DE102021124392A1 - MACHINE CONTROL USING A PREDICTIVE MAP - Google Patents

Abstract

One or more information cards are obtained from an agricultural work machine. The one or more information maps depict one or more agricultural characteristic values at different geographic locations of a field. An in situ sensor on the agricultural work machine senses an agricultural feature as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural 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

This description relates to agricultural machinery.

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 change harvester controls when such conditions occur during the 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 of a predictive model generator and a predictive map generator.
• 7 FIG. 14 is a flowchart illustrating an example of the operation of an agricultural harvester in receiving a preliminary information map and recognizing in situ sensor input in generating a functional predictive map.
• 8th Figure 12 is a block diagram showing an example of in situ sensor(s).
• 9 Figure 12 is a block diagram showing an example of a control zone generator.
• 10 is a flowchart showing an example of the operation of the in 8th control zone generator shown.
• 11 12 is a flow chart showing an example of operation of a control system in selecting a target setting value to control the agricultural harvester.
• 12 Fig. 12 is a block diagram showing an example of an operator interface control.
• 13 Figure 12 is a flow chart illustrating an example operator interface control.
• 14 Figure 12 is a pictorial representation showing an example operator interface display.
• 15 Figure 12 is a block diagram showing an example agricultural harvester in communication with a remote server environment.
• the 16-18 show examples of mobile devices that can be used in an agricultural harvesting machine.
• 19 Figure 12 is a block diagram showing an example of a computing environment used in an agricultural harvester can be, 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. All alterations and further modifications to the described devices, systems, methods, and any further application of the principles of the present disclosure are contemplated fully as would normally occur to those skilled in the art to which the disclosure pertains. In particular, it is fully contemplated that the features, components, steps, or combination thereof, described with respect to one example may be combined with the features, components, steps, or combination thereof, described with respect to other examples present disclosure.

The present description relates to the use of in situ data collected concurrently with a farming operation in combination with data provided by a map to generate a functional predictive map and more particularly a functional predictive corn cob size map. In some examples, the functional predictive corn cob size 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 different cob size unless the machine settings are also changed. For example, if the deck plates on the header of the agricultural harvester are improperly spaced, the ear of corn, or a portion of the ear of corn, may move through the gap defined by the spacing of the deck plates, resulting in grain loss through contact with the stalk rollers. positioned under the cover plates.

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 historical yield map illustratively depicts yield values across various geographic locations in one or more fields of interest. These historical yield maps are collected from previous harvesting operations on the field(s). A yield map can show yield in units of earnings value. An example of an earned value unit includes dry bushels per hectare. In some examples, a historical yield map may be derived from sensor readings from one or more yield sensors. Without limitation, these yield sensors may include, but are not limited to, gamma ray attenuation sensors, impactor sensors, load cells, cameras or other optical sensors, and ultrasonic sensors.

A seeding map illustratively depicts seeding characteristics across various geographic locations in a field of interest. These seed charts are typically collected from previous seed planting operations in the field. In some examples, the seeding map may be derived from control signals used by a seeder when the seed is planted, or from sensors on the seeder, such as sensors that confirm that a seed has been placed in a furrow generated by the Seeder is generated. Seeders may include geographic position sensors that Geolocate positions where the seed was planted and topographic sensors that generate topographic information of the field. The information generated during a previous seed planting operation can be used to determine various seeding characteristics, such as location (e.g., the geographic position of the planted seed in the field), spacing (e.g., both the spacing between individual seeds, spacing between and seed rows, or both), population (which can be inferred from spacing characteristics), orientation (e.g. seed orientation in the trench as well as or orientation of seed rows), depth (e.g. seed depth or furrow depth), dimensions (such as seed size) or genotype (such as seed species, seed hybrids, seed varieties, etc.). A variety of other seed characteristics can also be determined.

Alternatively or in addition to data from a previous farm, various seed characteristics may be generated on the seed maps based on data from third parties, such as third-party seed suppliers that provide the seed to the seed planter. These third parties may provide various data indicating various seed traits, for example dimensional data such as seed size, or genotype data such as seed types, seed hybrids or seed cultivars. In addition, seed providers may provide various data related to certain plant traits of the resulting plants of each different seed genotype. For example, plant growth data such as stem diameter, corn cob size, plant height, plant mass, etc., plant response to weather conditions, plant response to applied substances such as herbicides, fungicides, pesticides, insecticides, fertilizers, etc., plant response to pests, fungi, weeds, diseases, etc ., as well as any number of other plant characteristics.

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

The present discussion proceeds with respect to systems that receive a map, such as a preliminary information map, a map generated based on a previous operation, or a predictive map (e.g., a predictive yield map). The systems also use an in situ sensor to detect a variable indicative of one or more traits, such as an agricultural trait. An agricultural trait is any trait that can affect an agricultural process, such as a harvesting process. In one example, one or more in situ sensors detect one or more variables indicative of a size of cobs of vegetation, such as a diameter or other dimension representing a cross-sectional size of the cob (collectively referred to herein as "diameter"). , length or weight of crop corn cobs, for example one or more corn cob size sensors sensing diameter, length or weight of corn ears. However, it should be noted that the in situ sensor can detect a variable indicative of any number of other agricultural characteristics and is not limited to the characteristics described herein. The systems generate a model that models a relationship between the values on the received map and the output values of the in situ sensor. The model is used to generate a functional predictive map that predicts, for example, a corn cob size, an agricultural trait, or an operator command input at various areas of the field based on the values from the received map at those locations. The functional predictive 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. The functional predictive map can be used to control one or more of the controllable subsystems on the agricultural harvesting machine. For example, a deck plate position controller that generates control signals to control a machine actuator subsystem to adjust a position or spacing of deck plates on the agricultural harvesting machine.

Cover plates, also referred to as stripper plates, are included on row units of agricultural harvester headers, such as corn headers. Generally, a left and right cover plate is included on each row unit. Each cover panel has an inner edge and the inner edges of the left and right cover panels are spaced from each other. The spacing between the left and right cover plates defines a gap that accommodates vegetation such as corn plants. For example, the gap may taper from the rear (closer to the rear of the agricultural harvester) to the front (where the stalk enters) such that the spacing between the front of the deck plates is less than the spacing at the rear of the deck plates. When the agricultural harvester moved across a field, the gap defined by the spacing of the deck plates accommodates corn stalks of an aligned row of corn plants as the row unit moves along the row. As the row unit is moved along the row, the corn stalks are pulled through the nip by means of gathering chains (usually above the deck plates) or stalk rollers, sometimes called stalk rollers, (usually below the deck plates), or both, on the row unit so that the corn stalks are removed from the stalk carried corn cobs hit the top plates and are separated from the stalk. The severed ears of corn continue to be conveyed through the agricultural harvester, while the severed stalk material remains in the field, where the stalk material remains or is later collected, for example as part of a stalk collection process.

Having the correct adjustments, such as position and spacing, of the deck plates on the agricultural harvester is important to reduce losses such as grain loss from the header due to grain popping when the corn cob moves through the gap and touches the stalk rollers or by corn cob fling , and to reduce the ingestion of material other than grain (MOG). In field conditions where cob size can vary significantly, deck plate position and spacing can have a significant impact on performance. For example, if the spacing between the deck plates is too wide, grain popping (i.e., popping off or removing corn kernels from a cob when the butt end of the cob contacts the stalk rollers) can occur, resulting in grain loss from the header by causing popped grain Corn kernels are left on the field. If the distance between the top plates is too narrow, the stalks may be snapped off too early and the agricultural harvester will take up too much MOG by feeding MOG into the harvester along with the cob of corn, overloading the separator and separating grain from MOG on the sieves resulting in grain loss from the rear of the agricultural harvester when the residue is ejected. As harvesting speeds increase and headers become larger, agricultural harvester performance can be adversely affected if deck plate position and spacing are not adjusted properly and in a timely manner.

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 haul rate as a mass flow rate of grain through the elevator 130 or through other portions of the agricultural harvesting machine 100, or provide other output signals indicative of other sensed quantities. The crop characteristic sensors may include one or more yield sensors that sense the crop yield being harvested by the agricultural harvesting machine.

The yield sensor(s) may include a grain flow sensor that detects a flow of crop material, such as grain, in material handling subsystem 125 or other portions of agricultural harvesting machine 100 . For example, a yield sensor may include a gamma ray attenuation sensor that measures the flow rate of the harvested grain. In another example, a yield sensor includes an impact plate sensor that senses the impact of grain on a sensor plate or surface to measure the mass flow rate of the grain being harvested. In another example, a yield sensor includes one or more load cells that measure or detect a load or mass of harvested grain. For example, one or more load cells may be located at a bottom of the grain tank 132, with changes in the weight or mass of grain within the grain tank 132 during a measurement interval being indicative of total yield. The measurement interval can be extended for averaging or decreased for instantaneous measurements. In another example, a yield sensor includes cameras or optical sensing devices that detect the size or shape of an aggregated mass of harvested grain, such as the shape of the hill or the height of a hill of grain in the grain tank 132. The change in shape or height of the Hill during the measurement interval indicates a total yield during the measurement interval. In other examples, other yield collection technologies are used. For example, in one example, a yield sensor includes two or more of the sensors described above, and yield for a measurement interval is determined from signals output from each of the multiple different types of sensors. For example, yield is determined based on signals from a gamma ray attenuation sensor, an impact plate sensor, load cells in grain tank 132 and optical sensors along grain tank 132 .

The crop characteristic sensors may also include one or more cob size sensors that sense a size, such as diameter, length, or weight, of cobs of vegetation, such as ears of corn in the field.

Corn cob size sensors may be sensors configured to detect an impact or a result of an impact (e.g., displacement of the top plate(s)) of the corn cob against the top plates. Corn cob size sensors may include accelerometers, strain gauge sensors, and any number of other sensors configured to detect impact between the corn cob and the cover plates. In other examples, the corn cob size sensor(s) may be an optical sensor, such as a camera or other optical sensing device (e.g., radar, lidar, sonar, etc.) that images vegetation around the agricultural harvester detected. The images, which contain corncob information, can be processed using a variety of image processing techniques to derive the corncob sizes of vegetation around the agricultural harvester. These and various other corn cob size sensors may be used to provide in situ indications of corn cob sizes in the field in which agricultural harvester 100 is operating. It should be understood that these are just a few examples of corn cob size sensors, and those skilled in the art will recognize that various other corn cob size sensors may be used without departing from the spirit and scope of the disclosure. In some examples, the agricultural harvester may include one or more corn cob size sensors, such as a corn cob size sensor for each row unit on the header 102 of the agricultural harvester 100 . In some examples, the agricultural harvester may include one or more different types of corn cob size sensors.

Before describing how the agricultural harvesting machine 100 generates a functional predictive map and uses the functional predictive map for display or control, a brief description of some elements of the agricultural harvesting machine 100 and their operation is first 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. Characteristics of the field can include characteristics of a field, such as slope, weed intensity, weed type, soil moisture, surface quality; characteristics of crop properties, such as crop height, crop moisture, crop density, corn cob size, crop condition; Characteristics of grain properties, such as grain moisture, grain size, grain test weight; and machine performance characteristics such as loss levels, job quality, fuel consumption, and power utilization. A relationship between the feature values derived from in situ sensor signals and the advance information map values is identified and this relationship is used to generate a new functional predictive map. A functional predictive map predicts values at various geographic locations in a field, and one or more of these values can be used to control a machine, such as one or more subsystems of an agricultural harvester. In some cases, a functional predictive map may be presented to a user, such as an operator of an agricultural work machine, which may be an agricultural harvester. A functional predictive map may be presented to a user visually, such as via a display, haptically, or audibly. The user can interact with the functional predictive map to perform editing operations and other user interface operations. In some cases, a functional predictive map may be used for one or more of controls of an agricultural work machine, such as an agricultural harvester, presentation to an operator or other user, and presentation to an operator or user for interaction by the operator or user.

After the general approach regarding the 2 and 3 , a more specific approach to generating a functional predictive corn cob size map that can be presented to an operator or user, or used to control agricultural harvester 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 harvesting machine 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 features of a field simultaneously with a Record harvesting process. The in situ sensors 208 generate values corresponding to the sensed features. Agricultural harvester 100 also includes a predictive model or relationship generator (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. Some other examples of in situ sensors 208 are given in 8th shown. The predictive model generator 210 illustratively includes an information variable-to-in situ variable model generator 228 , and the predictive model generator 210 may include other elements 230 . The control system 214 includes the communication system control 229, the operator interface control 231, an adjustment control 232, the path planning control 234, the line speed control 236, the head and roll control 238, the draper belt control 240, the cover plate position control 242, the residue system control 244, the machine cleaning control 245, the zone control 247 and the control system 214 may include other elements 246 . The controllable subsystems 216 include machine and header actuators 248, the propulsion subsystem 250, the steering subsystem 252, the residue subsystem 138, the machine cleaning subsystem 254, and the controllable subsystems 216 may include a variety of other subsystems 256.

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

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

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

The in situ sensors 208 can be any of the sensors described herein. 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. Some examples of in situ sensors 208 are in 8th shown.

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 yield map can be selected from one of the past years where the weather conditions during the growing season were similar to the current weather conditions. Or, for example, a historical yield map from one of the last years can be selected if the context information is not similar. For example, a historical yield map can be selected for a previous year that was relatively "dry" while the current year is relatively "wet". There can still be a useful historical relationship, but the relationship can be the other way around. For example, areas of large cob size in a relatively wet year may be areas of small cob size in a dry year. 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 hold a set of dates 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 several past years.

The context information can also be used for correlations between areas with similar context properties, regardless of whether the geographic location corresponds to the same location on the prior information map 258 . For example, historical yield values from the area with similar soil types in other fields can be used as the preliminary information map 258 to create the predictive corn cob size map. For example, the contextual feature information associated with a different location may be applied to the location on the prior information map 258 that has similar feature information.

The predictive model generator 210 generates a model that indicates a relationship between the values sensed by the in situ sensor 208 and a feature mapped by the prior information map 258 onto the field. For example, if the prior information map 258 maps a vegetative index value to various locations in the field and the in situ sensor 208 detects a value indicative of corn cob size, then the prior information variable-to-in situ variable model generator 228 generates a predictive Corn cob size model that models the relationship between vegetative index scores and corn cob size scores. Then, the predictive map generator 212 uses the predictive corn cob size model generated by the predictive model generator 210 to generate a functional predictive corn cob size map representing the value of the corn cob size expected to be detected by the in situ sensors 208 at various Positions in the field based on the prior information map 258 are predicted. Or, for example, if prior information map 258 maps a historical yield value to various locations in the field and in situ sensor 208 detects a value indicative of corn cob size, then prior information variable-to-in situ variable model generator 228 generates a predictive cob size model that models the relationship between historical yield values (with or without contextual information) and in situ cob size values. Then, the predictive map generator 212 uses the predictive corn cob size model generated by the predictive model generator 210 to generate a functional predictive corn cob size map representing the value of the corn cob size expected by the in situ sensors 208 at various locations in the field based on the prior information map 258 predicted.

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 data type in functional predictive map 263 may be different than the data type in pre-information map 258, but has a relationship to the data type in pre-information map 258. For example, in some examples, the data type in pre-information map 258 may be in the functional predictive map 263 indicate the type of data in the functional predictive map 263 in some examples. In some examples, the data type in the functional predictive map 263 differs from one or both of the in situ data type sensed by the in situ sensors 208 and the data type in the advance information map 258. In some examples, the data type is in the functional predictive map 263 is the same as one or both of the in situ data type sensed by the in situ sensors 208 and the data type in the advance information map 258. In some examples, the data type in the functional predictive map 263 is the same as that of the In situ sensors 208 detected in situ data type or the data type in the advance information card 258 and is different from the other.

Continuing the previous vegetative index example, the predictive map generator 212 can use the vegetative index values in the advance information map 258 and the model generated by the predictive model generator 210 to generate a functional predictive map 263 that predicts the cob size 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 corncob size, 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 Corn cob size predicted at various locations across the field. The vegetative index value obtained from the preliminary information map 258 at those positions and the relationship between the vegetative index value and the cob size 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 corn cob size. The predictive map 264 may then be a predictive corn cob size map that maps predicted corn cob size values to various geographic locations in the field. In another example, the advance information map 258 may be a seed map and the variable sensed by the in situ sensors 208 may be corn cob size. The predictive map 264 may then be a predictive corn cob size map that maps predicted corn cob size 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 card 258 may be a vegetative index card, and the variable sensed by the in situ sensors 208 may be an operator command input indicative of a cover plate spacing adjustment. The predictive map 264 may then be a predictive corn cob size map that maps predicted corn cob size values to various geographic locations in the field. In another example, the preliminary information map 258 may be a vegetative index map and the variable sensed by the in situ sensors 208 may be a corn cob size. The predictive map 264 may then be a predictive cap spacing setting that maps predicted cap spacing settings 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 seed population map generated during planting and the variable sensed by the in situ sensors 208 may be corn cob size. The predictive map 264 may then be a predictive corn cob size map that maps predicted corn cob size values to various geographic locations in the field.

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 advance information map 258 may be a corn cob size map generated during a previous year and the variable sensed by the in situ sensors 208 may be corn cob size. The predictive map 264 may then be a predictive corn cob size map that maps predicted corn cob size values to various geographic locations in the field. In such an example, the relative corn cob size 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 representing a relationship between the relative corn cob size differences in the advance information map 258 and the corn cob size values sensed by in situ sensors 208 during the current harvesting operation. The predictive model is then used by the predictive map generator 212 to create a predictive corn cob size map. Predictive Map 264

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 be displayed to operator 260 and used to control or calibrate agricultural harvester 100, and in other examples, the control zones may be displayed to operator 260 or another user, or saved for later use.

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

Operator interface controller 231 is operable to generate control signals to control operator interface mechanisms 218 . Operator interface control 231 is also operable to present operator 260 with predictive map 264 or predictive control zone map 265 or other information derived from or based on predictive map 264, predictive control zone map 265, or both. The operator 260 can be a local operator or a remote operator. As an example, controller 231 generates control signals to control a display mechanism to display one or both of predictive map 264 and predictive control zone map 265 to operator 260 . Controller 231 may create operator actuable mechanisms that are displayed and operable by the operator to interact with the displayed map. The operator can edit the map, for example, by correcting a yield value displayed on the map based on the operator's observation. The adjustment controller 232 may generate control signals to adjust various adjustments to the agricultural harvesting machine 100 based on predictive map 264, 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, deck settings, such as deck gap or deck position, or both, concave gap, rotor settings, cleaning fan speed settings, the Header height, header functionality, reel speed, reel position, draper functionality (if agricultural harvester 100 is coupled to a draper header), corn head header functionality, internal distribution control, and other actuators 248 that affect the other functions of agricultural harvester 100 , to control. 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 that is above a selected threshold, feed rate controller 236 may reduce the speed of agricultural harvester 100 to maintain a constant feed rate of grain or biomass through the machine. The header and reel controller 238 may generate control signals to control a header or reel or other header functionality. The draper band controller 240 may generate control signals to control a draper band or other draper functionality based on the predictive map 264, the predictive control zone map 265, or both. Deck position controller 242 may generate control signals to control a position of a deck included in a header based on predictive map 264 or predictive control zone map 265, or both, and tailing system controller 244 may generate control signals to position a tailing subsystem 138 based on a predictive map 264 or a predictive control zone map 265 or both. The machine cleaning controller 245 may generate control signals to control the machine cleaning subsystem 254 . For example, based on the different types of seeds or weeds being passed through the agricultural harvesting machine 100, a particular type of machine cleaning operation or a frequency at which a cleaning operation is performed may be controlled. Other controls included in agricultural harvesting machine 100 may also control other subsystems based on predictive map 264 or predictive control zone map 265 or both.

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

At 280, agricultural harvester 100 receives the preliminary information card 258. Examples of the preliminary information card 258 or receiving the preliminary information card 258 are discussed with respect to blocks 282, 284, and 286. FIG. As discussed above, the preliminary information map 258 maps values of a variable corresponding to a first feature to various positions in the field, as indicated at block 282 . For example, the data may be collected based on aerial photographs or measured features taken during a previous year. The information may also be based on data detected in other ways (other than aerial photography). For example, in a previous year, an agricultural harvester 100 may have been fitted with a sensor that detected and geolocated a feature while the agricultural harvester 100 was driving through a field. The information may also be based on data detected in other ways (other than aerial photography). Data collected prior to the current harvesting operation, whether via aerial photography or otherwise, is indicated by block 284 . The preliminary information map 258 can be downloaded from the agricultural harvester 100 using the communication system 206 and stored in the data storage 202 . The preliminary information card 258 can also be loaded onto the agricultural harvester 100 in other ways using the communication system 206, and the loading of the preliminary information card 258 onto the agricultural harvester 100 is indicated by block 286 in the flow chart of FIG 3 . In some examples the advance information card 258 may be received by the 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, several years of historical yield maps may be received as candidate advance information maps. Each of these maps may include 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 types, etc. Contextual information may be used to select which historical yield map to select. For example, the weather conditions over a period of time, such as a current year, or the soil types for the current field can be compared to the weather conditions and soil types in the context information for each candidate advance information map. The results of such a comparison can be used to select which historical yield map to select. For example, years with similar weather patterns can generally produce similar yields or yield trends in a field. In some cases, years with opposite weather patterns can also be useful to predict corncob size based on historical yield. For example, an area with small cobs of corn in a dry year might have large cobs of corn in a wet year. 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 a corn cob size, 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 . Some examples of in situ sensors 208 are given in 8th shown. In some examples, data from onboard sensors is georeferenced using position heading or speed data from geographic position sensor 204 .

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

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

Note that in some examples, the preliminary information card 258 may include two or more different cards or two or more different card layers of a single card. Each map layer may represent a different type of data than the data type of another map layer, or the map layers may have the same type of data obtained at different points in time. Each map in the two or more different maps, or each layer in the two or more different map layers of a map, maps a different type of variable to the geographic locations in the field. In such an example, the predictive model generator 210 generates a predictive model that models the relationship between the in situ data and each of the various variables depicted by the two or more different maps or the two or more different map layers. Likewise, the in situ sensors 208 may include two or more sensors, each sensing a different type of variable. Thus, the predictive model generator 210 generates a predictive model that represents the relationships between each through the previous ing information card 258 and each variable type sensed by the in situ sensors 208 is modeled. 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 include one or more of the predictive values on predictive map 264 that are correlated to the geographic location, the control zones on the predictive Control zone map 265 correlated to geographic location and containing adjustment values or control parameters used based on the predicted values on predictive map 264 or the zones on predictive control zone map 265. In another example, the presentation may include more abstracted information or more detailed information. The representation may also include a confidence level that indicates an accuracy with which the predicted values on the predictive map 264 or the zones on the predictive control zone map 265 match measured values that can be measured by sensors on the agricultural harvesting machine 100 when moving the agricultural harvesting machine 100 through the field. Furthermore, an authentication/authorization system can be provided that implements authentication and authorization processes when information is presented at multiple locations. For example, there may be a hierarchy of people who are authorized to view and change maps and other presented information. For example, an onboard display device can display the maps locally on the machine in near real time, or the maps can also be generated at one or more remote locations. In some examples, each physical display device at each location may be associated with a person or user permission level. The user permission level can be used to determine what indicator marks are visible on the physical display device and what values that person can change. For example, a local operator of agricultural harvesting machine 100 may not be able to view the information corresponding to predictive map 264 or make changes to machine operation. However, a supervisor at a remote location can see the predictive map 264 on the display but not make any changes to it. A manager, who may be at a separate remote location, may be able to see all of the items on the predictive map 264 and also be able to change the predictive map 264 used in machine control . This is an example of an authorization hierarchy that can be implemented. The predictive map 264 or the predictive control zone map 265 or both can also be configured in other ways, as indicated by block 297 .

At block 298, inputs from the geographic position sensor 204 and other in situ sensors 208 are received by the control system. Block 300 represents receipt by the control system 214 of an input from the geographic position sensor 204 that includes a geographic Position of agricultural harvesting machine 100 identified. 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 corn cob size map may be used to control one or more controllable subsystems 216 . For example, the functional predictive corn cob size map may include corn cob size values georeferenced to locations within the field to be harvested. The functional predictive corn cob size map can be extracted and used to control the spacing or position of one or more sets of deck plates on the header 102 of the agricultural harvester 100 . The above example of deck plate spacing or deck plate position control using a functional predictive corn cob size map is provided by way of example only. Accordingly, a variety of other control signals may be generated using values obtained from a corn cob size predictive map or other type of functional predictive map 263 to control one or more of the controllable subsystems 216 .

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

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

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

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

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

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

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

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

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

When the harvesting process has been completed, the process proceeds from block 312 to block 330 where one or more of the predictive map 264, the predictive control zone map 265, and the predictive model generated by the predictive model generator 210 are stored. The predictive map 264, 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 the predictive model generator 210 and the predictive map generator 212 using a prior information map when generating a predictive model or receiving a functional predictive map, the predictive model generator 210 and the predictive map generator 212 in other examples in generating a predictive model and a functional predictive map, other types of maps including predictive maps such as a functional predictive map generated during the harvesting process may receive.

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 as a map one or more of a vegetative index map 332, a yield map 333 (such as a predictive yield map 335 or a historical yield map 337), a seeding map 399, or a pre-operation map 400. In some examples, the model generator 210 various other maps 401 receive. The predictive yield map 335 includes georeferenced predictive yield values. The predictive yield map 335 can be generated using an in 3 be generated according to the process described, wherein the preliminary information map includes a vegetative index map or a historical yield map and the in situ sensor includes a yield sensor. The predictive yield map 335 can also be generated in other ways. Historical yield map 337 includes historical yield values indicative of yield values across the field during a past harvest. The historical yield map 337 also includes contextual data indicative of the context or conditions that may have affected the yield value for the past year(s). For example, contextual data may include soil type, elevation, slope, planting date, harvest date, fertilizer application, seed type (e.g., hybrids, etc.), a measure of the presence of weeds, a measure of the presence of pests, weather conditions (e.g., rain, snow cover, hail, wind, temperature, etc.). The historical yield map 337 may also include other elements. [Add information to seeding card]

In addition to receiving one or more maps, the predictive model generator 210 also receives a geographic location 334 or an indication of a geographic location from the geographic location sensor 204. The in situ sensors 208 include, by way of example, a corn cob size sensor 336 and a processing system 338. In some examples, the corn cob size sensor 336 on board the agricultural harvester 100 . The processing system 338 processes sensor data generated by the corn cob size sensors 336 . Some other examples of in situ sensors 208 are also in 8th shown.

In some examples, the corn cob size sensor 336 may be an optical sensor on the agricultural harvester 100 . In some cases, the optical sensor can be a camera or other device that performs optical detection. Processing system 338 processes one or more images obtained via corn cob size sensor 336 to generate processed image data identifying one or more features of vegetation, such as crop plants, in the image. Vegetation features recognized by the processing system 338 may include size features of the cob plants, such as ears of corn. For example, processing system 338 may recognize a diameter, length, or weight of corn cobs included in an image.

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 from the in situ sensor 208 is present may not accurately represent the position of the value in the field. This is because there is a period of time between the time the agricultural harvesting machine makes initial contact with the feature and the time the feature is sensed by the in situ sensor 208, or vice versa, particularly in the case of a post forward looking optical corn cob size sensor, a period of time may elapse between the time the feature is sensed by the in situ sensor 208 and the time the agricultural harvester makes contact with the feature. Thus, a transition time between the occurrence of a feature and the detection of the feature by an in situ sensor 208 (or vice versa) is taken into account when georeferencing the detected data. This allows the feature value to be georeferenced to a precise position on the field.

For purposes of illustration, the yield values are in the context of yield values due to travel of separated crop along a header in a direction transverse to a direction of travel of the agricultural harvester, typically in a V-shape Area behind the agricultural harvester while the agricultural harvester is moving in a forward direction. The processing system 338 ranks a total yield sensed by a yield sensor during each time or measurement interval based on the travel times of the crop from different sections of the agricultural harvester, such as different lateral positions along a width of a header of the agricultural harvester, previous georeferenced regions to. For example, the processing system 338 maps a measured total yield from a measurement interval or time back to georeferenced areas traversed by a header of the agricultural harvesting machine during different measurement intervals or times. The processing system 338 allocates or splits the total yield from a particular measurement interval or time previously traversed into georeferenced areas that are part of the V-shaped area.

In other examples, the corn cob size sensor 336 may rely on various types of radiation and the manner in which radiation is reflected from, absorbed by, attenuated, or transmitted through vegetation. The corn cob size sensor 336 can sense other electromagnetic properties of grain and biomass, such as electrical permittivity, as the material passes between two capacitive plates. The cob size sensor 336 may also rely on mechanical properties of the vegetation, such as a signal generated when the cob impacts a piezoelectric element or when the impact is detected by a microphone or accelerometer. Other material properties and sensors can also be used. In some examples, raw or processed data from corn cob size 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. Cob size sensor 336 may include any other examples described herein, as well as any other sensor configured to generate a sensor signal indicative of a size of cobs of vegetation, such as ears of corn. In some examples, data from multiple sensors may be used to determine corn cob size and dimensions. For sizing a given ear of corn, an approach may be selected from a number of approaches based on whether the ear of corn is hulled, partially hulled or unhulled, diseased, damaged, or some other defining attribute.

The present discussion continues with respect to an example in which a corn cob size sensor 336 generates sensor signals indicative of a size characteristic, such as a diameter, length, or weight of corn cob plants, such as a diameter, length, or weight of corn ears. As in 4 As shown, the predictive model generator 210 includes a vegetative index-to-cob-size model generator 342, a yield-to-cob-size model generator 344, and a planting trait-to-cob-size model generator 346. In other examples, the predictive model generator 210 may include additional, fewer, or contain different components than those in the example of 4 shown. Accordingly, in some examples, the predictive model generator 210 may also include other elements 348, which may include other types of predictive model generators to generate other types of corn cob size models. For example, the predictive model generator 210 may include a prior operating trait-to-cob size model generator, for example, the prior operating trait may be a historical corn cob size detected during a previous harvesting operation, and thus the predictive model generator 210 may include a historical corn cob size-to-cob size model generator identifying a relationship between historical corn cob size values and a corn cob size value detected in situ.

The vegetative index-to-cob size model generator 342 identifies a relationship between in situ corn cob size data 340 at geographic locations corresponding to where in situ corn cob size data 340 was geolocated and vegetative index values from vegetative index map 332 corresponding to the same correspond to positions in the field where the corn cob size data 340 was geolocated. Based on this relationship established by the vegetative index to corn cob size model generator 342, the vegetative index to corn cob size model generator 342 generates a predictive corn cob size model. The corn cob size model is used by the predictive map generator 212 to predict corn cob size at various locations based on the georeferenced vegetative index values contained in the vegetative index map 332 at the same respective locations in the field.

The yield-to-cob size model generator 344 identifies a relationship between in situ corn cob size data 340 at geographic locations corresponding to that at which In situ corn cob size data 340 was geolocated and yield values from yield map 333 corresponding to the same locations in the field where the corn cob size data 340 was geolocated. Based on this relationship established by the yield-to-cob size model generator 344, the yield-to-cob size model generator 344 generates a predictive corn cob size model. The corn cob size model is used by the predictive map generator 212 to predict corn cob size at various locations based on the georeferenced yield values contained in the yield map 333 at the same respective locations in the field.

Seed feature to cob size model generator 346 identifies a relationship between in situ corn cob size data 340 at geographic locations corresponding to where in situ corn cob size data 340 was geolocated and seed feature values from seed map 339 corresponding to the same location on the field where the corn cob size data 340 was geolocated. Based on this relationship established by seed trait to cob size model generator 346, seed trait to cob size model generator 346 generates a predictive corn cob size model. The corn cob size model is used by the predictive map generator 212 to predict corn cob size at various locations based on the georeferenced seed feature values contained in the seed feature map 339 at the same respective locations in the field.

In other examples, the model generator 210 can include other model generators 348 . Based on the relationship established by the other model generators, the model generator generates a predictive corn cob size model. The cob size model is used by the predictive map generator 212 to predict a cob size at various locations based on the georeferenced feature value contained in a map at the same locations in the field.

In view of the foregoing, predictive model generator 210 is operable to generate a variety of predictive corn cob size models, such as one or more of the predictive corn cob size models generated by model generators 342, 344, 346, and 348. In another example, two or more of the predictive corn cob size models described above can be combined into a single predictive corn cob size model that predicts the corn cob size based on the vegetative index value, the seeding trait value, the pre-operation trait value, or the yield value at various locations in the field, or combinations thereof. Each of these corn cob size models, or combinations thereof, is collectively referred to as the corn cob size model 350 in 4 presents.

The predictive corn cob size model 350 is provided to the predictive map generator 212 . In the example of 4 predictive map generator 212 includes a corn cob size map generator 352. In other examples, predictive map generator 212 may include additional or different map generators. Accordingly, in some examples, the predictive map generator 212 may also include other elements 358, which may include other types of predictive map generators to generate other types of predictive maps. The corn cob size map generator 352 receives the predictive corn cob size model 350, which predicts the corn cob size based on in situ data 340 along with one or more of the vegetative index map 332, the yield map 333, the seeding map 399, the preliminary operation map 400 or other maps 401.

The corn cob size map generator 352 may generate a functional predictive corn cob size map 360 that values the corn cob size at various locations in the field based on the vegetative index value, yield value, seed trait value, pre-operation trait value, or other trait values at those locations in the field and the predictive corn cob size model 350 predicted. The generated functional predictive corn cob size map 360 may be provided to the control zone generator 213, the control system 214, or both, as shown in FIG 2 shown. The control zone generator 213 generates control zones and integrates those control zones to provide the functional predictive corn cob size map 360 with control zones. The functional predictive corn cob size map 360 (with or without control zones) may be presented to the operator 260 or other user or provided to the control system 214, which generates control signals to operate one or more of the controllable subsystems 216 based on the functional predictive corn cob size map 360 (with or without control zones) to control.

5 12 is a flowchart of an example of the operation of predictive model generator 210 and predictive map generator 212 in generating predictive cob size model 350 and functional predictive cob size map 360. At block 362, predictive model generator 210 and predictive map receive generator 212 one or more maps, such as one or more vegetative index maps 332, one or more yield maps 333, one or more sowing maps 399, one or more preliminary operation maps 400, or one or more other maps 401, or any combination thereof. In block 364 an in situ sensor signal from an in situ sensor 208 is received. As discussed above, the in situ sensor 208 may include a corn cob size sensor 336 that generates a sensor signal indicative of corn cob size characteristics, such as diameters, lengths, or weights of corn ears in the field. In some examples, the in situ sensor 208 can include various other sensors, as indicated by block 370 . Some other examples of other in situ sensors 208 are in 8th shown.

At block 372, the processing system 338 processes the one or more received sensor signals received from the cob size sensor 336 to generate a cob size value indicative of a cob size of vegetation in the field, such as a size of a corn ear.

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 based on machine delays (e.g., machine processing speed, sensor attributes, etc.) and machine speed, determine an accurate geographic location at which to associate the in situ sensed corn cob size. For example, the position of agricultural harvester 100 at the time a corn cob size sensor signal is sensed may not correspond to the precise position of the sensed corn cob (or the crop having the sensed corn cob) in the field. Thus, a position of the agricultural harvester 100 when the corn cob size sensor signal is received may not correspond to the position of the corn cob (or the plant with the corn cob).

At block 384, the predictive model generator 210 generates one or more predictive corn cob size models, such as the corn cob size model 350, that establish a relationship between at least one of a vegetative index value, a seeding trait value, a prior farm trait value, or a yield value derived from a map, such as the vegetative index map 332, seed map 399, pre-operation map 400, or yield map 333, and a corn cob size value detected by in situ sensor 208. For example, the predictive model generator 210 may generate a predictive corn cob size model based on a vegetative index value, a seeding trait value, a pre-operation trait value or a yield value and a detected corn cob size value indicated by the sensor signal obtained from the in situ sensor 208.

At block 386, the predictive corn cob size model, such as the predictive corn cob size model 350, is provided to the predictive map generator 212, which generates a functional predictive corn cob size map that has a predictive value of the corn cob size based on the vegetative index map, the seeding map, the pre-operation map or the yield map and the predictive corn cob size model 350 to different geographic locations in the field. For example, in some examples, the functional predictive corn cob size map 360 predicts corn cob size characteristics, such as a diameter, a length, or a weight or a value indicative of corn cob characteristics. In other examples, functional predictive corn cob size map 360 predicts other items, as indicated by block 392 . Further, the functional predictive corn cob size map 360 may be generated during the course of an agricultural harvesting operation. Thus, as an agricultural harvester moves through a field in which an agricultural harvest operation is being performed, the functional predictive corn cob size map 360 is generated.

At block 394 , the predictive map generator 212 outputs the functional predictive corn cob size map 360 . At block 393, the predictive map generator 212 configures the functional predictive corn cob size map 360 for consumption by the control system 214. At block 395, the predictive map generator 212 may also provide the functional predictive corn cob size map 360 to the control zone generator 213 for generation and integration of control zones. At block 397, the predictive map generator 212 configures the functional predictive corn cob size map 360 in other ways as well. The functional predictive corn cob size 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 corn cob size map 360 .

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 drive subsystem 250 Taxes. 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 other examples, the control system 214 may generate control signals to control a speed of the threshing rotor 112, may generate control signals to control a concave gap, or may generate control signals to adjust power output to some of the crop processing systems, such as the harvest chains or stalk rollers .

6 1 is a block diagram of an exemplary portion of an agricultural machine 100 shown in FIG 1 is shown. In particular shows 6 examples of predictive model generator 210 and predictive map generator 212, among others. In the illustrated example, preliminary information map 258 may be a vegetative index map 332, a seeding map 399, a yield map 333 (such as a historical yield map 337), or a preliminary operation map 400. The pre-operational map 400 may include feature values indicative of a feature at various locations in the field. The feature values can be feature values that were collected in the field during a preliminary operation, for example a preliminary operation by another agricultural working machine, for example a spray device. The advance notice card 258 may also include other advance notice cards 401, e.g. B. a pre-corn cob size map generated or otherwise provided in various ways. In one example, a pre-corn cob size map may be a historical corn cob size map generated based on data collected during a previous harvest operation, such as a previous harvest operation in a previous harvest season. 6 Also shows that predictive model generator 210 and predictive map generator 212 may receive, in addition to pre-information map 258, functional predictive corn cob size map 360 and yield map 333 (such as predictive yield map 335). The functional predictive corn cob size map 360 and the predictive yield map 335 can be used similarly to the advance information map 258 in that the model generator 210 models a relationship between information provided by the functional predictive corn cob size map 360 or the predictive yield map 335 and features provided by In -situ sensors 208 to generate a predictive model. The map generator 212 may use the predictive model generated by the model generator 210 to generate a functional predictive map that includes the features sensed by the in situ sensors 208 or a related feature at various locations in the field based on one or more of the values in the functional predictive corn cob size map 360 or the predictive yield map 335 at those positions in the field and based on the predictive model. In some examples, yield map 333 is a preliminary information map, such as yield map 337, or is a predictive yield map, such as predictive yield map 335. Predictive model generator 210 and predictive map generator 212 may also receive various other maps 401, such as other predictive ones Cob size maps generated in a manner different than functional predictive corn cob size map 360 .

in the in 6 In the example shown, the in situ sensor 208 includes one or more agricultural feature sensors 402, an operator input sensor 404, and a processing system 406. The in situ sensors 208 may include other sensors 408 as well. Some other examples of in situ sensors 208 are given in 8th shown. Agricultural feature sensors 402 may include one or more of the in situ sensors 208 described herein. Agricultural trait sensors 402 sense one or more variables indicative of an agricultural trait.

Operator input sensor 404 senses various operator inputs. The inputs may be adjustment inputs for controlling adjustments on the agricultural harvesting machine 100 or other control inputs such as steering inputs and other inputs. Thus, when an operator of agricultural harvesting machine 100, such as operator 260, changes a setting or provides a commanded input, such as via operator interface mechanism 218, such input is recognized by operator input sensor 404, which provides a sensor signal that senses that operator input indicates. For purposes of this disclosure, operator input may also be considered a feature, such as an agricultural feature, and thus an operator input may be an agricultural feature that is sensed by an in situ sensor 208 . Processing system 406 may receive one or more sensor signals from agricultural feature sensors 402 or operator input sensor 404, or both, and generate an output indicative of the recognized feature. For example, processing system 406 may receive sensor input from agricultural trait sensor 402 and generate an output indicative of an agricultural trait. Processing system 406 may also receive input from operator input sensor 404 and generate an output indicative of the sensed operator input.

The predictive model generator 210 may include a corncob size-to-agricultural trait model generator 416, a corncob-size-to-command model generator 422, and another trait-to-command model generator 423. In other examples, the predictive model generator 210 may include additional, fewer, or different model generators 424, such as specific agricultural trait model generators. Additionally, the other feature-to-instruction model generator 423 may include, as the other feature, vegetative index values provided by the vegetative index map 332, seeding feature values provided by the seeding map 399, pre-operation feature values provided by the pre-operation map 400, or Yield values provided by yield map 333. The predictive model generator 210 may receive a geographic position 334 or an indication of a geographic position from the geographic position sensor 204 and generate a predictive model 426 that models a relationship between the information in one or more of the maps and one or more agricultural features that are identified by a agricultural feature sensor 402, or one or more operator input commands sensed by operator input sensor 404, or both. For example, the corn cob size-to-agricultural trait model generator 416 creates a model that models a relationship between corn cob size values (which may be on or are indicated by one or more of the maps) and agricultural trait values obtained from agricultural trait sensors 402 are recorded. The cob size-to-command model generator 422 creates a model that models a relationship between cob size values (which may be on or indicated by one or more of the maps) and operator input commands sensed by the operator input sensor 404 . The other trait-to-command model generator 423 creates a model that models a relationship between other trait values (such as vegetative index values, pre-operation trait values, seeding trait values, or yield values) and operator input commands sensed by the operator input sensor, such as an operator input command that one deck plate spacing or a position setting for one or more sets of deck plates on the agricultural harvesting machine.

The predictive model 426 generated by the predictive model generator 210 may include one or more of the predictive models generated by the corncob size-to-agricultural trait model generator 416, the corncob-size-to-command model generator 422, the other trait-to-command model generator 423 and other model generators that may be included as part of other elements 424.

In the example of 6 For example, predictive map generator 212 includes agricultural feature predictive map generator 428 and operator command predictive map generator 432. In other examples, predictive map generator 212 may include additional, fewer, or different map generators 434. The predictive agricultural feature map generator 428 receives one or more of the maps and a predictive model 426 that is a relationship between one or more values provided by one or more of the maps and one or more agricultural feature values detected by agricultural feature sensors 402 , modeled (such as a predictive model generated by the corn cob size-to-agricultural feature model generator 416). The predictive agricultural trait map generator 428 generates a functional predictive agricultural trait map 436 that, at any position in the field, contains an agricultural trait at that position in the field based on a value, such as a corn cob size value, contained in one or more of the maps corresponding to that position on the field and predicted based on the predictive model 426 .

The predictive operator command map generator 432 receives one or more of the maps a predictive model 426 representing a relationship between one or more corn cob size values and one or more operator command inputs (such as a predictive model generated by the corn cob size-to-command model generator 422 ) or one or more other features and one or more operator command inputs (such as a predictive model, generated by the other feature-to-command model generator 423). The predictive operator command map generator 432 generates a functional predictive operator command map 440 that predicts operator commands at various locations based on one or more of the values in the one or more maps at those locations in the field and based on the predictive model 426. For example, the predictive operator command map generator 432 generates a functional predictive operator command map 440 that, at any given location in the field, an operator command at that location based on a corn cob size value, a yield value, a vegetative index value, a seeding trait value, or a pre-operation trait value contained in the functional predictive Corn cob size map 360 is included, with yield map 333, vegetative index map 332, seed trait map 399, and pre-operation map 400 corresponding to that location, respectively.

The predictive map generator 212 outputs one or more of the functional predictive maps 436 or 440 . Either of the functional predictive maps 436 or 440 can be provided to the control zone generator 213, the control system 214, or both, as in FIG 2 shown. The control zone generator 213 generates control zones and integrates those control zones to provide a functional predictive agricultural feature map 436 with control zones and a functional predictive operator command map 440 with control zones. One or more of the functional predictive maps 436 or 440 (with or without control zones) can be provided to the control system 214, which generates control signals to control one or more of the controllable subsystems of the agricultural harvesting machine 100, such as controllable subsystems 216 based on or multiple of functional predictive maps 436 or 440 (with or without control zones). One or more of maps 436 or 440 (with or without control zones) may be presented to operator 260 or another user.

7 12 is a flow chart illustrating an example of the operation of a predictive model generator 210 and a predictive map generator 212 in generating one or more predictive models 426 and one or more functional predictive maps 436 and 440, respectively. At block 442, the predictive model generator 210 and the predictive map generator 212 receive a map. The card can contain one or more of the 6 Maps shown include such as vegetative index map 332, yield map 333, seeding map 399, pre-operation map 400, functional predictive stalk diameter map 360, or other maps 401. At block 444, the predictive model generator 210 receives a sensor signal representing sensor data from an in- situ sensor 208 includes. The in situ sensor may be one or more agricultural feature sensors 402, an operator input sensor 404, or another sensor 408. Some examples of in situ sensors 208 are given in 8th shown. Block 446 indicates that the sensor signal received by the predictive model generator 210 includes data of a type indicative of an agricultural trait. Block 450 indicates that the sensor signal received from the predictive map generator 210 may be a sensor signal having data of a type indicative of an operator command input. The predictive model generator 210 may also receive other in situ sensor inputs, as indicated by block 452 .

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

Coming back to 7 at block 456, the predictive model generator 210 also receives a geographic position 334 or an indication of geographic position from the geographic position sensor 204, as in FIG 6 shown. The geographic location 334 may correlate to the geographic location from which the sensed variable(s) sensed by in situ sensors 208 were taken. For example, the predictive model generator 210 may receive the geographic position 334 or indication of geographic position from the geographic position sensor 204 and determine an accurate geographic position based on machine delays, machine speed, etc. from which the processed data 409 was derived.

At block 458, the predictive model generator 210 generates one or more predictive models 426 representing a relationship between a mapped value in a map received at block 442 and model a feature represented in the processed data 409 . For example, in some cases, the mapped value in the received map may be a corn cob size value, and the predictive model generator 210 generates a predictive model using the mapped value of a received map and a feature sensed by in situ sensors 208, as shown in the processed data 409 , or a related feature, such as a feature that correlates with the feature sensed by in situ sensors 208 .

For example, at block 460, the predictive model generator 210 may generate a predictive model 426 that models a relationship between a corn cob size value obtained from one or more maps and agricultural feature data obtained from an in situ sensor 208. In another example, at block 462 , the predictive model generator 210 may generate a predictive model 426 that models a relationship between a corn cob size value obtained from one or more cards and operator command inputs obtained from an in situ sensor 208 . In another example, at block 463, the predictive model generator 210 may generate a predictive model 426 representing a relationship between another feature value obtained from one or more cards and operator command inputs obtained from an in situ sensor 208. modeled. The model generator 210 may generate a variety of other predictive models that model relationships between various other feature values obtained from a map or maps and data from one or more in situ sensors 208 .

The one or more predictive models 426 are provided to the predictive map generator 212 . At block 466, the predictive map generator 212 generates one or more functional predictive maps. The functional predictive maps may be one or more functional predictive agricultural feature maps 436, or one or more functional predictive operator command maps 440, or any combination of these maps. The functional predictive agricultural feature map 436 predicts an agricultural feature at various positions in the field. The functional predictive operator command map 440 predicts desired or likely operator command inputs at various locations in the field. Furthermore, one or more of functional predictive maps 436 and 440 may be generated during a farming operation. Thus, as the agricultural harvester 100 moves through a field in performing a farming operation, the one or more predictive maps 436 and 440 are generated while the farming operation is being performed.

At block 468, the predictive map generator 212 outputs the one or more functional predictive maps 436 and 440. At block 470, the predictive map generator 212 may configure the one or more maps for presentation to and possible interaction by an operator 260 or other user. At block 472, the predictive card generator 212 may configure the one or more cards for the control system 214 to consume. At block 474, the predictive map generator 212 may provide the one or more predictive maps 436 and 440 to the control zone generator 213 for generation and integration of control zones. At block 476 , the predictive map generator 212 otherwise configures the one or more predictive maps 436 and 440 that may be presented to the operator 260 or other user, or may also be provided to the control system 214 .

At block 478, the control system 214 then generates control signals to control the controllable subsystems of the agricultural harvesting machine 100, such as controllable subsystems 216, based on the one or more functional predictive maps 436 or 440 (or the functional predictive maps 436 or 440 having control zones ) and an input from the geographic position sensor 204 . For example, when the functional predictive agricultural feature map 436 is provided to the control system 214, one or more controllers responsively generate control signals to control one or more of the controllable subsystems 216 to operate the agricultural harvesting machine 100 based on the predicted agricultural feature values in functional predictive agricultural feature map 436 or functional predictive agricultural feature map 436 containing control zones. In another example, when the functional predictive command map 440 is provided to the control system 214, one or more controllers responsively generate control signals to control one or more of the controllable subsystems 216 to operate the agricultural harvesting machine 100 based on the predicted operator command values in functional predictive command map 440 or functional predictive command map 440 containing control zones. Block 480 indicates this.

Block 484 shows an example where the control system 214 uses the functional predictive operator command map 440 or the functional predictive Receives operator command card 440 with added control zones. In response, the adjustment controller 232 generates control signals to control other machine adjustments or machine functions based on predicted operator command inputs in the functional predictive operator command map 440 or the functional predictive operator command map 440 adding control zones. Block 485 shows that control signals for controlling the operation of the agricultural harvesting machine 100 can also be generated in other ways, for example based on a combination of the functional predictive maps 436 or 440. For example, one or more controllers generate based on the functional predictive maps 436 or 440 (with or without control zones) or both control signals to control one or more of the controllable subsystems 216 to operate the agricultural harvesting machine 100 based on the predicted agricultural feature values in the functional predictive agricultural feature map 436 or the functional predictive agricultural feature map 436 containing control zones or operator command values in functional predictive operator command map 440, or functional predictive operator command map 440 containing control zones.

The control system 214 may generate control signals to control the header or other machine actuator(s) 248, such as to control a position or spacing between 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 other examples, the control system 214 may generate control signals to control a speed of the threshing rotor 112, may generate control signals to control a concave gap, or may generate control signals to adjust power output to some of the crop processing systems, such as the harvest chains or stalk rollers .

8th FIG. 12 is a block diagram illustrating example in situ sensors 208. FIG. In situ sensors 208 can detect a variety of agricultural traits. Some of the in 8th The sensors shown, or various combinations thereof, may include both a sensor 402 and a processing system 406, while others may act as the sensor 402 described above with respect to FIGS 6 and 7 will be described with the processing system 406 being separate. Some of the possible in situ sensors 208 used in 8th are shown and described above with respect to the previous figures and are similarly numbered. 8th FIG. 1 shows that the in situ sensors 208 can include operator input sensors 980, machine sensors 982, crop property sensors 984, field and soil property sensors 985, environmental feature sensors 987, and in situ sensors 208 a variety of other sensors 226. Operator input sensors 980 may be sensors that detect operator inputs via operator interface mechanisms 218 . Therefore, operator input sensors 980 can detect user movement of linkages, joysticks, a steering wheel, buttons, knobs, pedals, or other user input devices. Operator input sensors 980 may also detect user interactions with other operator input mechanisms, such as a touch-sensitive screen, a microphone that uses speech recognition, or a variety of other operator input mechanisms.

Machine sensors 982 can sense various characteristics of agricultural harvesting machine 100 . As discussed above, the machine sensors 982 may include, for example, machine speed sensors 146, a separator loss sensor 148, a clean grain camera 150, a forward-looking imaging mechanism 151, loss sensors 152, or a geographic position sensor 204, examples of which are described above. Machine sensors 982 may also include machine setting sensors 991 that sense machine settings. Some examples of machine settings have been given above in relation to 1 described. The header (e.g., header) position sensor 993 may sense the position of the header 102, reel 164, cutter 104, or other header, such as a position relative to the frame of the agricultural harvesting machine 100 or relative to the face of the field. For example, sensors 993 may measure altitude of the header 102 capture above the ground. Machine sensors 982 may also include header (e.g., header) orientation sensors 995 . Sensors 995 may sense the orientation (such as pitch or roll) of header 102, such as orientation relative to agricultural harvester 100 or relative to the ground. Machine sensors 982 may include stability sensors 997 . The stability sensors 997 detect an oscillation or bouncing motion (and amplitude) of the agricultural harvesting machine 100. The machine sensors 982 can also include residue setting sensors 999 that are configured to detect whether the agricultural harvesting machine 100 is configured to shred the residue, a swath generate or otherwise treat the residue. The machine sensors 982 may include a shoe fan speed sensor 951 that senses the speed of the cleaning fan 120 . The machine sensors 982 may include concave distance sensors 953 that sense the distance between the rotor 112 and the concaves 114 on the agricultural harvesting machine 100 . Machine sensors 982 may include chopper gap sensors 955 that sense the size of the openings in chopper 122 . The machine sensors 982 may include a threshing rotor speed sensor 957 that senses a rotor speed of the rotor 112 . Engine sensors 982 may include a rotor force sensor 959 that senses the force (eg, pressure, torque, etc.) used to drive rotor 112 . The machine sensors 982 may include a wire gap sensor 961 that senses the size of the openings in the wire 124 . Machine sensors 982 may include a MOG moisture sensor 963 that senses a moisture content of MOG that passes agricultural harvester 100 . The machine sensors 982 may include a machine orientation sensor 965 that senses the orientation (such as pitch or roll) of the agricultural harvesting machine 100 relative to the frame or relative to the area of the field. Machine sensors 982 may include material feed rate sensors 967 that sense material feed rate as material moves through feeder housing 106, clean grain elevator 130, or elsewhere in agricultural harvesting machine 100. Machine sensors 982 may include biomass sensors 969 that sense biomass moving through feeder housing 106 , through separator 116 , or elsewhere in agricultural harvesting machine 100 . The machine sensors 982 may include a fuel consumption sensor 971 that senses fuel consumption rate of the agricultural harvesting machine 100 over time. Machine sensors 982 may include a power usage sensor 973 that senses power usage in agricultural harvesting machine 100, such as which subsystems are using power, the rate at which subsystems are using power, or the distribution of power among the subsystems in agricultural harvesting machine 100 Machine sensors 982 may include tire pressure sensors 977 that sense air pressure in tires 144 of agricultural harvesting machine 100 . Machine sensor 982 may include a variety of other machine performance sensors or machine feature sensors, which are indicated by block 975 . The machine performance sensors and the machine feature sensors 975 may sense machine performance or features of the agricultural harvesting machine 100 .

The crop characteristic sensors 984 can detect characteristics of the separated crop during processing of the crop by the agricultural harvesting machine 100 . Crop properties can include things such as crop type, crop moisture, grain quality (such as broken grain), MOG values, grain components such as starches and protein, MOG moisture, and other properties of the crop.

The field and soil feature sensors 985 can detect the characteristics of the field and soil. The field and soil properties can include soil moisture, soil compactness, the presence and location of standing water, soil type, and other soil and field characteristics.

Environmental feature sensors 987 may sense one or more environmental features. The environmental features can include things like wind direction and speed, precipitation, fog, dust levels or other pollution, or other environmental features.

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

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

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

The target setting identification component 498 sets a value of the target setting used to control the WMA or set of WMAs in different control zones. For example, if the selected WMA is header or other machine actuators 248 and the functional predictive map analyzed is a functional predictive corn head size map 360 (with control zones) that maps predictive corn head size values representing a diameter, length, or weight of corn heads (such as corn ears ) at various positions across the field, the target adjustment in each control zone may be a cap position or cap spacing adjustment based on corn head size values contained in the functional predictive corn head size map 360 within the identified control zone. This is because, with a corn head size of vegetation at a position in the field to be harvested by the agricultural harvesting machine 100, it is important, among other things, to control the position or spacing of the cover plates of the agricultural harvesting machine 100 so that the Cover plates have proper settings to reduce losses and reduce non-grain material (MOG) pick-up.

In some examples where the agricultural harvester 100 is to be controlled based on a current or future position of the agricultural harvester 100, multiple target settings for a WMA at a particular position may be possible. In this case, the target settings can have different values and compete. Therefore, the target settings must be resolved so that only a single target setting can be used to control the WMA. For example, if the WMA is an actuator in drive system 250 that is being controlled to control the speed of agricultural harvesting machine 100, there may be several different competing sets of criteria used by control zone generation system 488 in identifying the control zones and target settings for the selected one WMA are taken into account in the control zones. For example, different target settings may be used to control the position or spacing of the deck plate based on, for example, a recognized or predicted corn head size value, a recognized or predicted operator command input value, a recognized or predicted yield value, a recognized or predicted vegetative index value, a recognized or predicted feed rate value, a recognized or predicted fuel efficiency value, a detected or predicted grain loss value, or a combination thereof. It should be understood that these are merely examples and the target settings for different WMAs can be based on various other values or combinations of values. However, the agricultural harvest can machine 100 does not have multiple positions or spacing arrangements for the same set of deck plates at any given time. Rather, the position or spacing of a set of deck plates of agricultural harvesting machine 100 is at a specific position or spacing at all times. Thus, one of the competing target settings is selected to control the position or spacing of the agricultural harvesting machine 100 deck plates.

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

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

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

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

Once the attitude resolver is identified for a particular regime zone, that attitude resolver can be used to resolve competing target attitudes, identifying more than one target attitude based on the control zones. The different types of setting resolvers can have different shapes. For example, the setting resolvers identified for each regime zone may include a human choice resolver in which the competing target settings are presented to an operator or other user for resolution. In another example, the attitude resolver may include a neural network or other artificial intelligence or machine learning system. In such cases, the setting resolvers may resolve the competing target settings based on a predicted or historical quality metric corresponding to each of the different target settings. For example, increased deck clearance can reduce the amount of MOG pickup but increase grain loss from the header. Decreased top plate clearance can increase the amount of MOG pickup and thus decrease overall machine capacity. When a quality metric is selected, such as grain loss or machine capacity, the predicted or historical value for the selected quality metric given the two competing top plate clearance setting values can be used to resolve competing settings for a WMA or set of WMAs. In some cases, the adjustment resolvers can be a set of threshold rules that can be used instead of or in addition to the regime zones. An example of a threshold rule can be expressed as follows:

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

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

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

At block 530, the control zone generator 213 receives a map that is analyzed for processing. In one example, as shown at block 532, the map analyzed is a functional predictive map. For example, the map analyzed may be one of functional predictive maps 436 or 440. In another example, the map analyzed may be the functional predictive corn cob size map 360 . Block 534 indicates that the card to be analyzed can be other cards.

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

At block 554, regimen zone criteria identification component 522 obtains regimen zone definition criteria for the selected WMA or set of WMAs. Block 556 provides an example where the regime zone definition criteria are based on manual input from operator 260 or another user. Block 558 illustrates an example where the regime zone definition criteria are based on corn cob size values. Block 560 illustrates an example where the regimen zone definition criteria are based on vegetative index values. Block 561 illustrates an example where the regime zone criteria are based on seed attribute values. Block 562 gives provides an example where the regimen zone definition criteria are based on yield values. Block 564 provides an example where the regime zone definition criteria is or includes other criteria as well, for example, crop type or variety, weed type, weed intensity, or crop condition, such as whether the crop is kinked. Other criteria can also be used.

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

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

At block 588, the WMA selector 486 determines whether there are more WMAs or sets of WMAs to process. If additional WMAs or sets of WMAs remain to be processed, processing returns to block 536 where the next WMA or set of WMAs for which to define control zones and regime zones is selected. If no additional WMAs or sets of WMAs remain for which to generate control zones or regime zones, processing moves to block 590 where the control zone generator 213 outputs a map of control zones, target settings, regime zones, and setting resolvers for each of the WMAs or sets of WMAs . As discussed above, the issued card may be presented to operator 260 or another user; the card issued may be provided to the control system 214; or the issued card may be otherwise issued.

11 12 illustrates an example of the operation of the control system 214 in controlling the agricultural harvesting machine 100 based on a map output from the control zone generator 213 . Thus, at block 592, the control system 214 receives a map of the work location. In some cases, as represented by block 594, the map may be a functional predictive map that may include control zones and regime zones. In some cases, the received map may be a functional predictive map excluding tax zones and regime zones. Block 596 provides an example where the received worksite map may be a pre-information map with tax zones and regime zones identified thereon. Block 598 provides an example where the received map may include multiple different maps or multiple different map layers. Block 610 provides an example where the received map may take other forms as well.

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

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

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

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

12 FIG. 14 is a block diagram showing an example of an operator interface controller 231. FIG. In an illustrated example, operator interface control 231 includes an operator ner input command processing system 654, another control interaction system 656, a language processing system 658 and an action signal generator 660. The operator input command processing system 654 includes the language processing system 662, the touch gesture handling system 664 and other elements 666. The other control interaction system 656 includes the control input processing system 668 and the control output generator 670. The language processing system 658 includes the Trigger detector 672, recognition component 674, synthesis component 676, natural language understanding system 678, dialog management system 680, and other elements 682. Action signal generator 660 includes visual control signal generator 684, audio control signal generator 686, haptic control signal generator 688, and other elements 690. Before the operation of the in 12 While the example operator interface control 231 shown in the illustration will describe how to handle various operator interface actions, a brief description of some of the elements of the operator interface control 231 and the operation associated therewith will first be provided.

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

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

The language processing system 658 can be invoked in a number of ways. For example, voice processing system 662 continuously provides input from a microphone (which is one of operator interface mechanisms 218) to voice processing system 658 in one example. The microphone detects speech from the operator 260 and the speech processing system 662 provides the speech processing system 658 with the recognized speech. Trigger detector 672 detects a trigger indicating that language processing system 658 is invoked. In some cases, when speech processing system 658 receives continuous speech input from speech processing system 662, speech recognition component 674 performs continuous speech recognition on all speech spoken by operator 260. In some cases, the language processing system 658 is configured to be invoked using a wake-up word. That is, in some cases, operation of the speech processing system 658 may be initiated based on recognition of a selected spoken word, referred to as a wake-up word. In such an example, where the detection component 674 detects the wakeup word, the detection component 674 provides an indication that the wakeup word was detected to trigger the detector 672 . The trigger detector 672 detects that the voice processing system 658 has been invoked or triggered by the wake-up word. In another example, language processing system 658 may be invoked by an operator 260 operating an actuator on a user interface mechanism, such as by touching an actuator on a touch-sensitive display screen, pressing a button, or providing another trigger input. In such an example, trigger detector 672 may detect that language processing system 658 has been invoked when a trigger input is detected via a user interface mechanism. Trigger detector 672 may also detect that speech processing system 658 has been invoked in other ways.

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

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

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

13 12 is a flow chart illustrating an example of the operation of operator interface controller 231 in generating an operator interface display on operator interface mechanism 218, which may include a touch-sensitive display screen. 13 also illustrates an example of how operator interface controller 231 may recognize and process operator interactions with the touch-sensitive display screen.

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

At block 706, visual control signal generator 684 in operator interface controller 231 controls the touch-sensitive display screen in operator interface mechanisms 218 to generate a display showing all or part of a field represented by the received card. Block 708 indicates that the displayed field may include a current position marker showing a current position of agricultural harvesting machine 100 relative to the field. Block 710 provides an example in which the displayed field includes a next work item marker identifying a next work item (or region on the field), in which the agricultural harvesting machine 100 is operated. Block 712 provides an example in which the displayed field includes an upcoming area display section indicating areas that are yet to be processed by the agricultural harvesting machine 100, and Block 714 provides an example in which the displayed field includes previously visited display sections. represent the areas of the field that the agricultural harvesting machine 100 has already worked. Block 716 provides an example in which the displayed panel displays various features of the georeferenced location panel on the map. For example, if the received map is a predictive corn head size map, such as functional predictive corn head size map 360, the displayed field may display the various corn head size values georeferenced within the displayed field. In other examples, the card received may be a different card from those described herein. Thus, the displayed panel may display various attribute values, such as yield values, vegetative index values, seeding attribute values, or operator command values, as well as various other values that are georeferenced within the displayed panel. The mapped features may be shown in the previously visited areas (as shown in block 714), in the upcoming areas (as shown in block 712), and in the next work unit (as shown in block 710). Block 718 provides an example where the displayed field also includes other items.

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

in the in 14 In the example shown, the user interface display 720 illustrates that the touch-sensitive display screen includes a display feature for operating a microphone 722 and a speaker 724 . Thus, the touch-sensitive display may be communicably coupled to microphone 722 and speaker 724 . Block 726 indicates that the touch-sensitive display screen may include a variety of operator interface control actuators, such as buttons, keyboards, soft keyboards, links, icons, switches, etc. The operator 260 may manipulate the operator interface control actuators to perform various functions.

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

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

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

In the example of 14 the user interface display 720 also has a control display section 738 . Control display portion 738 allows the operator to display information and interact with the display of user interface 720 in various ways.

For example, the actuators and indicator markers in section 738 may be displayed as individual items, fixed lists, scrollable lists, drop-down menus, or drop-down lists. in the in 14 In the example shown, the display section 738 displays information for the three different corn head size categories corresponding to the three symbols mentioned above. The display portion 738 also includes a set of touch-sensitive actuators that the operator 260 can interact with by touch. For example, the operator 260 may touch the touch-sensitive actuators with a finger to activate the respective touch-sensitive actuator.

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

The display portion 738 also includes an interactive marker display portion indicated generally at 743 . The interactive marker display section 743 includes a symbol column 746 that displays the symbols corresponding to each category of values or attributes (in the case of 14 , corn head size) tracked on the field display section 728. The display section 738 also includes an interactive identifier display section, indicated generally at 745 . The interactive identifier display section 745 includes an identifier column 748 showing the identifier (which may be a textual identifier or other identifier) representing the category of values or attributes (in the case of 14 , corn head size). Without limitation, the icons in icon column 746 and the identifiers in identifier column 748 may include any display feature, such as different colors, shapes, patterns, intensities, text, symbols, or other display features, and may be customizable through interaction of an operator of agricultural harvesting machine 100.

The display portion 738 also includes an interactive value display portion indicated generally at 747 . The interactive value display section 747 includes a value display column 750 that displays selected values. The selected values correspond to the attributes or values being tracked or displayed on panel display portion 728, or both. The selected values can be selected by an operator of the agricultural harvesting machine 100 . The selected values in the value display column 750 define a range of values or a value by which other values, such as predicted values, are to be classified. So in the example in 14 a predicted or measured corn head diameter that is greater than or equal to 6.6 cm (2.6 inches) categorized as "large stalk diameter" and a predicted or measured corn head diameter that is less than or equal to 3.5 cm (1.4 inches). , is classified as “small corn head diameter”. In some examples, the selected values may include a range such that a predicted or measured value that is within the range of the selected value is classified under the corresponding identifier. As in 14 As shown, a "mean corn head diameter" includes a range of 3.5 cm to 6.6 cm (1.41 inches to 2.59 inches), so a predicted or measured corn head diameter falling in the range of 3.5 cm to 6.6 cm (1.41 in. to 2.59 in.) is classified as "mean corn head diameter". The selected values in value display column 750 are adjustable by an operator of agricultural harvesting machine 100 . In one example, operator 260 can select the particular portion of field display portion 728 for which the values in column 750 are to be displayed. Thus, the values in column 750 may correspond to values in display sections 712, 714, or 730.

The display section 738 also includes an interactive threshold display section, indicated generally at 749 . The interactive threshold display section 749 includes a threshold display column 752 that displays action thresholds. The action thresholds in column 752 may be thresholds corresponding to the selected values in value display column 750 . When the predicted or measured values of features being tracked or displayed, or both the meet corresponding action thresholds in threshold indicator column 752, then control system 214 takes the action identified in column 754. In some cases, a measured or predicted value may satisfy a corresponding action threshold by meeting or exceeding the corresponding action threshold. For example, in one example, operator 260 may select a threshold to change the threshold by touching the threshold in threshold display column 752 . Once selected, the operator 260 can change the threshold. The thresholds in column 752 may be configured to take the designated action when the measured or predicted value of the feature exceeds, equals, or is below the threshold. In some cases, the threshold may represent a range of values or a range of deviations from the selected values in the value display column 750 such that a predicted or measured feature value that meets or falls within the threshold satisfies the threshold. For example, in the example of corn head sizes, a predicted corn head diameter that is within 10% of 6.6 cm (2.6 inches) meets the corresponding action threshold (within 10% of 6.6 cm (2.6 inches)), and an action such as increasing the cover plate spacing is performed by the control system 214 . In other examples, the thresholds in the Threshold Display Column 752 are separate from the selected values in the Value Display column 750, such that the values in the Value Display column 750 define the classification and display of predicted or measured values, while the Action Thresholds define when an action is taken based on the measured or predicted values. For example, while a predicted or measured corn head diameter of 5.08 cm (2.0 inches) may be referred to as "mean corn head diameter" for classification and display purposes, the action threshold may be 5.33 cm (2.1 inches), so that no action is taken until the corn head diameter meets the threshold. In other examples, the thresholds in threshold display column 752 may include distances or times. For example, in the example of a distance, the threshold may be a threshold distance from the area of the field where the measured or predicted value is georeferenced that the agricultural harvesting machine 100 must have before any action is taken. For example, a Threshold Distance value of 1.52 m (5 feet) would mean that an action would be taken if the agricultural harvester is in or within 1.52 m (5 feet) of the area of the field where the measured or predicted value is georeferenced. In an example where the threshold is time, the threshold may be a threshold time for the agricultural harvester 100 to reach the area of the field where the measured or predicted value is georeferenced. For example, a threshold of 5 seconds would mean that an action is taken when the agricultural harvesting machine 100 is still 5 seconds away from the area of the field where the measured or predicted value is georeferenced. In such an example, the current position and the driving speed of the agricultural harvesting machine can be taken into account.

The display portion 738 also includes an interactive action display portion indicated generally at 751 . Interactive action display section 751 includes an action display column 754 that displays action identifiers that indicate actions to be taken when a predicted or measured value meets an action threshold in threshold display column 752 . The operator 260 can touch the action identifiers in column 754 to change the action to be taken. When a threshold is met, action can be taken. For example, at the bottom of column 754 an action to increase the deck gap and an action to decrease the deck gap are identified as actions to be taken when the measured or predicted value meets the threshold in column 752 . In some examples, when a threshold is met, multiple actions can be taken. For example, a top plate spacing may be adjusted, a power output to the stem processing components (eg, stem rollers, collector chains, etc.) may be adjusted, and a speed of the agricultural machine may be adjusted. These are just some examples.

The actions that can be specified in column 754 can be any of a variety of different types of actions. For example, the actions may include a keep away action that, when performed, prevents the agricultural harvester 100 from continuing to harvest in an area. The actions may include a “change speed” action that, when performed, changes the ground speed of the agricultural harvesting machine 100 through the field. The actions may include a setting change action to change a setting of an internal actuator or another WMA or set of WMAs, or to implement a setting change action affecting a setting, such as the deck plate spacing, of one or more sets of decks header plates along with various other settings. These are just examples, and a variety of other actions are contemplated here.

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

Returning to the flow chart of 13 the description of the operation of the operator interface controller 231 continues. At block 760 , operator interface controller 231 recognizes an input setting of a flag and controls touch-sensitive user interface display 720 to display the flag on panel display portion 728 . The recognized input may be operator input, as indicated at 762 , or input from another controller, as indicated at 764 . At block 766 , operator interface controller 231 recognizes an in situ sensor input indicative of a measured feature of the field from one of in situ sensors 208 . At block 768, visual control signal generator 684 generates control signals to control user interface display 720 to display actuators, to modify user interface display 720, and to modify machine controls. For example, block 770 represents that one or more of the actuators for setting or modifying the values in columns 739, 746, and 748 may be displayed. Thus, the user can set flags and change the characteristics of those flags. For example, a user can change the corn cob size levels and corn cob size designators according to the flags. Block 772 represents that action thresholds in column 752 are displayed. Block 776 represents the actions in column 754 being displayed and block 778 represents the selected value in column 750 being displayed. Block 780 indicates that a variety of other information and actuators may be displayed on user interface display 720 as well.

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

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

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

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

Table 1

Operator: "Johnny, update me on the current corn cob size values."

Operator interface control: "Corn cob diameters are large at current location."

Operator: "Johnny, what should I do about the cob size levels?"

Operator interface controls: "Increase deck gap on header."

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

Table 2

Operator Interface Control: "In the last 10 minutes, 95% of the time was in areas with medium cob size levels."

Operator interface control: "The next 1 acre is 66% medium corn cob size levels and 33% small corn cob size levels."

Operator interface control: "Warning: We are now approaching an area of large corn cob sizes. Adjust the top plate gap.”

Operator interface control: “Caution: Approaching an area with a small corn cob size level. Adjust the top plate gap.”

The example shown in Table 3 illustrates that some actuators or user input mechanisms on touch-sensitive display 720 may be supplemented with voice dialogue. The example in Table 3 illustrates that the action signal generator 660 can generate action signals to automatically mark an area of high cob size level in the field to be harvested.

Table 3

Human: "Johnny, mark the area with large cob size level."

Operator interface control: "Large corn cob size level area is highlighted."

The example presented in Table 4 illustrates that the action signal generator 660 may engage in a dialog with the operator 260 to begin and end marking a large corn cob size level area.

Table 4

Human: "Johnny, start marking the area with large cob size level."

Operator interface control: "High corn cob size level area will be highlighted."

Human: "Johnny, stop marking the area with large cob size level."

Operator interface control: "High corn cob size level area marking stopped."

The example shown in Table 5 illustrates that action signal generator 160 may generate signals to mark an area of corn cob size levels in a manner different than those shown in Tables 3 and 4.

Table 5

Human: "Johnny, mark the next 30 meters as a large cob size level area."

Operator interface control: "The next 30 meters is marked as a large corn cob size level area."

Referring again to 13 Block 906 illustrates that operator interface controller 231 may otherwise detect and process conditions for outputting a message or other information. For example, the other controller interaction system 656 may recognize input from other controllers indicating that the operator 260 should be presented with alarms or output messages. Block 908 shows that the outputs can be audio messages. Block 910 shows that the outputs can be visual messages and block 912 shows that the outputs can be haptic messages. Until the operator interface controller 231 determines that the current harvesting operation is complete, as indicated by block 914, processing returns to block 698 where the geographical position of the harvester 100 is updated and processing continues as described above to the user interface display 720 To update.

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

Thus, it can be seen that one or more maps are obtained from an agricultural harvester showing agricultural feature values at different geographic locations of a field to be harvested. An in situ sensor on the harvester detects a trait having values indicative of an agricultural trait, such as a corn head size or an operator command, as the agricultural harvester moves through the field. A predictive map generator generates a predictive map that predicts control values for various locations based on the values in the pre-information map and the agricultural feature sensed by the in situ sensor. A control system controls the controllable subsystem based on the control values in the predictive map.

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

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

A number of user interface displays were also discussed. The displays can take a number of different forms and can have a number of different user controlled operator interface mechanisms thereon. For example, the user-actuable operator interface mechanisms may include text boxes, check boxes, icons, links, drop-down menus, search boxes, and so on. The user 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. When the screen on which the user actuable operator interface mechanisms are displayed is a touch-sensitive screen, the user actuatable operator interface mechanisms can also 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 perform the functions associated with those systems, components, logic or perform interactions. In addition, any or all of the systems, components, logic, and interactions may be implemented by software loaded into memory and then executed by a processor or server or other computing component, as described below. Any or all of the systems, components, logic, and interactions may also be implemented through various combinations of hardware, software, firmware, etc., some examples of which are described below. These are some examples of different structures that can be used to implement any or all of the systems, components, logic, and interactions described above. Other structures can also be used.

15 12 is a block diagram of agricultural harvester 600 shown in FIG 2 shown agricultural harvesting machine 100 may be similar. Agricultural harvester 600 communicates with elements in a remote server architecture 500. In some examples, remote server architecture 500 provides computing, software, data access, and storage services that do not require end-user knowledge of the physical location or configuration of the system that provides the Services. In various examples, remote servers may provide the services over a wide area network, such as the Internet, using appropriate protocols. For example, remote server applications can be deployed over a wide area network and accessed through a web browser or other computer component. Software or components included in 2 shown and related data may be stored on servers at a remote location. The computing resources in a remote server environment can be consolidated at a remote data center site or distributed to a variety of remote data centers. Remote server infrastructures can provide services across shared data centers, although the services appear to the user as a single point of entry. Thus, the components and functions described herein can be provided from a remote server at a remote site via a remote server architecture. Alternatively, the components and functions may be provided by a server, or the components and functions may be installed directly or otherwise on end devices.

in the in 15 example shown are some elements of the in 2 shown and these items are similarly numbered. 15 FIG. 12 specifically shows that the predictive model generator 210 or the predictive map generator 212 or both may be located at a server location 502 that is remote from the agricultural harvester 600. FIG. Therefore, the one in the example applies 15 shown agricultural harvester 600 via remote server location 502 to systems.

15 also illustrates another example of a remote server architecture. 15 shows that some elements of 2 may be located at a remote server location 502, while others may be located at a different location. For example, data store 202 may be located at a separate site from site 502 and accessed through the remote server at site 502 . Regardless of where the elements are located, the elements can be accessed directly by the agricultural harvester 600 over a network, such as a wide area network or a local area network; the items may be hosted at a remote location by a service; or the items may be provided as a service or accessed via a connection service located at a remote location. In addition, data can be stored at any location and the stored data can be retrieved or forwarded to operators, users or systems. For example, physical carriers can be used instead of or in addition to electromagnetic radiation carriers. In some examples where network coverage is poor or non-existent, another machine, e.g. a tank truck or other mobile machine or vehicle, have an automatic, semi-automatic or manual information collection system. When the combine harvester apparatus 600 gets close to the machine containing the information gathering system, such as a tanker truck, prior to refueling, the information gathering system collects the information from the combine harvester 600 via any ad hoc wireless link. The information collected may then be relayed to another network when the machine containing the received information reaches a location where wireless telecommunications service coverage or other wireless coverage is available. For example, a fuel truck may enter an area that has wireless communications coverage when it is traveling to a location to fuel other machines, or when it is at a main fuel depot. All of these architectures are considered herein. Furthermore, the information may be stored in the agricultural harvester 600 until the agricultural harvester 600 reaches an area of wireless communication coverage. The agricultural harvester 600 itself can send the information to another network.

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

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

16 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 handheld device 16 in which the present system (or portions thereof) may be employed. 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 17-18 are examples of portable or mobile devices.

16 provides a general block diagram of the components of a terminal 16, showing some of the 2 components shown can run, 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 . The interface 15 and the communication links 13 communicate with a processor 17 (which may also be the processors or servers from the other FIG. can embody) via a bus 19, which is also connected to the memory 21 and the input/output components (I/O) 23 as well as the clock generator 25 and the locating system 27.

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.

17 FIG. 12 shows an example where the device 16 is a tablet computer 600. FIG. In 17 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.

18 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.

19 is an example of a computing environment in which elements of 2 can be used. With reference to 19 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 can be described in appropriate sections of 19 be used.

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

System memory 830 includes computer storage media in the form of volatile and/or non-volatile memory, or both, such as read-only memory (ROM) 831 and random access memory (RAM) 832. A basic input/output system 833 (BIOS) that contains the basic programs that help To transfer information between elements within the computer 810, such as at startup, is typically stored in ROM 831. RAM 832 typically contains data or program modules, or both, that are immediately accessible to processing unit 820 and/or are currently operating. Illustrated by way of example and not limitation 19 the operating system 834, the application programs 835, further program modules 836 and program data 837.

Computer 810 may also include other removable/non-removable volatile/non-volatile computer storage media. For example shows 19 a hard disk drive 841 that reads from or writes to non-removable, non-volatile magnetic media, an optical disk drive 855, and a non-volatile optical disk 856. Hard disk drive 841 is typically connected to system bus 821 via a non-removable storage interface, such as interface 840, and optical disk drive 855 is typically connected to system bus 821 via a removable storage interface, such as interface 850.

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

The drives and associated computer storage media discussed above and in 19 11-11 provide storage for computer 810 of computer-readable instructions, data structures, program modules, and other data. In 19 For example, the hard disk drive 841 is shown as a storing operating system 844, application programs 845, further 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 over a network interface or an adapter 870 connected to the LAN 871. 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. 19 illustrates, for example, that remote application programs 885 can reside on remote computer 880 .

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

Example 1 is an agricultural working machine comprising:

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

• einen prädiktiven Maiskopfgrößenkartengenerator, der als die funktionelle prädiktive Karte eine funktionelle prädiktive Maiskopfgrößenkarte erzeugt, die prädiktive Maiskopfgrößen als prädiktive Steuerwerte auf die verschiedenen geografischen Positionen in dem Feld abbildet.

Example 2 is the agricultural work machine of any or all of the preceding examples, where the predictive map generator includes:

• a predictive corn head size map generator that generates, as the functional predictive map, a functional predictive corn head size map that maps predictive corn head sizes as predictive control values to the various geographic locations in the field.

• eine Deckplattenpositionssteuerung, die ein Deckplattenpositionssteuersignal auf Grundlage der erkannten geografischen Position und der funktionellen prädiktiven Maiskopfgrößenkarte erzeugt und das steuerbare Teilsystem auf Grundlage des Deckplattenpositionssteuersignals steuert, um einen Abstand zwischen einem mindestens einen Satz von Deckplatten an der landwirtschaftlichen Arbeitsmaschine zu steuern.

Example 3 is the agricultural work machine of any or all of the preceding examples, wherein the control system includes:

• a deck plate position controller that generates a deck plate position control signal based on the detected geographic location and the functional predictive corn head size map and controls the controllable subsystem based on the deck plate position control signal to control a spacing between at least one set of deck plates on the agricultural work machine.

• einen prädiktiven Bedienerbefehlskartengenerator, der eine funktionelle prädiktive Bedienerbefehlskarte als die funktionelle prädiktive Karte erzeugt, die prädiktive Bedienerbefehle auf die verschiedenen geografischen Positionen im Feld abbildet.

Example 4 is the agricultural work machine of any or all of the preceding examples, where the predictive map generator includes:

• a predictive operator command map generator that generates a functional predictive operator command map as the functional predictive map that maps predictive operator commands to the various geographic locations in the field.

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

Example 5 is the agricultural work machine of any or all of the preceding examples, wherein the control system includes:

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

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

Example 6 is the agricultural work machine of any or all of the preceding examples and further includes:

• a predictive model generator that generates a predictive model that models a relationship between the agricultural feature and the corn cob size based on a value of the agricultural feature in the map at the geographic location and the value of the corn cob size detected by the in situ sensor corresponding to the geographic position
• wherein the predictive map generator generates the functional predictive map based on the values of the agricultural feature in the map and based on the predictive model.

• einen prädiktiven Modellgenerator, der ein prädiktives Modell erzeugt, das eine Beziehung zwischen dem vegetativen Indexmerkmal und der Maiskolbengröße auf Grundlage eines Werts des vegetativen Indexmerkmals in der vegetativen Indexkarte an der geografischen Position und des Werts der Maiskolbengröße modelliert, die durch den In-situ-Sensor erfasst wird, der der geografischen Position entspricht,
• wobei der prädiktive Kartengenerator die funktionelle prädiktive Karte auf Grundlage der Werte des vegetativen Indexmerkmals in der vegetativen Indexkarte und auf Grundlage des prädiktiven Modells erzeugt.

Example 7 is the agricultural work machine of any or all of the preceding examples, the map being a vegetative index map which contains values of a vegetative index characteristic as values of the agricultural characteristic, and further comprising:

• a predictive model generator that generates a predictive model that models a relationship between the vegetative index trait and the cob size based on a value of the vegetative index trait in the vegetative index map at the geographic location and the value of the corn cob size detected by the in situ sensor is recorded, which corresponds to the geographical position,
• wherein the predictive map generator generates the functional predictive map based on the values of the vegetative index feature in the vegetative index map and based on the predictive model.

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

Example 8 is the agricultural work machine of any or all of the preceding examples, wherein the map is a yield map that includes values of a yield trait as values of the agricultural trait, and further comprising:

• a predictive model generator that generates a predictive model that models a relationship between the yield trait and the corn cob size based on a value of the yield trait in the yield map at the geographic location and the value of the corn cob size detected by the in situ sensor, which corresponds to the geographic position,
• wherein the predictive map generator generates the functional predictive map based on the values of the yield trait in the yield map and based on the predictive model.

• einen prädiktiven Modellgenerator, der ein prädiktives Modell erzeugt, das eine Beziehung zwischen dem Aussaatmerkmal und der Maiskolbengröße auf Grundlage eines Werts des Aussaatmerkmals in der Aussaatkarte an der geografischen Position einen Wert der Maiskolbengröße modelliert, der durch den In-situ-Sensor erkannt wird, der der geografischen Position entspricht,
• wobei der prädiktive Kartengenerator die funktionelle prädiktive Karte auf Grundlage der Werte des Aussaatmerkmals in der Aussaatkarte und auf Grundlage des prädiktiven Modells erzeugt.

Example 9 is the agricultural work machine of any or all of the preceding examples, wherein the map is a sowing map that includes values of a sowing characteristic as values of the agricultural characteristic and further comprises:

• a predictive model generator that generates a predictive model that models a relationship between the seeding characteristic and the corn cob size based on a value of the seeding characteristic in the seeding map at the geographic position a value of the corn cob size detected by the in situ sensor, the corresponds to the geographical position,
• wherein the predictive map generator generates the functional predictive map based on the values of the seeding feature in the seeding map and based on the predictive model.

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

Example 10 is the agricultural work machine of any or all of the preceding examples, the control system further comprising:

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

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

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

• obtaining a map including values of an agricultural feature corresponding to different geographic locations in a field;
• detecting a geographic location of the agricultural work machine;
• detecting a value of a corn cob size corresponding to the geographic location with an in situ sensor;
• generating a functional predictive map of the field that maps predictive control values to the different geographic locations in the field based on the values of the agricultural feature in the map and based on the value of the cob size to the different geographic locations in the field; and
• Controlling a controllable subsystem based on the geographic location of the agricultural work machine and based on the control values in the functional predictive map.

• Erzeugen einer funktionellen prädiktiven Maiskolbengrößenkarte, die prädiktive Maiskolbengrößen auf die verschiedenen geografischen Positionen in dem Feld abbildet.
• Beispiel 13 ist das computerimplementierte Verfahren eines oder aller vorhergehenden Beispiele, wobei das Steuern eines steuerbaren Teilsystems Folgendes umfasst:
• Erzeugen eines Deckplattenpositionssteuersignals auf Grundlage der erkannten geografischen Position und der funktionellen prädiktiven Maiskolbengrößenkarte; und
• Steuern des steuerbaren Teilsystems auf Grundlage des Deckplattenpositionssteuersignals, um einen Abstand zwischen einem mindestens einen Satz von Deckplatten auf der landwirtschaftlichen Arbeitsmaschine zu steuern.

Example 12 is the computer-implemented method of any or all of the preceding examples, wherein generating a functional predictive map comprises:

• generating a functional predictive corn cob size map that maps predictive corn cob sizes to the various geographic locations in the field.
• Example 13 is the computer-implemented method of any or all of the preceding examples, wherein controlling a controllable subsystem comprises:
• generating a top plate position control signal based on the detected geographic position and the functional predictive corn cob size map; and
• controlling the controllable subsystem based on the deck position control signal to control a spacing between at least one set of decks on the agricultural work machine.

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

Example 14 is the computer-implemented method of any or all of the preceding examples, wherein generating a functional predictive map comprises:

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

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

Example 15 is the computer-implemented method of any or all of the preceding examples, wherein controlling the controllable subsystem comprises:

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

• Erzeugen eines prädiktiven Modells, das eine Beziehung zwischen dem landwirtschaftlichen Merkmal und der Maiskolbengröße auf Grundlage eines Werts des landwirtschaftlichen Merkmals in der Karte an der geografischen Position und des Werts der Maiskolbengröße modelliert, die durch den In-situ-Sensor erkannt wird, der der geografischen Position entspricht,
• wobei der prädiktive Kartengenerator die funktionelle prädiktive Karte auf Grundlage der Werte des landwirtschaftlichen Merkmals in der Karte und auf Grundlage des prädiktiven Modells erzeugt.

Example 16 is the agricultural work machine of any or all of the preceding examples and further includes:

• Generating a predictive model that models a relationship between the agricultural feature and the cob size based on a value of the agricultural feature in the map at the geographic location and the value of the cob size detected by the in situ sensor corresponding to the geographic position corresponds to
• wherein the predictive map generator generates the functional predictive map based on the values of the agricultural feature in the map and based on the predictive model.

• Erzeugen eines prädiktiven Modells, das eine Beziehung zwischen dem vegetativen Indexmerkmal und der Maiskolbengröße modelliert, auf Grundlage eines Werts des vegetativen Indexmerkmals in der vegetativen Indexkarte an der geografischen Position und des Werts der Maiskolbengröße, der durch den In-situ-Sensor erfasst wird, der der geografischen Position entspricht,
• wobei das Erzeugen der funktionellen prädiktiven Karte das Erzeugen der funktionellen prädiktiven Karte auf Grundlage der Werte des vegetativen Indexmerkmals in der vegetativen Indexkarte und auf Grundlage des prädiktiven Modells umfasst.

Example 17 is the computer-implemented method of any or all of the preceding examples, wherein the map is a vegetative index map including as agricultural trait values values of a vegetative index trait, and further comprising:

• Generating a predictive model that models a relationship between the vegetative index trait and the corncob size, based on a value of the vegetative index trait in the vegetative index map at the geographical position and the value of the corncob size detected by the in situ sensor, the corresponds to the geographical position,
• wherein generating the functional predictive map comprises generating the functional predictive map based on the values of the vegetative index feature in the vegetative index map and based on the predictive model.

• Erzeugen eines prädiktiven Modells, das eine Beziehung zwischen dem Ertragsmerkmal und der Maiskolbengröße auf Grundlage eines Werts des Ertragsmerkmals in der Ertragskarte an der geografischen Position und eines Werts der Maiskolbengröße modelliert, die durch den In-situ-Sensor erfasst wird, der der geografischen Position entspricht,
• wobei das Erzeugen der funktionellen prädiktiven Karte das Erzeugen der funktionellen prädiktiven Karte auf Grundlage der Werte des Ertragsmerkmals in der Ertragskarte und auf Grundlage des prädiktiven Modells umfasst.

Example 18 is the computer-implemented method of any or all of the preceding examples, wherein the map is a yield map including as agricultural trait values values of a yield trait and further comprising:

• Generating a predictive model that models a relationship between the yield-related trait and the cob size based on a value of the yield-related trait in the yield map at the geographic location and a value of the cob size detected by the in situ sensor corresponding to the geographic location ,
• wherein generating the functional predictive map comprises generating the functional predictive map based on the values of the yield trait in the yield map and based on the predictive model.

• Erzeugen eines prädiktiven Modells, das eine Beziehung zwischen dem Aussaatmerkmal und der Maiskolbengröße auf Grundlage eines Werts des Aussaatmerkmals in der Aussaatkarte an der geografischen Position und eines Werts der Maiskolbengröße modelliert, die durch den In-situ-Sensor erkannt wird, der der geografischen Position entspricht,
• wobei das Erzeugen der funktionellen prädiktiven Karte das Erzeugen der funktionellen prädiktiven Karte auf Grundlage der Werte des Aussaatmerkmals in der Aussaatkarte und auf Grundlage des prädiktiven Modells umfasst.

Example 19 is the computer-implemented method of any or all of the preceding examples, wherein the map is a seed map including as agricultural trait values values of a seed trait, and further comprising:

• Generating a predictive model that models a relationship between the seed trait and the corn cob size based on a value of the seed trait in the seed map at the geographic location and a value of the corn cob size detected by the in situ sensor corresponding to the geographic location ,
• wherein generating the functional predictive map comprises generating the functional predictive map based on the values of the seeding feature in the seeding map and based on the predictive model.

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

Example 20 is an agricultural work machine comprising:

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

Although the subject matter has been described in language specific to structural features or methodical acts, it should be understood that 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) receiving a map (258) including values of an agricultural characteristic corresponding to different geographic locations in a field; a geographic position sensor (204) that detects a geographic position of the agricultural work machine (100); an in situ sensor (208) that detects a value of a corn cob size corresponding to the geographic location; a predictive map generator (212) that generates a functional predictive map of the field that maps predictive control values to the various geographic locations in the field based on the values of the agricultural feature in the map and based on the value of the cob size; a controllable subsystem (216); and a control system (214) that generates a control signal to control the controllable subsystem (216) based on the geographical position of the agricultural work machine (100) and based on the predictive control values in the functional predictive map. Agricultural working machine after claim 1 wherein the predictive map generator comprises: a predictive corn head size map generator that generates, as the functional predictive map, a functional predictive corn head size map that maps predictive corn head sizes as predictive control values to the various geographic locations in the field. Agricultural working machine after claim 2 , wherein the control system comprises: a deck plate position controller that generates a deck plate position control signal based on the detected geographic location and the functional predictive corn head size map and controls the controllable subsystem based on the deck plate position control signal to adjust a spacing between at least one set of deck plates on the agricultural work machine Taxes. Agricultural working machine after claim 1 wherein the predictive map generator comprises: an operator command predictive map generator that generates a functional predictive operator command map as the functional predictive map that maps predictive operator commands to the various geographic locations in the field. Agricultural working machine after claim 4 , wherein the control system comprises: an adjustment controller that generates an operator command control signal indicative of an operator command based on the detected geographic location and the functional predictive operator command map, and controls the controllable subsystem based on the operator command control signal to execute the operator command. Agricultural working machine after claim 1 and further comprising: a predictive model generator that generates a predictive model that models a relationship between the agricultural feature and the corn cob size based on a value of the agricultural feature in the map at the geographical position and the value of the corn cob size generated by the in- situ sensor corresponding to the geographic position, the predictive map generator generating the functional predictive map based on the values of the agricultural feature in the map and based on the predictive model. Agricultural working machine after claim 1 , wherein the map is a vegetative index map including as values of the agricultural trait values of a vegetative index trait, and further comprising: a predictive model generator that generates a predictive model representing a relationship between the vegetative index trait and the corn cob size based on a value of the vegetative index feature in the vegetative index map is modeled at the geographical position and the value of the corn cob size detected by the in situ sensor corresponding to the geographical position, the predictive map generator generating the functional predictive map based on the values of the vegetative index feature in generated from the vegetative index map and based on the predictive model. Agricultural working machine after claim 1 , wherein the map is a yield map including values of a yield-related trait as values of the agricultural trait, and further comprising: a predictive model generator that generates a predictive model representing a relationship between the yield-related trait and the cob size based on a value of the yield-related trait in the Yield map is modeled at the geographic location and the value of the corn cob size detected by the in situ sensor corresponding to the geographic location, the predictive map generator generating the functional predictive map based on the values of the yield trait in the yield map and based on the predictive model generated. Agricultural working machine after claim 1 , wherein the map is a sowing map including values of a sowing characteristic as values of the agricultural characteristic, and further comprising: a predictive model generator that generates a predictive model showing a relationship between the sowing characteristic and the cob size based on a value of the sowing characteristic in the Seeding map at the geographic position models a value of the corn cob size detected by the in situ sensor corresponding to the geographic position, the predictive map generator generating the functional predictive map based on the values of the seeding characteristic in the seeding map and based on the predictive model generated. Agricultural working machine after claim 1 , wherein the control system further comprises: an operator interface controller that generates a user interface map representation of the functional predictive map, the user interface map representation comprising a field portion with one or more markers that indicate the predictive control values at one or more geographic locations on the field portion. A computer-implemented method for controlling an agricultural working machine (100), comprising: obtaining a map (258) including agricultural feature values corresponding to different geographic locations in a field; detecting a geographic position of the agricultural work machine (100); detecting a value of a corn cob size that corresponds to the geographic location; with an in situ sensor (208); generating a functional predictive map of the field that maps predictive control values to the different geographic locations in the field based on the values of the agricultural feature in the map and based on the value of the cob size to the different geographic locations in the field; and Controlling a controllable subsystem (216) based on the geographical position of the agricultural work machine (100) and based on the control values in the functional predictive map. Computer-implemented method claim 11 wherein generating a functional predictive map comprises: generating a functional predictive corn cob size map mapping predictive corn cob sizes to the various geographic locations in the field. Computer-implemented method claim 12 , wherein controlling a controllable subsystem comprises: generating a top plate position control signal based on the detected geographic location and the functional predictive corn cob size map; and controlling the controllable subsystem based on the deck position control signal to control a spacing between at least one set of decks on the agricultural work machine. Computer-implemented method claim 11 wherein generating a functional predictive map comprises: generating a functional predictive operator command map that maps predictive operator commands to the various geographic locations in the field. Agricultural working machine (100), comprising a communication system (206) receiving a map (258) including values of an agricultural characteristic corresponding to different geographic locations in a field; a geographic position sensor (204) that detects a geographic position of the agricultural work machine (100); an in situ sensor (208) that detects a value of a corn cob size corresponding to the geographic location; a predictive model generator (210) that generates a predictive model that models a relationship between the agricultural feature and the corncob size based on a value of the agricultural feature in the map (258) at the geographic location and the value of the corncob size represented by the in situ sensor corresponding to geographic position is detected; a predictive map generator (212) that generates a functional predictive map of the field mapping predictive control values to the various geographic locations in the field based on the values of the agricultural feature in the map (258) and based on the predictive model; a controllable subsystem (216); and a control system (214) that generates a control signal to control the controllable subsystem (216) based on the geographical position of the agricultural work machine (100) and based on the control values in the functional predictive map.

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