DE102021124364A1 - CROP MOISTURE MAP GENERATION AND CONTROL SYSTEM - 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 trait at various locations in the field based on a relationship between the values in the one or more information maps and the agricultural trait 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 adverse 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.

EXECUTIVE 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.
• 6 Figure 12 is a block diagram showing an example agricultural harvester in communication with a remote server environment.
• the 7-9 show examples of mobile devices that can be used in an agricultural harvesting machine.
• 10 Figure 12 is a block diagram showing an example of a computing environment that may be used in an agricultural harvester and the architectures illustrated in the previous figures.

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. Any alterations and further modifications to the described devices, systems, methods, and any further application of the principles of the present disclosure are fully contemplated 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 acquired concurrently with a farming operation in combination with previous data to produce a functional predictive map and in particular to generate 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. Since ground slope indicates a change in elevation, providing two or more elevation values allows the slope to be calculated over the areas of 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 (i.e., 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. The soil types may include, for example, sandy soil, clay soil, mud soil, peat soil, chalky soil, clay soil, and various other soil types. Soil moisture can refer to the amount of water stored or otherwise contained in the soil. The floor humidity can also be referred to as soil wetness. Land cover may refer to the amount of elements or materials that cover the ground, including vegetation material such as crop residues or cover crops, dirt, and various other elements or materials. Generally, in agricultural terms, land cover includes a measure of residual crop residues, such as a remaining mass of plant stems, as well as a measure 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 individual sand, mud and clay particles, 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, and previous farming operations performed in past seasons, such as a previous harvest. The agricultural machines that perform these agricultural operations may have on-board sensors that recognize features indicative of soil properties, for example features indicative of soil type, soil moisture, soil cover, soil structure, and various other features indicative of various other soil properties.

In addition, 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 image data. 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 collect a core sample at different geographic locations across the field of 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 an operations manager, who may provide various data collected or observed by the user or operator.

In addition, the soil property 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 from previous harvesting operations on the field or fields collected. 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. However, in another example, the crop moisture value unit may be expressed in various other ways, such as a level value, for example “high, medium, low” or “high, normal/desired/expected, low” as well as various other expressions. 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, a vegetative index map, a topographic map, a soil feature map, or a map generated during a preliminary operation, and also a 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 some examples, 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 . The agricultural harvester 100 includes headers, such as a header 102 and a cutter, generally indicated at 104. The agricultural harvester 100 also includes a feeder housing 106, a feed accelerator 108, and a thresher, generally indicated at 110. The feeder housing 106 and the Feed accelerators 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 cutting device 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 crop 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 . That 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 oscillation 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 chopper gap 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. The crop property sensors can also measure the feed rate of biomass through the feeder house 106, through the separator 116 or elsewhere in the agricultural harvesting machine 100. The crop property sensors may also sense the rate of travel 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 quantities sensed. The crop property sensors may include one or more crop moisture sensors that sense moisture of crops being harvested by the agricultural harvesting machine.

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 map and combining information from the advance information map with a georeferenced sensor signal generated by an in situ sensor, the sensor signal being indicative of a feature in the field, such as features of crop or weeds, that is 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 obtained 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 interfaces to carry out operations. In some cases, a functional predictive map can be used for one or more of: controlling an agricultural work machine, such as an agricultural harvester, presenting to an operator or other user, and presenting 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 may be presented to an operator or user, or used to control 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 a prior 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.

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 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 historical crop moisture map can be selected from one of the last years when the weather conditions during the growing season were similar to the current year's weather conditions. 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" (i.e., experienced drought or reduced rainfall), while the current year is "wet" (i.e., 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, 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 represents a relationship between the values sensed by the in situ sensor 208 and a feature mapped onto the field by the prior information map 258 . 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 the type of in situ data captured by 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 type of data in the functional predictive map 263 may be different than the type of data in the prior information map 258 but related to the type of data in the prior information map 258 . For example, the type of data in advance information map 258 may indicate the type of data in functional predictive map 263 in some examples in 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 advance information map 258 and the model generated by the predictive model generator 210 to generate a functional predictive generate a map 263 predicting 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 locations and the relationship between the soil property value and 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 has predicted biomass values at various geographic locations in the field depicts. 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 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 predictive map 264. In this case, control zone generator 213 analyzes the map and identifies control zones, which have a defined size in order to take the response time of the controllable subsystems 216 into account. 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 crop types and then generate the predictive map 264 and the predictive control zone map 265 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 dem Operator 260 can be displayed and used to control or calibrate agricultural harvesting machine 100, and in other examples, the control zones can 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 control steering subsystem 252 to steer agricultural harvester 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 used in agricultural harvest ma 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 advance information card 258. Examples of the advance information card 258 or receiving the advance information card 258 are discussed with respect to blocks 281, 282, 284, and 286. 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 displayed. 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 a comparison 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 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 preliminary information map 258 and the in situ values detected 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 generates 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 predictive map generator 212 configures the predictive map 264 so 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, such as indicated by block 296.

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 contain 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 plot 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 corresponds to measured values that may be measured by sensors on agricultural harvester 100 as agricultural harvester 100 moves 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 is understood that the particular control signals that are generated and the particular controllable subsystems 216 that are controlled may vary based on one or more different things. 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 can 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. Accordingly, 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 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 deviations within the in situ sensor data (or the relationship between the in situ sensor data and the information in the pre-information map 258) are within a selected range, or are less than a defined amount, or are 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 another example, the transition of the agricultural harvesting machine 100 to a different topography or to another control zone can 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, predictive control zone map 265, and predictive model may be stored locally on data storage 202 or sent to a remote system using communication system 206 for later 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 moisture map 333, a topographical map 341, a soil feature 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. B. rain, snow cover, hail, wind, include 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, a soil feature map 343, or a preliminary operational map 400 as a preliminary information map, the predictive model generator 210 also receives a geographic position indicator 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 identifying one or more crop moisture values. 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 orders the aggregated crop moisture from a certain measurement interval or a certain time previously traversed georeferenced areas that are part of the V-shaped area.

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 used by the predictive map generator 212 to predict crop moisture at various locations based on the georeferenced vegetative index value contained in the 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 generate crop moisture at various locations based on the georefe referenced soil property value contained in soil property map 343 at the same positions 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 shown.

The predictive crop moisture model 350 is provided to the predictive map generator 212 . In the example of 4 For example, 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 12 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 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 343, 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 cards 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 harvester 100, since there is a period of time between the time that the initial contact between the crop and the agricultural harvester occurs and the time that the crop reaches the Crop moisture sensor 336 reached, 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, when an agricultural harvesting machine moves through a field in which an agricultural harvesting operation is being carried out, 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 propulsion 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 of 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 which the control system 214 receives a functional predictive map or a functional predictive map with added control zones, controls the operator interface controller 231 the operator interface mechanisms 218 of the agricultural harvesting machine 100. In another example where the control system 214 implements the functional predictive map or the functional predictive map with hi When receiving 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, in which 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.

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 actuatable operator interface mechanisms can also be actuated in a 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 may be implemented by hardware elements, such as processors, memory, or other processing components, some of which are described below, that are associated with such systems, components, logic, or perform functions related to interactions. In addition, any or all of the systems, components, logic, and interactions may be implemented by software loaded into memory and then processed by a processor or server or other computing device components are executed 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.

6 12 is a block diagram of agricultural harvester 600 used 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 6 example shown are some elements of some of the in 2 items shown are similar and those items are numbered similarly. 6 FIG. 12 specifically shows that the predictive map generator 212 may be located at a server location 502 that is remote from the agricultural harvester 600. FIG. Therefore engages in the in 6 For example, as shown, agricultural harvester 600 approaches systems via remote server location 502 .

6 also shows another example of a remote server architecture. 6 shows that some elements of 2 may be located at a remote server location 502 while others may be located elsewhere. 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. If the combine 600 is in proximity to the machine containing the information collection system, such as a tanker truck, prior to refueling, the information collection system collects the information from the combine 600 via any ad hoc wireless connection. The information collected may then be forwarded 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 parts thereof arranged on a variety of different devices can't be. 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).

7 16 is a simplified block diagram of an illustrative example of a portable or mobile computing device that may be used as a user's or customer's portable terminal 16 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 8-9 are examples of portable or mobile devices.

7 provides a general block diagram of the components of a terminal device 16, including some of the 2 components shown that 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.

8th FIG. 12 shows an example in which the terminal 16 is a tablet computer 600. FIG. In 8th the computer 600 is shown with the user interface screen 602 . 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 600 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 600 may also illustratively receive voice input.

9 is similar to that 8th 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.

10 is an example of a computing environment in which elements of 2 can be used. With reference to 10 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 10 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 communication 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. By way of example and not by way of limitation 10 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 10 illustrates a hard disk drive 841, by way of example only, reading from or writing to non-removable, non-volatile magnetic media, an optical disk drive 855, and a non-volatile optical disk 856. The hard disk drive 841 is typically connected to the Sys System bus 821 and optical disk drive 855 are 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 10 , provide storage for computer 810 of computer-readable instructions, data structures, program modules, and other data. In 10 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 may be either the same as or different from the operating system 834, application programs 835, other program modules 836, and 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 may also include other peripheral output devices, such as speakers 897 and printer 896, which may 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. 10 FIG. 12 shows, for example, that remote application programs 885 can reside on remote computer 880. FIG.

It should also be noted that the various examples described herein can be combined in different 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 Vorabinformationskarte empfängt, die Werte eines landwirtschaftlichen Merkmals entsprechend unterschiedlichen geografischen Orten in einem Feld beinhaltet;
• einen geografischen Positionssensor, der eine geografische Position der landwirtschaftlichen Arbeitsmaschine erkennt;
• einen In-situ-Sensor, der einen Wert von Erntegutfeuchtigkeit erkennt, der der geografischen Position entspricht;
• einen prädiktiven Modellgenerator, der ein prädiktives landwirtschaftliches Modell erzeugt, das eine Beziehung zwischen dem landwirtschaftlichen Merkmal und der Erntegutfeuchtigkeit auf Grundlage eines Werts des landwirtschaftlichen Merkmals in der Vorabinformationskarte an der geografischen Position und des Werts der Erntegutfeuchtigkeit modelliert, der durch den In-situ-Sensor erfasst wird, der der geografischen Position entspricht; und
• einen prädiktiven Kartengenerator, der eine funktionelle prädiktive landwirtschaftliche Karte des Feldes erzeugt, die prädiktive Werte von Erntegutfeuchtigkeit auf Grundlage der Werte des landwirtschaftlichen Merkmals in der Vorabinformationskarte und auf Grundlage des prädiktiven landwirtschaftlichen Modells auf die verschiedenen geografischen Positionen in dem Feld abbildet.

Another example is an example that includes any or all of the preceding examples, including:

• a communication system that receives a preliminary information map that includes values of an agricultural feature corresponding to different 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 crop moisture that corresponds to the geographic location;
• a predictive model generator that generates a predictive agricultural model that models a relationship between the agricultural trait and the crop moisture based on a value of the agricultural trait in the advance information map at the geographic location and the value of the crop moisture detected by the in situ sensor is recorded that corresponds to the geographical position; and
• a predictive map generator that generates a functional predictive agricultural map of the field that maps predictive values of crop moisture to the various geographic locations in the field based on the values of the agricultural trait in the preliminary information map and based on the predictive agricultural model.

Another example is an example that includes any or all of the preceding examples, wherein the predictive map generator is the functional configured predictive agricultural map for consumption by a control system that generates control signals to control a controllable subsystem on the agricultural work machine based on the functional predictive agricultural map.

Another example is an example that includes any or all of the preceding examples, wherein the control signals control the controllable subsystem to adjust a rate of advancement of material through the agricultural work machine.

Another example is an example that includes any or all of the preceding examples, wherein the preliminary information map includes a previous vegetative index map that includes as agricultural characteristic values vegetative index values corresponding to various geographic locations in the field.

Another example is an example that includes any or all of the preceding examples, wherein the predictive model generator is configured to generate a relationship between vegetative index values and crop moisture based on the crop moisture value sensed by the in situ sensor that is the geographic location and a vegetative index value corresponds to identify in the vegetative index map at the geographic location, wherein the predictive agricultural model is configured to receive a vegetative index value as a model input and a predictive crop moisture value as a model output based on the identified relationship to create

Another example is an example that includes any or all of the preceding examples, wherein the preliminary information map includes a historical crop moisture map that includes as agricultural characteristic values historical crop moisture values corresponding to various geographic locations in the field.

Another example is an example that includes any or all of the preceding examples, wherein the predictive model generator is configured to generate a relationship between historical crop moisture values and crop moisture based on the crop moisture value detected by the in situ sensor. corresponding to the geographic location, and a historical crop moisture value in the historical crop moisture map at the geographic location, wherein the predictive agricultural model is configured to receive a historical crop moisture value as a model input and a predictive crop moisture value as a generate model output based on the identified relationship.

Another example is an example that includes any or all of the preceding examples, wherein the prior information map includes a topographic map including as agricultural feature values topographic feature values corresponding to different geographic locations in the field.

Another example is an example that includes any or all of the preceding examples, wherein the predictive model generator is configured to generate a relationship between the topographical feature and crop moisture based on the value of crop moisture detected by the in situ sensor. corresponding to the geographic location, and a value of the topographical feature in the topographical map at the geographic location, wherein the predictive agricultural model is configured to receive a value of the topographical feature as a model input and a predictive value of crop moisture as a generate model output based on the identified relationship.

Another example is an example that includes any or all of the preceding examples, wherein the prior information map includes a soil property map that includes as the agricultural feature values values of a soil property that correspond to different geographic locations in the field.

Another example is an example that includes any or all of the preceding examples, wherein the predictive model generator is configured to generate a relationship between soil property and crop moisture based on the value of crop moisture detected by the in situ sensor that is the corresponds to geographic location, and to identify a value of the soil property in the soil property map at the geographic location, wherein the predictive agricultural model is configured to receive a value of the soil property as a model input and a predictive value of the crop moisture as a model output based on the identified to create relationship.

• Empfangen einer Vorabinformationskarte in einer landwirtschaftlichen Arbeitsmaschine, die Werte eines landwirtschaftlichen Merkmals angibt, die verschiedenen geografischen Positionen in einem Feld entsprechen;
• Erkennen einer geografischen Position der landwirtschaftlichen Arbeitsmaschine;
• Erkennen eines Werts der Erntegutfeuchtigkeit mit einem In-situ-Sensor, der der geografischen Position entspricht;
• Erzeugen eines prädiktiven landwirtschaftlichen Modells, das eine Beziehung zwischen dem landwirtschaftlichen Merkmal und der Erntegutfeuchtigkeit modelliert; und
• Steuern eines prädiktiven Kartengenerators, um die funktionelle prädiktive landwirtschaftliche Karte des Feldes zu erzeugen, die prädiktive Werte von Erntegutfeuchtigkeit auf Grundlage der Werte des landwirtschaftlichen Merkmals in der Vorabinformationskarte und des prädiktiven landwirtschaftlichen Modells auf die verschiedenen Positionen in dem Feld abbildet.

Another example is an example that includes any or all of the preceding examples, including:

• receiving a preliminary information card in an agricultural working machine, the values of an agricultural characteristic indicates that correspond to different geographic locations in a field;
• detecting a geographic location of the agricultural work machine;
• detecting a crop moisture value with an in situ sensor that corresponds to the geographic location;
• generating a predictive agricultural model that models a relationship between the agricultural trait and crop moisture; and
• Controlling a predictive map generator to generate the functional predictive agricultural map of the field mapping predictive values of crop moisture to the various locations in the field based on the values of the agricultural characteristic in the advance information map and the predictive agricultural model.

• Konfigurieren der funktionellen prädiktiven landwirtschaftlichen Karte für ein Steuersystem, das Steuersignale erzeugt, um ein steuerbares Teilsystem auf der landwirtschaftlichen Arbeitsmaschine auf Grundlage der funktionellen prädiktiven landwirtschaftlichen Karte zu steuern.
• Ein weiteres Beispiel ist ein Beispiel, das eines oder alle vorhergehenden Beispiele beinhaltet, wobei das Empfangen der Vorabinformationskarte das Empfangen einer früheren vegetativen Indexkarte umfasst, die als Werte des landwirtschaftlichen Merkmals vegetative Indexwerte beinhaltet, die den verschiedenen geografischen Positionen in dem Feld entsprechen.

Another example is an example that includes any or all of the preceding examples and further includes:

• configuring the functional predictive agricultural map for a control system that generates control signals to control a controllable subsystem on the agricultural work machine based on the functional predictive agricultural map.
• Another example is an example that includes any or all of the preceding examples, wherein receiving the preliminary information map includes receiving a previous vegetative index map that includes as agricultural feature values vegetative index values corresponding to different geographic locations in the field.

• Identifizieren einer Beziehung zwischen den vegetativen Indexwerten und Erntegutfeuchtigkeit auf Grundlage des erkannten Werts der Erntegutfeuchtigkeit, die der geografischen Position entspricht, und eines vegetativen Indexwerts in der vegetativen Indexkarte an der geografischen Position; und
• Steuern eines prädiktiven Modellgenerators, um das prädiktive landwirtschaftliche Modell zu erzeugen, das einen vegetativen Indexwert als eine Modelleingabe empfängt und einen prädiktiven Wert von Erntegutfeuchtigkeit als eine Modellausgabe auf Grundlage der identifizierten Beziehung erzeugt.

Another example is any or all of the preceding examples, wherein generating a predictive agricultural model includes:

• identifying a relationship between the vegetative index values and crop moisture based on the recognized crop moisture value corresponding to the geographic location and a vegetative index value in the vegetative index map at the geographic location; and
• Controlling a predictive model generator to generate the predictive agricultural model that receives a vegetative index value as a model input and generates a predictive value of crop moisture as a model output based on the identified relationship.

Another example is an example that includes any or all of the preceding examples, wherein receiving the preliminary information map comprises receiving a historical crop moisture map that includes as the agricultural characteristic values historical crop moisture values corresponding to different geographic locations in the field.

• Identifizieren einer Beziehung zwischen der historischen Erntegutfeuchtigkeit und der Erntegutfeuchtigkeit auf Grundlage des Werts der Erntegutfeuchtigkeit, die durch den In-situ-Sensor erkannt wird, der der geografischen Position und einem historischen Wert der Erntegutfeuchtigkeit entspricht, in der historischen Erntegutfeuchtigkeitskarte an der geografischen Position; und
• Steuern eines prädiktiven Modellgenerators, um das prädiktive landwirtschaftliche Modell zu erzeugen, das einen historischen Wert von Erntegutfeuchtigkeit als eine Modelleingabe empfängt und einen prädiktiven Wert von Erntegutfeuchtigkeit als eine Modellausgabe auf Grundlage der identifizierten Beziehung erzeugt.

Another example is any or all of the preceding examples, wherein generating a predictive agricultural model includes:

• identifying a relationship between the historical crop moisture and the crop moisture based on the value of the crop moisture detected by the in situ sensor corresponding to the geographic location and a historical value of the crop moisture in the historical crop moisture map at the geographic location; and
• Controlling a predictive model generator to generate the predictive agricultural model that receives a historical value of crop moisture as a model input and generates a predicted value of crop moisture as a model output based on the identified relationship.

• Steuern eines Bedienerschnittstellenmechanismus, um die funktionelle prädiktive landwirtschaftliche Karte darzustellen.

Another example is an example that includes any or all of the preceding examples, further comprising:

• Controlling an operator interface mechanism to present the functional predictive agricultural map.

• ein Kommunikationssystem, das eine vorherige Karte empfängt, die Werte eines landwirtschaftlichen Merkmals angibt, die unterschiedlichen geografischen Positionen in einem Feld entsprechen;
• einen geografischen Positionssensor, der eine geografische Position der landwirtschaftlichen Arbeitsmaschine erkennt;
• einen In-situ-Sensor, der einen Wert von Erntegutfeuchtigkeit erkennt, der der geografischen Position entspricht;
• einen prädiktiven Modellgenerator, der ein prädiktives Modell für Erntegutfeuchtigkeit erzeugt, das eine Beziehung zwischen dem landwirtschaftlichen Merkmal und der Erntegutfeuchtigkeit auf Grundlage eines Werts des landwirtschaftlichen Merkmals in der vorherigen Karte an der geografischen Position und des Werts der Erntegutfeuchtigkeit modelliert, der durch den In-situ-Sensor erkannt wird, der der geografischen Position entspricht; und
• einen prädiktiven Kartengenerator, der eine funktionelle prädiktive Erntegutfeuchtigkeitskarte des Feldes erzeugt, die prädiktive Werte der Erntegutfeuchtigkeit auf Grundlage der landwirtschaftlichen Merkmalswerte in der vorherigen Karte und auf Grundlage des prädiktiven Erntegutfeuchtigkeitsmodells auf die verschiedenen Positionen in dem Feld abbildet.

Another example is an example that includes any or all of the preceding examples, including:

• a communication system that receives a previous map indicative of values of an agricultural characteristic corresponding to different geographic positions 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 crop moisture that corresponds to the geographic location;
• a predictive model generator that generates a crop moisture predictive model representing a relationship between the agricultural trait and the crop moisture based on a value of the agricultural trait in the previous map modeled at the geographic location and the value of crop moisture detected by the in situ sensor corresponding to the geographic location; and
• a predictive map generator that generates a functional predictive crop moisture map of the field that maps predictive crop moisture values to the various locations in the field based on the agricultural characteristic values in the previous map and based on the predictive crop moisture model.

• eine vegetative Indexkarte, die als Werte des landwirtschaftlichen Merkmals vegetative Indexwerte angibt, die den verschiedenen geografischen Positionen in dem Feld entsprechen;
• eine historische Erntefeuchtigkeitskarte, die als Werte des landwirtschaftlichen Merkmals historische Werte der Erntefeuchtigkeit angibt, die den verschiedenen geografischen Positionen in dem Feld entsprechen;
• eine topographische Karte, die als Werte des landwirtschaftlichen Merkmals Werte eines topographischen Merkmals angibt, die den verschiedenen geografischen Positionen in dem Feld entsprechen, oder
• eine Bodeneigenschaftskarte, die als Werte des landwirtschaftlichen Merkmals Werte einer Bodeneigenschaft angibt, die den verschiedenen geografischen Positionen in dem Feld entsprechen.

Another example is an example that includes any or all of the preceding examples, where the previous card includes one or more of the following:

• a vegetative index map indicating, as agricultural characteristic values, vegetative index values corresponding to different geographic locations in the field;
• a historical crop moisture map indicating, as agricultural characteristic values, historical values of crop moisture corresponding to different geographic locations in the field;
• a topographical map giving as agricultural feature values values of a topographical feature corresponding to different geographic positions in the field, or
• a soil property map giving as agricultural characteristic values values of a soil property corresponding to different geographic locations in the field.

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 preliminary information map that includes values of an agricultural characteristic that correspond to different geographic positions 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 crop moisture corresponding to the geographic location; a predictive model generator (210) that generates a predictive agricultural model that models a relationship between the agricultural feature and the crop moisture based on a value of the agricultural feature in the advance information map at the geographic position and the value of the crop moisture generated by the domestic situ sensor corresponding to geographic position is detected; and a predictive map generator (212) that generates a functional predictive agricultural map of the field mapping predictive values of crop moisture to the various geographic locations in the field based on the values of the agricultural feature in the preliminary information map and based on the predictive agricultural model. Agricultural working machine after claim 1 , wherein the predictive map generator configures the functional predictive agricultural map for consumption by a control system that generates control signals to control a controllable subsystem on the agricultural work machine based on the functional predictive agricultural map. Agricultural working machine after claim 2 , wherein the control signals control the controllable subsystem to adjust a rate of advancement of material through the agricultural work machine. Agricultural working machine after claim 1 wherein the preliminary information map comprises a previous vegetative index map comprising as the agricultural characteristic values vegetative index values corresponding to the different geographic positions in the field. Agricultural working machine after claim 4 , wherein the predictive model generator is configured to generate a relationship between vegetative index values and crop moisture based on the value of crop moisture detected by the in situ sensor corresponding to the geographical position and a vegetative index value in the vegetative index map at the identify a geographic location, wherein the predictive agricultural model is configured to receive a vegetative index value as a model input and to generate a predictive value of crop moisture as a model output based on the identified relationship Agricultural working machine after claim 1 wherein the preliminary information map comprises a historical crop moisture map comprising as agricultural characteristic values historical crop moisture values corresponding to different geographic locations in the field. Agricultural working machine after claim 6 , wherein the predictive model generator is configured to generate a relationship between historical crop moisture values and crop moisture based on the crop moisture value detected by the in situ sensor corresponding to the geographic location and a historical crop moisture value in the identify a historical crop moisture map at the geographic location, wherein the predictive agricultural model is configured to receive a historical crop moisture value as a model input and to generate a predictive crop moisture value as a model output based on the identified relationship. Agricultural working machine after claim 1 wherein the prior information map comprises a topographical map comprising as agricultural feature values values of a topographical feature corresponding to the different geographic positions in the field. Agricultural working machine after claim 8 , wherein the predictive model generator is configured to generate a relationship between the topographical feature and the crop moisture based on the value of the crop moisture detected by the in situ sensor corresponding to the geographic location and a value of the topographical feature in identify the topographic map at the geographic location, wherein the predictive agricultural model is configured to receive a value of the topographic feature as a model input and to generate a predicted value of crop moisture as a model output based on the identified relationship. Agricultural working machine after claim 1 wherein the prior information map comprises a soil property map comprising as agricultural feature values values of a soil property corresponding to the different geographic positions in the field. Agricultural working machine after claim 10 , wherein the predictive model generator is configured to generate a relationship between the soil property and crop moisture based on the value of the crop moisture detected by the in situ sensor corresponding to the geographic location and a value of the soil property in the soil property map at the identify a geographic location, wherein the predictive agricultural model is configured to receive a soil property value as a model input and to generate a predictive crop moisture value as a model output based on the identified relationship. A computer-implemented method for generating a functional predictive agricultural map, comprising: receiving at an agricultural work machine (100) a preliminary information map (258) containing values of an agricultural characteristic corresponding to different geographic locations in a field; detecting a geographic position of the agricultural work machine (100); detecting a value of crop moisture with an in situ sensor (208) that corresponds to the geographic location; generating a predictive agricultural model that models a relationship between the agricultural trait and crop moisture; and controlling a predictive map generator (212) to generate the functional predictive agricultural map of the field mapping predictive values of crop moisture to the various locations in the field based on the values of the agricultural characteristic in the preliminary information map and the predictive agricultural model. Computer-implemented method claim 12 , further comprising: configuring the functional predictive agricultural map for a control system that generates control signals to control a controllable subsystem on the agricultural work machine based on the functional predictive agricultural map. Computer-implemented method claim 12 wherein receiving the preliminary information map comprises receiving a previous vegetative index map including as agricultural characteristic values vegetative index values corresponding to different geographic locations in the field. An agricultural work machine (100) comprising: a communication system (206) receiving a previous map indicative of values of an agricultural feature representing various geographic corresponding positions 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 crop moisture corresponding to the geographic location; a predictive model generator (210) that generates a predictive crop moisture model that models a relationship between the agricultural feature and the crop moisture based on a value of the agricultural feature in the previous map at the geographic location and the value of the crop moisture generated by the in- situ sensor corresponding to geographic position is detected; and a predictive map generator (212) that generates a functional predictive crop moisture map of the field that maps predictive crop moisture values to the various locations in the field based on the agricultural feature values in the previous map and based on the predictive crop moisture model.

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