the predictive agricultural model; a controllable subsystem (216); and a control system (214) that generates a control signal to control the controllable subsystem (216) based on the geographical position of the agricultural work machine (100) and based on the control values in the functional predictive agricultural map.

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