Predictive map generation and control system

Abstract

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural property values at different geographic locations of a field. An on-board sensor on the agricultural work machine senses the agricultural property as the agricultural work machine moves through the field. A prediction map generator generates a prediction map that predicts the agricultural property at different locations in the field based on a relationship between values in the one or more information maps and the agricultural property sensed by the on-board sensor. The prediction map can be output and used for automated machine control.

Description

TECHNICAL FIELD

[0001] This description relates to agricultural machines, forestry machines, construction machines, and turf management machines. BACKGROUND

[0002] There are various different types of agricultural machines. Some agricultural machines include harvesters, such as combine harvesters, sugar cane harvesters, cotton harvesters, self-propelled forage harvesters, and swathers. Some harvesters can also be equipped with different types of headers to harvest different types of crops.

[0003] Agricultural harvesters typically include an engine or other power source that produces a limited amount of power. The power produced is provided to various subsystems of the agricultural harvester.

[0004] The above discussion is merely provided as general background information and is not intended to aid in the determination of the scope of the claimed subject matter. SUMMARY

[0005] One or more information maps are obtained by the agricultural work machine. The one or more information maps map one or more agricultural property values at different geographic locations of a field. Onboard sensors of the agricultural work machine sense the agricultural property as the agricultural work machine moves through the field. A prediction map generator generates a prediction map that predicts the agricultural property at different locations in the field based on a relationship between values in the one or more information maps and the agricultural property sensed by the onboard sensors. The prediction map can be output and used for automated machine control.

[0006] This Summary is provided to introduce some concepts in a simplified form that are further described below in the DETAILED DESCRIPTION. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used in determining the scope of the claimed subject matter. The claimed subject matter is not limited to implementations that solve any or all disadvantages noted in the background. BRIEF DESCRIPTION OF DRAWINGS

[0007] Figure 1 is a partial schematic view of an example of a combine harvester.

[0008] Figure 2 is a block diagram showing some parts of an agricultural harvester in more detail according to some examples of the present disclosure.

[0009] Figures 3A-3B (Hereinafter referred to collectively as FIG. 3) shows a flowchart illustrating an example of the operation of an agricultural harvester in generating a map.

[0010] Figure 4 is a block diagram showing one example of a prediction model generator and a prediction metric map generator.

[0011] is a partial schematic view of an example of a combine harvester.Figure 5 is a flowchart illustrating an example of operations of an agricultural harvester in receiving a vegetation index map, a crop moisture map, a soil property map, a topography map, a predicted yield map or a predicted biomass map, detecting power characteristics, and generating a functional predicted power map for controlling the agricultural harvester during a harvesting operation.

[0012] Figure 6A is a block diagram illustrating one example of a prediction model generator and a prediction map generator.

[0013] Figure 6B is a block diagram illustrating some examples of field sensors.

[0014] Figure 7 is a flowchart illustrating one example of operations of an agricultural harvester including using an information map and field sensor inputs to generate a functional predicted power map.

[0015] Figure 8 is a block diagram illustrating one example of a control zone generator.

[0016] Figure 9 is a flowchart illustrating one example of operations of the control zone generator illustrated in Figure 8

[0017] Figure 10 is a flowchart illustrating one example of operations of a control system in selecting a target setpoint value to control an agricultural harvester.

[0018] Figure 11 is a block diagram illustrating one example of an operator interface controller.

[0019] Figure 12 is a flowchart illustrating one example of an operator interface controller.

[0020] Figure 13 is a schematic diagram illustrating one example of an operator interface display.

[0021] Figure 14 is a block diagram illustrating one example of an agricultural harvester in communication with a remote server environment.

[0022] Figures 15-17 is shown an example of a mobile device that can be used with an agricultural harvester.

[0023] Figure 18 is a block diagram illustrating one example of a computing environment that can be used with an agricultural harvester. DETAILED DESCRIPTION

[0024] ​To facilitate an 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 the same. It will, nevertheless, be understood that no limitation of the scope of the disclosure is intended. Alterations and further modifications of the described devices, systems, methods, and any further applications of the principles of the present disclosure are fully contemplated as would normally occur to one skilled in the art to which the disclosure relates. In particular, it is fully contemplated that the features, components, steps, and / or combinations thereof described with respect to one example can be combined with the features, components, steps, and / or combinations thereof described with respect to any of the other examples for the present disclosure.

[0025] The present specification relates to generating a prediction map, and more specifically a prediction power map, using in-field data acquired contemporaneously with an agricultural operation in conjunction with predictive data or prior data (previous data). In some examples, the prediction power map can be used to control an agricultural work machine, such as an agricultural harvester. As discussed above, the power generation of a harvester is limited and overall performance can be reduced when one or more subsystems have increased power demands.

[0026] Performance of a harvester can be adversely affected based on a number of different criteria. For example, areas of dense crop plants, weeds, or a combination thereof can have an adverse effect on operation of the harvester because the subsystems require more power to process the greater amount of material, including crop plants and weeds. A vegetation index can indicate areas where dense crop plants, weeds, or a combination thereof can be present. Or, for example, crop plants or weeds with a higher moisture content also require more power to process. Or, for example, soil properties (e.g., type or moisture) can affect power usage of the steering and propulsion systems. For example, wet clay can cause additional slippage that reduces efficiency of the drivetrain compared to dry soil. Or, for example, the terrain of a field can change the power characteristics of an agricultural harvester. For example, when the harvester is climbing a hill, some power needs to be diverted to the propulsion system to maintain a constant speed. Or, for example, areas of a field with a higher grain yield can require more power to be diverted to the crop processing subsystems. Or, for example, areas of a field that contain a large amount of biomass can require more power to be diverted to the crop processing subsystems.

[0027] Vegetation index maps illustratively map values of a vegetation index (which can be indicative of vegetation growth) at different geographic locations in a field of interest. One example of a vegetation index includes the normalized difference vegetation index (NDVI). There are many other vegetation indices within the scope of the present disclosure. In some examples, a vegetation index can be derived from sensor readings of one or more electromagnetic radiation bands reflected by plant matter. Without limitation, these bands can be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum.

[0028] Vegetation index maps can be used to identify the presence and location of vegetation. In some examples, these maps enable identification and geographic referencing of weeds in the presence of bare soil, crop residue, or other plant matter, including crops or other weeds. For example, at the end of a growing season, when crops are mature, crop plants can exhibit a relatively low level of live growing vegetation. However, weeds typically remain in a growing state after crops have matured. Thus, if a vegetation index map is generated relatively late in the growing season, the vegetation index map can indicate the location of weeds in a field.

[0029] Crop moisture maps illustratively map crop moisture at different geographic locations in a field of interest. In one example, crop moisture can be sensed by an unmanned aerial vehicle (UAV) equipped with a moisture sensor prior to a harvesting operation. As the unmanned aerial vehicle traverses the field, crop moisture readings are geolocated to create a crop moisture map. This is merely one example, and crop moisture maps can also be created in other ways, for example, crop moisture can be predicted for an entire field based on precipitation, soil moisture, or a combination thereof.

[0030] Topography maps illustratively map the height of the ground or other topographical characteristics across different geographic locations in a field of interest. Since ground slope is indicative of a change in height, having two or more height values allows for the calculation of slope across an area with known height values. Greater granularity of slope can be achieved with more areas with known height values. As an agricultural harvester traverses the terrain in a known direction, pitch and roll of the agricultural harvester can be determined based on the slope of the ground (i.e., areas of changing height). Topographical characteristics mentioned below can include, but are not limited to, height, slope (e.g., including machine orientation with respect to slope), and ground contour (e.g., roughness).

[0031] Soil property maps illustratively map soil property values (which can indicate soil type, soil moisture, soil cover, soil structure, and various other soil properties) across different geographic locations in a field of interest. Thus, the soil property maps provide geographically referenced soil properties across the field of interest. Soil type can refer to a taxonomic unit in soil science, where each soil type includes a defined set of shared properties. Soil types can include, for example, sandy loam, clay, silt loam, peat, chalk, loam, and various other soil types. Soil moisture can refer to the amount of water held or otherwise contained in the soil. Soil moisture can also be referred to as soil wetness. Soil cover can refer to the amount of an item or material covering the soil, including vegetative material such as crop residue or cover crop, debris, and various other items or materials. Generally, in agricultural terms, soil cover includes a measure of remaining crop residue, such as the amount of plant stalks remaining, as well as a measure of cover crop. Soil structure can refer to the arrangement of soil solid portions and pore spaces located between the soil solid portions. Soil structure can include the manner in which individual particles (such as individual particles of sand, silt, and clay) are combined. Soil structure can be described in terms of grade (degree of aggregation), class (average size of aggregates), and form (type of aggregates), as well as various other descriptions. These are just examples. Various other characteristics and properties of the soil can be mapped as soil property values on the soil property maps.

[0032] These soil property maps can be generated based on data collected during another operation corresponding to the field of interest, such as a previous agricultural operation of the same season, such as a planting operation or a spraying operation, as well as a previous agricultural operation performed in a past season, such as a previous harvesting operation. The agricultural machines performing those agricultural operations can have on-board sensors that detect characteristics indicative of soil properties, such as characteristics indicative of soil type, soil moisture, soil cover, soil structure, and various other characteristics indicative of various other soil properties. In addition, operational characteristics or machine settings of the agricultural machines during the prior operation (previous operation), as well as other data, can be used to generate the soil property maps. For example, header height data indicative of header heights of an agricultural harvester across different geographic locations in the field of interest during the previous harvesting operation, as well as weather data indicative of weather conditions, such as precipitation data or wind data during the interim (e.g., from the time of the previous harvesting operation and the time of generation of the soil property map), can be used to generate a soil moisture map. For example, by knowing the height of the header, the amount of remaining plant residue (such as crop stalks) can be known or estimated, and can be used in conjunction with the precipitation data to predict the level of soil moisture. This is just one example.

[0033] The present discussion also includes prediction maps that predict characteristics based on the information maps and relationships with field sensors. Two of these maps include a predicted yield map and a predicted biomass map. In one example, the predicted yield map is generated by receiving an a priori vegetation index map and sensing yield during a harvesting operation, determining a relationship between the a priori vegetation index map and the yield sensor signal, and generating a predicted yield map using the relationship based on the a priori vegetation index map and the relationship. In one example, the predicted biomass map is generated by receiving an a priori vegetation index map and sensing biomass, determining a relationship between the a priori vegetation index map and the biomass sensor signal, and generating a predicted biomass map using the relationship based on the a priori vegetation index map and the relationship. The predicted yield map and the predicted biomass map can be created or otherwise generated based on other information maps. For example, the predicted yield map and the predicted biomass map can be generated based on satellites or growth models.

[0034] Accordingly, the present discussion is directed to examples in which the system receives one or more of a vegetation index map, a weed map, a crop moisture map, a soil property map, a topography map, a predicted yield map, or a predicted biomass map, and also detects variables indicative of crop conditions using field sensors during a harvesting operation. The system generates a model that models relationships between vegetation index values, crop moisture values, soil property values, predicted yield values, or predicted biomass values from the plurality of maps and field data from the field sensors. The model is used to generate a functional predicted power map that functions to generate an expected power characteristic of an agricultural harvester in a field. The functional predicted power characteristic map generated during a harvesting operation can be presented to an operator or other user or to an agricultural harvester for automatic control during a harvesting operation, or both.

[0035] Figure 1 is a partial schematic view of a self-propelled agricultural harvester 100. In the example shown, the agricultural harvester 100 is a combine harvester. Additionally, while a combine harvester is provided as an example throughout this disclosure, it should be understood that the present description also applies to other types of harvesters, such as a cotton harvester, a sugarcane harvester, a self-propelled forage harvester, a swather, or other agricultural work machines. Accordingly, the present disclosure is intended to encompass the various types of harvesters described, and is therefore not limited to a combine harvester. Moreover, the present disclosure is directed to other types of work machines, such as agricultural planters and sprayers, construction equipment, forestry equipment, and lawn care equipment, in which the generation of prediction maps can be applied. Accordingly, the present disclosure is intended to encompass these various types of harvesters and other work machines, and is therefore not limited to a combine harvester.

[0036] As Figure 1As shown, the agricultural harvester 100 illustratively includes an operator compartment 101 that can have various different operator interface mechanisms for controlling the agricultural harvester 100. The agricultural harvester 100 includes front end equipment such as a header 102 and a cutter 104 generally indicated. The agricultural harvester 100 also includes a feedhouse 106, a feed accelerator 108, and a threshing machine generally indicated at 110. The feedhouse 106 and the feed accelerator 108 form part of a material handling subsystem 125. The header 102 is pivotally coupled to a frame 103 of the agricultural harvester 100 along a pivot axis 105. One or more actuators 107 drive the header 102 to move about the axis 105 in a direction generally indicated by arrow 109. Thus, a vertical position (header height) of the header 102 above a ground surface 111 on which the header 102 travels is controllable by actuating the actuators 107. Although not shown in Figure 1 FIG. 1, the agricultural harvester 100 can also include one or more actuators that operate to apply a tilt angle, a roll angle, or both, to the header 102 or portions of the header 102. Tilt refers to an angle at which the cutter 104 engages the crop. For example, the tilt angle is increased by controlling the header 102 to point a distal edge 113 of the cutter 104 more toward the ground surface. The tilt angle is decreased by controlling the header 102 to point the distal edge 113 of the cutter 104 more away from the ground surface. Roll angle refers to an orientation of the header 102 about a fore-aft longitudinal axis of the agricultural harvester 100.

[0037] The threshing machine 110 illustratively includes a threshing rotor 112 and a set of concaves 114. In addition, the agricultural harvester 100 also includes a separator 116. The agricultural harvester 100 also includes a grain cleaning subsystem or grain cleaning house (collectively, a grain cleaning subsystem 118) that includes a grain cleaning fan 120, a chaffer 122, and a sieve 124. The material handling subsystem 125 also includes an unloading threshing cylinder 126, a tailings elevator 128, a clean grain elevator 130, and an unloading auger 134 and a spout 136. The clean grain elevator moves clean grain into a clean grain tank 132. The agricultural harvester 100 also includes a residue subsystem 138 that can include a chopper 140 and a spreader 142. The agricultural harvester 100 also includes a propulsion subsystem that includes an engine that drives ground engaging components 144 (e.g., wheels or tracks). In some examples, a combine within the scope of the present disclosure can have more than one of any of the above-mentioned subsystems. In some examples, the agricultural harvester 100 can have a left grain cleaning subsystem and a right grain cleaning subsystem, a separator, etc., which are not shown in Figure 1 FIG. 1.

[0038] In operation, and as outlined, the agricultural harvester 100 illustratively moves through a field in a direction indicated by arrow 147. As the agricultural harvester 100 moves, the header 102 (and associated reel 164) engages the crop to be harvested and gathers the crop toward the cutter 104. The operator of the agricultural harvester 100 can be a local human operator, a remote human operator, or an automated system. The operator of the agricultural harvester 100 can determine one or more of a height setting, a tilt angle setting, or a roll angle setting of the header 102. For example, the operator inputs one or more settings to a control system that controls the actuator 107 (described in more detail below). The control system can also receive settings from the operator to establish the tilt angle and roll angle of the header 102 and implement the input settings by controlling associated actuators (not shown) that operate to change the tilt angle and roll angle of the header 102. The actuator 107 maintains the header 102 at a height above the ground 111 based on the height setting, and, where applicable, at a desired tilt and roll angle. Each of the height, roll, and tilt settings can be implemented independently of the other settings. The control system responds to header errors (e.g., a difference between the height setting and a measured height of the header 104 above the ground 111, and in some examples, tilt angle and roll angle errors) with a responsiveness determined based on a selected sensitivity level. If the sensitivity level is set at a higher sensitivity level, the control system responds to smaller header position errors and attempts to reduce detected errors more quickly than if the sensitivity level is at a lower sensitivity level.

[0039] Returning to the description of the operation of the agricultural harvester 100, after the crop is cut by the cutter 104, the cut crop material moves through the feeder house 106 toward the feed accelerator 108, which accelerates the crop material into the threshing machine 110. The crop material 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, where a portion of the straw is moved by the unloading threshing cylinder 126 toward the straw sub-system 138. The portion of the straw that is conveyed to the straw sub-system 138 is chopped by the straw chopper 140 and spread on the field by the spreader 142. In other configurations, the straw is released from the agricultural harvester 100 into a pile. In other examples, the straw sub-system 138 can include a weed seed rejector (not shown), such as a seed bagger or other seed collector, or a seed crusher or other seed destroyer.

[0040] The grain falls to the clean grain subsystem 118. The chaffer 122 separates some of the larger pieces of material from the grain, and the sieve 124 separates some of the finer pieces of material from the clean grain. The clean grain falls onto an auger that moves the grain to the inlet end of the clean grain elevator 130, and the clean grain elevator 130 moves the clean grain upward, storing the clean grain in the clean grain bin 132. The chaff is removed from the clean grain subsystem 118 by the airflow generated by the clean grain fan 120. The clean grain fan 120 directs air up through the sieve and the chaffer along an airflow path. The airflow carries the chaff in the agricultural harvester 100 rearward toward the chaff handling subsystem 138.

[0041] The tailings elevator 128 returns the tailings to the threshing machine 110, where the tailings are re-threshed. Alternatively, the tailings can also be passed to a separate re-threshing mechanism by the tailings elevator or another transport device, where the tailings are also re-threshed.

[0042] Figure 1 It is also shown in one example that the agricultural harvester 100 includes a ground speed sensor 146, one or more separator loss sensors 148, a clean grain camera 150, a forward view image capture mechanism 151 that can be in the form of a stereo camera or a monocular camera, and one or more loss sensors 152 disposed in the clean grain subsystem 118.

[0043] The ground speed sensor 146 senses the speed of travel of the agricultural harvester 100 over the ground. The ground speed sensor 146 can sense the speed of travel of the agricultural harvester 100 by sensing the rotational speed of ground-engaging components such as wheels or tracks, drive shafts, axles, or other components. In some cases, a positioning system can be used to sense the speed of travel, such as a global positioning system (GPS), a dead reckoning system, a LORAN system, or various other systems or sensors that provide an indication of the speed of travel.

[0044] The loss sensors 152 schematically provide output signals indicative of the amount of grain loss occurring in the right and left sides of the clean grain subsystem 118. In some examples, the sensors 152 are impact sensors that count the grain impacts per unit of time or per unit of travel distance to provide an indication of the grain loss occurring at the clean grain subsystem 118. The impact sensors for the right and left sides of the clean grain subsystem 118 can provide separate signals or combined or aggregated signals. In some examples, the sensors 152 can include a single sensor rather than providing separate sensors for each clean grain subsystem 118.

[0045] The separator loss sensors 148 provide signals indicative of grain loss in the left and right separators (not shown separately in FIG. 1). The separator loss sensors 148 can be associated with the left and right separators and can provide separate grain loss signals or a combined or aggregated signal. In some cases, sensing grain loss in the separators can also be performed using various different types of sensors. Figure 1

[0046] The agricultural harvester 100 can also include other sensors and measurement mechanisms. For example, the agricultural harvester 100 can include one or more of the following sensors: a header height sensor that senses a height of the header 102 above the ground 111; a stability sensor that senses a vibration or bounce (and amplitude) of the agricultural harvester 100; a residue setting sensor configured to sense whether the agricultural harvester 100 is configured to chop residue, produce a windrow, etc.; a clean grain bin fan speed sensor to sense a fan 120 speed; a concave gap sensor that senses a gap between the rotor 112 and the concave 114; a threshing rotor speed sensor that senses a rotor speed of the rotor 112; a chaffer screen gap sensor that senses a size of openings in the chaffer screen 122; a screen mesh gap sensor that senses a size of openings in the screen mesh 124; a material other than grain (MOG) moisture sensor that senses a moisture level of MOG passing through the agricultural harvester 100; one or more machine setting sensors configured to sense various configurable settings of the agricultural harvester 100; a machine orientation sensor that senses an orientation of the agricultural harvester 100; and a crop property sensor that senses various different types of crop properties, such as crop type, crop moisture, and other crop properties. The crop property sensor can also be configured to sense properties of cut crop material as the crop material is processed by the agricultural harvester 100. For example, in some cases, the crop property sensor can sense: grain quality, such as broken grain, MOG levels; grain constituents, such as starch and protein; and a grain feed rate as grain travels through the feedhouse 106, the clean grain elevator 130, or other places in the agricultural harvester 100. The crop property sensor can also sense a feed rate of biomass through the feedhouse 106, the separator 116, or other places in the agricultural harvester 100. The crop property sensor can also sense a feed rate through the elevator 130 or through other portions of the agricultural harvester 100 as a grain mass flow rate, or provide other output signals indicative of other sensed variables.

[0047] ​Examples of sensors for detecting or sensing power characteristics include, but are not limited to, voltage sensors, current sensors, torque sensors, fluid pressure sensors, fluid flow sensors, force sensors, bearing load sensors, and rotation sensors. Power characteristics can be measured at different levels of granularity. For example, power usage can be sensed at a machine-wide level, at a subsystem-wide level, or through individual components of a subsystem.

[0048] Before describing how the agricultural harvester 100 generates a functional predictive power map and uses the functional predictive power map for control, a brief description of some items on the agricultural harvester 100 and their operation will first be described. Figure 2 And the description of FIG. 3 describes: receiving a general type of information map and combining information from the information map with georeferenced sensor signals generated by in-field sensors, where the sensor signals are indicative of characteristics in a field, such as power characteristics of an agricultural harvester. The characteristics of the field can include, but are not limited to, characteristics of the field (such as slope, weed intensity, weed type, soil moisture, surface quality); characteristics of crop properties (such as crop height, crop moisture, crop density, crop condition); characteristics of grain properties (such as grain moisture, grain size, grain test weight); and characteristics of machine performance (such as loss level, work quality, fuel consumption, and power utilization). Relationships between characteristic values obtained from the in-field sensor signals and information map values are identified, and the relationships are used to generate a new functional predictive map. The functional predictive map predicts values at different geographic locations in the field, and one or more of the values can be used to control a machine, such as one or more subsystems of an agricultural harvester. In some cases, the functional predictive map can be presented to a user, such as an operator of an agricultural work machine, which can be an agricultural harvester. The functional predictive map can be presented to the user in a visual manner (such as through a display), in a tactile manner, or in an audible manner. The user can interact with the functional predictive map to perform editing operations and other user interface operations. In some cases, the functional predictive map can be used to control an agricultural work machine (such as an agricultural harvester), presented to an operator or other user, and presented to an operator or user for operator or user interaction, one or more of.

[0049] In reference to Figure 2 And the description of FIG. 3 describes a general method, reference is made to Figure 4 And Figure 5 A more specific method for generating a functional predictive power characteristic map is described, which functional predictive weed map can be presented to an operator or user, or used to control the agricultural harvester 100, or both. Again, although this discussion is directed to an agricultural harvester, and in particular a combine harvester, the scope of the present disclosure encompasses other types of agricultural harvesters or other agricultural work machines.

[0050] Figure 2 is a block diagram showing some portions of an example agricultural harvester 100. Figure 2 The agricultural harvester 100 is shown to schematically include one or more processors or servers 201, data storage 202, a geo-location sensor 204, a communication system 206, and one or more field sensors 208 that sense one or more agricultural properties of a field while the harvesting operation is in progress. Agricultural properties can include any property that can have an influence on the harvesting operation. Some examples of agricultural properties include properties of the harvesting machine, the field, the plants on the field, and the weather. Other types of agricultural properties are also included. Agricultural properties can include any property that can have an influence on the harvesting operation. Some examples of agricultural properties include properties of the harvesting machine, the field, the plants on the field, the weather, etc. The field sensors 208 generate values corresponding to the sensed properties. The agricultural harvester 100 also includes a prediction model or relationship generator (hereinafter collectively referred to as "prediction model generator 210"), a prediction 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 can also include a variety of other agricultural harvester functions 220. The field sensors 208 include, for example, on-board sensors 222, remote sensors 224, and other sensors 226 that sense properties of the field during the course of the agricultural operation. The prediction model generator 210 schematically includes an information variable to field variable model generator 228, and the prediction model generator 210 can include other items 230. The control system 214 includes a communication system controller 229, an operator interface controller 231, a settings controller 232, a path planning controller 234, a cooling controller 235, a feed rate controller 236, a header and reel controller 238, a belt conveyor belt controller 240, a table deck position controller 242, a residue system controller 244, a machine cleanout controller 245, a zone controller 247, and the system 214 can include other items 246. The controllable subsystems 216 include machine and header actuators 248, a propulsion subsystem 250, a steering subsystem 252, a residue subsystem 138, a machine cleanout subsystem 254, and the subsystems 216 can include a variety of other subsystems 256.

[0051] Figure 2 The agricultural harvester 100 is also shown to receive an information map 258. As described below, the information map 258 includes, for example, a vegetation index map or a vegetation map from a prior operation. However, the information map 258 can also encompass other types of data obtained prior to the harvesting operation or maps from prior operations. Figure 2An operator 260 is also shown as operating the agricultural harvester 100. The operator 260 interacts with the operator interface mechanism 218. In some examples, the operator interface mechanism 218 can include joysticks, levers, steering wheels, linkages, pedals, buttons, dials, keypads, user-actuatable elements on a user interface display device (such as icons, buttons, etc.), microphones and speakers (where voice recognition and speech synthesis are provided), and various other types of control devices. In cases where a touch-sensitive display system is provided, the operator 260 can interact with the operator interface mechanism 218 using touch gestures. These examples are provided as illustrative examples and are not intended to limit the scope of the present disclosure. Thus, other types of operator interface mechanisms 218 can be used and are within the scope of the present disclosure.

[0052] The information map 258 can be downloaded onto the agricultural harvester 100 and stored in the data storage 202 using the communication system 206 or otherwise. In some examples, the communication system 206 can 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 of networks. The communication system 206 can 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.

[0053] The geo-location sensor 204 illustratively senses or detects a geo-location or position of the agricultural harvester 100. The geo-location sensor 204 can include, but is not limited to, a global navigation satellite system (GNSS) receiver that receives signals from GNSS satellite transmitters. The geo-location sensor 204 can also include a real-time kinematic (RTK) component configured to improve the accuracy of position data derived from GNSS signals. The geo-location sensor 204 can include a dead reckoning system, a cellular triangulation system, or any of a variety of other geo-location sensors.

[0054] The field sensors 208 can be any of the field sensors described above with reference to the field sensors 208 of the agricultural harvester 100 of FIG. 1. Figure 1Any of the sensors described. The on-site sensors 208 include on-board sensors 222 installed on the on-board agricultural harvester 100. Such sensors can include, for example, perception sensors (e.g., forward looking monocular or stereo camera systems and image processing systems), image sensors inside the agricultural harvester 100 such as one or more clean grain cameras installed to identify weed seeds exiting the agricultural harvester 100 through a residue removal system or from a clean grain system. The on-site sensors 208 also include remote on-site sensors 224 that capture on-site information. On-site data includes data acquired from sensors mounted on the harvester or data acquired by any sensor that detects data during the harvesting operation.

[0055] After retrieval by the agricultural harvester 100, the prior information map selector 209 can filter or select one or more specific prior information maps 258 as the prior information map for use by the predictive model generator 210. In one example, the prior information map selector 209 selects a map based on a comparison of contextual information in the prior information map to the current contextual information. For example, a historical yield map can be selected from a year in the past several years in which the weather conditions of the growing season were similar to the weather conditions of the current year. Or, for example, a historical yield map can be selected from a year in the past several years when the contextual information is dissimilar. For example, a historical yield map can be selected for a previous year that was "dry" (i.e., had drought or reduced precipitation conditions) while the current year is "wet" (i.e., has increased precipitation or flooding conditions). While this relationship can be the opposite, it can still be a useful historical relationship. For example, areas that were flooded in a wet year can be areas that have higher yields in dry years because these areas can retain more water in dry years. The current contextual information can include contextual information beyond the immediate contextual information. For example, the current contextual information can include, but is not limited to, a set of information corresponding to the current growing season, a set of data corresponding to the winter season before the current growing season, or a set of data corresponding to the past several years, and so on.

[0056] Contextual information can also be used for statistics between areas with similar contextual characteristics, whether or not the geographic locations correspond to the same locations on the information map 258. For example, historical yield values from areas with similar soil types in other fields can be used as information maps 258 to create a predictive yield map. For example, contextual characteristic information associated with different locations can be applied to locations on the information map 258 that have similar characteristic information.

[0057] The predictive model generator 210 generates a model indicative of a relationship between values sensed by the field sensors 208 and the metrics mapped to the field by the information map 258. For example, if the information map 258 maps vegetation index values to different locations in the field, and the field sensors 208 sense values indicative of header power usage, the information variable to field variable model generator 228 generates a predictive power model modeling a relationship between vegetation index values and header power usage values. The predictive power model can also be generated based on vegetation index values from the information map 258 and multiple field data values generated by the field sensors 208. The predictive map generator 212 then generates a functional predictive power map using the predictive power model generated by the predictive model generator 210 that predicts values of power characteristics (such as power used by a subsystem) sensed by the field sensors 208 at different locations in the field based on the information map 258.

[0058] In some examples, the type of values in the functional predictive map 263 can be the same as the type of field data sensed by the field sensors 208. In some cases, the type of values in the functional predictive map 263 can have different units than the data sensed by the field sensors 208. In some examples, the type of values in the functional predictive map 263 can be different than the type of data sensed by the field sensors 208, but related to the type of data sensed by the field sensors 208. For example, in some examples, the type of data sensed by the field sensors 208 can dictate the type of values in the functional predictive map 263. In some examples, the type of data in the functional predictive map 263 can be different than the type of data in the information map 258. In some cases, the type of data in the functional predictive map 263 can have different units than the data in the information map 258. In some examples, the type of data in the functional predictive map 263 can be different than the type of data in the information map 258, but related to the type of data in the information map 258. For example, in some examples, the type of data in the information map 258 can dictate the type of data in the functional predictive map 263. In some examples, the type of data in the functional predictive map 263 is different than one or both of the type of field data sensed by the field sensors 208 and the type of data in the information map 258. In some examples, the type of data in the functional predictive map 263 is the same as one or both of the type of field data sensed by the field sensors 208 and the type of data in the information map 258. In some examples, the type of data in the functional predictive map 263 is the same as one of the type of field data sensed by the field sensors 208 or the type of data in the information map 258, and different than the other.

[0059] Continuing with the previous example, where Infographic 258 is a vegetation index map and field sensor 208 senses values ​​indicating header power usage, prediction map generator 212 can use the vegetation index values ​​from Infographic 258 and the model generated by prediction model generator 210 to generate a functional prediction map 263 predicting header power usage at different locations in the field. Prediction map generator 212 therefore outputs prediction map 264.

[0060] like Figure 2 As shown, prediction map 264 predicts the values ​​of sensed characteristics (sensed by field sensors 208) or characteristics related to sensed characteristics at various locations on the field, based on information values ​​at different locations in information map 258 and a prediction model. For example, if prediction model generator 210 has generated a prediction model indicating the relationship between vegetation index values ​​and harvester power usage, then, given vegetation index values ​​at different locations on the field, prediction map generator 212 generates prediction map 264 predicting the values ​​of harvester power usage at different locations on the field. The vegetation index values ​​at these locations obtained from the vegetation index map and the relationship between the vegetation index values ​​and harvester power usage obtained from the prediction model are used to generate prediction map 264.

[0061] The following will describe some changes in the data types mapped in Infographic 258, the data types sensed by Field Sensor 208, and the data types predicted in Prediction Graph 264.

[0062] In some examples, the data type in Infographic 258 differs from the data type sensed by Field Sensor 208, but the data type in Prediction Graph 264 is the same as that sensed by Field Sensor 208. For example, Infographic 258 could be a vegetation index map, and the variable sensed by Field Sensor 208 could be yield. Prediction Graph 264 could then be a predicted yield map that maps predicted yield values ​​to different geographic locations in the field. In another example, Infographic 258 could be a vegetation index map, and the variable sensed by Field Sensor 208 could be crop height. Prediction Graph 264 could then be a predicted crop height map that maps predicted crop height values ​​to different geographic locations in the field.

[0063] Further, in some examples, the data type in the information map 258 is different from the data type sensed by the field sensor 208, and the data type in the prediction map 264 is different from both the data type in the information map 258 and the data type sensed by the field sensor 208. For example, the information map 258 can be a vegetation index map, and the variable sensed by the field sensor 208 can be crop height. The prediction map 264 can then be a predicted biomass map that maps predicted biomass values to different geographic locations in the field. In another example, the information map 258 can be a vegetation index map, and the variable sensed by the field sensor 208 can be yield. The prediction map 264 can then be a predicted speed map that maps predicted harvester speed values to different geographic locations in the field.

[0064] In some examples, the information map 258 is from a previous pass through the field during a prior operation, and the data type is different from the data type sensed by the field sensor 208, but the data type in the prediction map 264 is the same as the data type sensed by the field sensor 208. For example, the information map 258 can be a seed population map generated during planting, and the variable sensed by the field sensor 208 can be stem size. The prediction map 264 can then be a predicted stem size map that maps predicted stem size values to different geographic locations in the field. In another example, the information map 258 can be a seeding mix map, and the variable sensed by the field sensor 208 can be crop status, such as standing crop or lodged crop. The prediction map 264 can then be a predicted crop status map that maps predicted crop status values to different geographic locations in the field.

[0065] In some examples, the information map 258 is from a previous pass through the field during a prior operation, and the data type is the same as the data type sensed by the field sensor 208, and the data type in the prediction map 264 is also the same as the data type sensed by the field sensor 208. For example, the information map 258 can be a yield map generated during the previous year, and the variable sensed by the field sensor 208 can be yield. The prediction map 264 can then be a predicted yield map that maps predicted yield values to different geographic locations in the field. In such an example, the relative yield differences in the georeferenced information map 258 from the previous year can be used by the prediction model generator 210 to generate a prediction model that models a relationship between the relative yield differences on the information map 258 and the yield values sensed by the field sensor 208 during the current harvesting operation. The prediction model is then used by the prediction map generator 210 to generate the predicted yield map.

[0066] In another example, the information map 258 can be a power usage map of the threshing / separating sub-system generated during the priori operation, and the variable sensed by the field sensor 208 can be threshing / separating sub-system power usage. The predicted map 264 can then be a predicted threshing / separating sub-system power usage map mapping predicted threshing / separating sub-system power usage values to different geographic locations in the field.

[0067] In some examples, the predicted map 264 can be provided to a control zone generator 213. The control zone generator 213 groups adjacent portions of the field into one or more control zones based on the data values of the predicted map 264 associated with those adjacent portions. A control zone can include two or more contiguous portions of a field, such as a field, for which the control parameters corresponding to the control zone for controlling a controllable sub-system are constant. For example, the response time to change the settings of a controllable sub-system 216 can not be satisfactory to respond to changes in values contained in a map, such as the predicted map 264. In this case, the control zone generator 213 parses the map and identifies control zones of a defined size to accommodate the response time of the controllable sub-system 216. In another example, the size of the control zones can be determined to reduce wear caused by excessive actuator movement resulting from continuous adjustment. In some examples, there can be different control zone groups for each controllable sub-system 216 or group of controllable sub-systems 216. The control zones can be added to the predicted map 264 to obtain a predicted control zone map 265. The predicted control zone map 265 can thus be similar to the predicted map 264 except that the predicted control zone map 265 includes control zone information defining the control zones. Thus, as described herein, a functional predicted map 263 can or can not include control zones. Both the predicted map 264 and the predicted control zone map 265 are functional predicted maps 263. In one example, the functional predicted map 263 does not include control zones, such as the predicted map 264. In another example, the functional predicted map 263 does include control zones, such as the predicted control zone map 265. In some examples, if an intercrop production system is implemented, there can be multiple crops in the field at the same time. In this case, the predicted map generator 212 and the control zone generator 213 are able to identify the locations and characteristics of the two or more crops and then generate the predicted map 264 and the predicted map with control zones 265 accordingly.

[0068] It should also be appreciated that the control zone generator 213 can cluster the values to generate the control zones, and the control zones can be added to the predicted control zone map 265 or a separate map showing only the generated control zones. In some examples, the control zones can be used to control or calibrate the agricultural harvester 100 or both. In other examples, the control zones can be presented to the operator 260 and used to control or calibrate the agricultural harvester 100, and in other examples, the control zones can be presented to the operator 260 or another user or stored for later use.

[0069] The prediction map 264 or the prediction control zone map 265 or both are provided to the control system 214, which generates control signals based on the prediction map 264 or the prediction control zone map 265 or both. In some examples, the communication system controller 229 controls the communication system 206 to communicate the prediction map 264 or the prediction control zone map 265 or control signals based on the prediction map 264 or the prediction control zone map 265 to other agricultural harvester machines that are harvesting in the same field. In some examples, the communication system controller 229 controls the communication system 206 to send the prediction map 264, the prediction control zone map 265, or both to other remote systems.

[0070] The operator interface controller 231 is operable to generate control signals to control the operator interface mechanisms 218. The operator interface controller 231 is also operable to present the prediction map 264 or the prediction control zone map 265 or other information derived from or based on the prediction map 264, the prediction control zone map 265, or both, to the operator 260. The operator 260 can be a local operator or a remote operator. As an example, the controller 231 generates control signals to control the display mechanisms to display one or both of the prediction map 264 and the prediction control zone map 265 to the operator 260. The controller 231 can generate operator actuatable mechanisms, which are shown and can be actuated by the operator to interact with the displayed maps. The operator can edit the maps by, for example, correcting the power characteristics displayed on the maps based on the operator’s observations. The settings controller 232 can generate control signals to control various settings on the agricultural harvester 100 based on the prediction map 264, the prediction control zone map 265, or both. For example, the settings controller 232 can 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 control, for example, one or more of the sieve and chaffer settings, the concave gap, the rotor settings, the clean grain fan speed settings, the header height, the header functions, the reel speed, the reel position, the belt conveyor functions (where the agricultural harvester 100 is coupled to a belt conveyor header), the corn header functions, the in-bin distribution control, and other actuators 248 that affect the operation of the agricultural harvester 100. The path planning controller 234 illustratively generates control signals to control the steering subsystem 252 to steer the agricultural harvester 100 according to a desired path. The path planning controller 234 can control the path planning system to generate a route for the agricultural harvester 100 and can control the propulsion subsystem 250 and the steering subsystem 252 to steer the agricultural harvester 100 along the route. The cooling controller 235 can control the cooling operation of the cooling subsystem 255 of the agricultural harvester 100. For example, the cooling controller 235 can adjust the fan speed or the fan blade pitch of the fans of the cooling subsystem 255. Or, for example, the cooling controller 235 can increase the fluid flow through the radiator or other heat dissipating devices. The feed rate controller 236 can control various subsystems, such as the propulsion subsystem 250 and the machine actuators 248, to control the feed rate based on the prediction map 264 or the prediction control zone map 265 or both. For example, as the agricultural harvester 100 approaches an area having a predicted subsystem power usage value above a selected threshold, the feed rate controller 236 can reduce the speed of the agricultural harvester 100 to maintain the power allocation to the predicted power usage requirements of one or more subsystems. The header and reel controller 238 can generate control signals to control the header or the reel or other header functions.The belt conveyor belt controller 240 can generate control signals to control the belt conveyor belt or other belt conveyor functions based on the prediction map 264, the prediction control zone map 265, or both. The header table position controller 242 can generate control signals to control the position of the header table included on the harvesting machine 100 based on the prediction map 264 or the prediction control zone map 265 or both, and the residue system controller 244 can generate control signals to control the residue subsystem 138 based on the prediction map 264 or the prediction control zone map 265 or both. The machine clean grain controller 245 can generate control signals to control the machine clean grain subsystem 254. Other controllers included on the agricultural harvester 100 can also control other subsystems based on the prediction map 264 or the prediction control zone map 265 or both.

[0071] Figure 3A and Figure 3B FIG. 3 (collectively referred to herein as FIG. 3) illustrates a flowchart that illustrates one example of the operation of the agricultural harvester 100 in generating the prediction map 264 and the prediction control zone map 265 based on the information map 258.

[0072] At 280, the agricultural harvester 100 receives the information map 258. Examples of the information map 258 or receiving the information map 258 are discussed with respect to blocks 281, 282, 284, and 286. As discussed above, the information map 258 maps values of a variable corresponding to a first characteristic to different locations in the field as shown in block 282. As shown in block 281, receiving the information map 258 can include selecting one or more of a plurality of possible information maps available. For example, one information map can be a vegetation index map generated from aerial images. Another information map can be a map generated during a previous pass through the field, which can be performed by a different machine (such as a sprayer or other machine) performing a previous operation in the field. The process of selecting one or more prior information maps can be manual, semi-automatic, or automatic. The prior information map 258 is based on data collected prior to the current harvesting operation. This is indicated by block 284. For example, the data can be collected based on aerial images taken during a previous year or early in the current growing season or other time. As shown in block 285, the information map can be a prediction map that predicts the characteristic based on the information map and a relationship to in-field sensors. The process of generating the prediction map is discussed in more detail with respect to FIG. 4. Figure 5The process can also be performed using other sensors and other prior maps to generate, for example, a predicted yield map or a predicted biomass map. These predicted maps can be used as prior maps in other prediction processes, as indicated by block 285. The data can be based on data detected in a manner different from using aerial images. For example, the data of information map 258 can be transmitted to agricultural harvester 100 using communication system 206 and stored in data storage device 202. The data of information map 258 can also be provided to agricultural harvester 100 in other ways using communication system 206, and this is represented by block 286 in the flowchart of FIG. 3. In some examples, information map 258 can be received by communication system 206.

[0073] At the start of the harvesting operation, field sensors 208 generate sensor signals indicative of one or more field data values indicative of a characteristic, for example a power characteristic such as power usage of one or more subsystems, as indicated by block 288. Examples of field sensors 288 are discussed with respect to blocks 222, 290, and 226. As explained above, field sensors 208 include on-board sensors 222, remote field sensors 224 such as UAV-based sensors that fly each time to gather field data (as shown in block 290), or other types of field sensors specified by field sensors 226. In some examples, data from on-board sensors is georeferenced using location, heading, or speed data from geographic position sensors 204.

[0074] Prediction model generator 210 controls information variables to field variables model generator 228 to generate a model that models the relationship between the mapped values contained in information map 258 and the field values sensed by field sensors 208, as indicated by block 292. The characteristics or data types represented by the mapped values in information map 258 and the field values sensed by field sensors 208 can be the same characteristics or data types or different characteristics or data types.

[0075] The relationship or model generated by prediction model generator 210 is provided to prediction map generator 212. Prediction map generator 212 uses the prediction model and information map 258 to generate prediction map 264 that predicts values of the characteristic sensed by field sensors 208 at different geographic locations in the field being harvested, or different characteristics related to the characteristic sensed by field sensors 208, as indicated by block 294.

[0076] It should be noted that in some examples, the information map 258 can include two or more different maps or two or more different layers of a single map. Each layer can represent a different data type than another layer, or the layers can have the same data type obtained at different times. Each map of the two or more different maps or each layer of the two or more different layers of a map maps different types of variables to geographic locations in the field. In such examples, the prediction model generator 210 generates a prediction model that models relationships between the in-field data and each of the different variables mapped by the two or more different maps or the two or more different layers of a map. Similarly, the in-field sensors 208 can include two or more sensors, each of which senses a different type of variable. Thus, the prediction model generator 210 generates a prediction model that models relationships between each type of variable mapped by the information map 258 and each type of variable sensed by the in-field sensors 208. The prediction map generator 212 can use the prediction model and each of the maps or layers of maps in the information map 258 to generate a functional prediction map 263 that predicts values of each sensed characteristic (or characteristics related to the sensed characteristics) sensed by the in-field sensors 208 at different locations in the field being harvested.

[0077] The prediction map generator 212 configures the prediction map 264 such that the prediction map 264 is operable (or consumable) by the control system 214. The prediction map generator 212 can provide the prediction map 264 to the control system 214 or to the control zone generator 213 or to both. Some examples of different ways that the prediction map 264 can be configured or output are described with respect to blocks 296, 295, 299, and 297. For example, the prediction map generator 212 configures the prediction map 264 such that the prediction 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 different controllable subsystems of the agricultural harvester 100, as indicated by block 296.

[0078] The control zone generator 213 can divide the prediction map 264 into control zones based on the values on the prediction map 264. Contiguously geolocated values within a threshold of each other can be grouped into control zones. The threshold can be a default threshold, or the threshold can be set based on operator input, based on input from an automation system, or based on other criteria. The size of the zones can be based on the responsiveness of the control system 214, controllable subsystems 216, based on wear considerations, or based on other criteria, as indicated by block 295. The prediction map generator 212 configures the prediction map 264 for presentation to an operator or other user. The control zone generator 213 can configure the prediction control zone map 265 for presentation to an operator or other user. This is indicated by block 299. When presented to an operator or other user, the presentation of the prediction map 264 or the prediction control zone map 265 or both can include one or more of the predicted values on the prediction map 264 related to geographic locations, the control zones on the prediction control zone map 265 related to geographic locations, and the set values or control parameters used based on the predicted values on the map 264 or the zones on the prediction control zone map 265. In another example, the presentation can include more abstract information or more detailed information. The presentation can also include a confidence level that indicates the accuracy of the predicted values on the prediction map 264 or the zones on the prediction control zone map 265 to match measured values that can be measured by sensors on the agricultural harvester 100 as the agricultural harvester 100 moves through the field. Additionally, where information is presented to more than one location, an authentication and authorization system can be provided to implement authentication and authorization processes. For example, there can be a hierarchy of individuals that are authorized to view and change the maps and other presented information. As an 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, or both. In some examples, each physical display device at each location can be associated with a person or user permission level. The user permission level can be used to determine which display indicia are visible on the physical display device, and which values the corresponding person can change. For example, a local operator of the agricultural harvester 100 can not be able to see the information corresponding to the prediction map 264 or make any changes to the machine operation. However, a supervisor such as a supervisor at a remote location can be able to see the prediction map 264 on a display, but be prevented from making any changes. A manager that can be at a separate remote location can be able to see all of the elements on the prediction map 264 and also be able to change the prediction map 264. In some cases, the prediction map 264 can be accessible and changeable by a manager located remotely, can be used for machine control. This is one example of an authorization hierarchy that can be implemented. The prediction map 264 or the prediction control zone map 265 or both can also be configured in other ways, as indicated by block 297.

[0079] At block 298, inputs from the geo-location sensor 204 and other field sensors 208 are received by the control system. In particular, at block 300, the control system 214 detects input from the geo-location sensor 204 identifying the geo-location of the agricultural harvester 100. Block 302 represents sensor input indicating the trajectory or heading of the agricultural harvester 100 being received by the control system 214, and block 304 represents the speed of the agricultural harvester 100 being received by the control system 214. Block 306 represents other information being received by the control system 214 from the various field sensors 208.

[0080] At block 308, the control system 214 generates control signals to control the controllable subsystems 216 based on the prediction map 264 or the prediction control zone map 265 or both, as well as the inputs from the geo-location sensor 204 and any other field sensors 208. At block 310, the control system 214 applies the control signals to the controllable subsystems. It will be appreciated that the particular control signals that are generated and the particular controllable subsystems 216 that are controlled can vary based on one or more different factors. For example, the control signals that are generated and the controllable subsystems 216 that are controlled can be based on the type of prediction map 264 or prediction control zone map 265 or both that is being used. Similarly, the control signals that are generated and the controllable subsystems 216 that are controlled, as well as the timing of the control signals, can be based on various delays in the flow of the crop through the agricultural harvester 100 and the responsiveness of the controllable subsystems 216.

[0081] As an example, the prediction map 264 generated in the form of a prediction power map can be used to control one or more of the subsystems 216. For example, the prediction power map can include power usage demand values that geographically reference locations within the field being harvested. The power usage demand values from the prediction power map can be extracted and used to control the steering and propulsion subsystems 252 and 250. By controlling the steering and propulsion subsystems 252 and 250, the feed rate of material moving through the agricultural harvester 100 can be controlled. Similarly, the header height can be controlled to take more or less material, and thus, the header height can also be controlled to control the feed rate of material through the agricultural harvester 100. In other examples, if the prediction map 264 maps predicted header power usage to locations in the field, then power allocation to the header can be implemented. For example, if the values present in the prediction power map indicate that one or more areas have a higher power usage demand for the header subsystem, then the header and reel controller 238 can allocate more power from the engine to the header subsystem, which can require less power to be allocated to other subsystems, such as by reducing speed and reducing power to the propulsion subsystem. The foregoing example involving header control using a prediction power map is provided by way of example only. Thus, a variety of other control signals can be generated using values obtained from a prediction power map or other types of prediction maps to control one or more of the controllable subsystems 216.

[0082] At block 312, it is determined whether the harvesting operation has been completed. If harvesting has not been completed, the process proceeds to block 314, where it continues to read field sensor data from the geo-location sensors 204 and the field sensors 208 (and possibly other sensors).

[0083] In some examples, at block 316, the agricultural harvester 100 can also detect learning trigger criteria to perform machine learning on one or more of the prediction map 264, the prediction control zone map 265, the models generated by the prediction model generator 210, the zones generated by the control zone generator 213, the one or more control algorithms implemented by the controllers in the control system 214, and other triggers for learning.

[0084] The learning trigger criteria can include any of a variety 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, triggering learning can include recreating the relationships used to generate the prediction model when a threshold amount of field sensor data is obtained from the field sensors 208. In such examples, the amount of field sensor data received from the field sensors 208 that exceeds the threshold triggers or causes the prediction model generator 210 to generate a new prediction model used by the prediction map generator 212. Thus, as the agricultural harvester 100 continues the harvesting operation, the threshold amount of field sensor data received from the field sensors 208 triggers the creation of new relationships represented by the prediction models generated by the prediction model generator 210. Further, the new prediction model can be used to regenerate a new prediction map 264, a prediction control zone map 265, or both. Block 318 represents detecting the threshold amount of field sensor data used to trigger the creation of a new prediction model.

[0085] In other examples, the learning trigger criteria can be based on the degree of change in the field sensor data from the field sensors 208, such as the degree of change over time or compared to a previous value. For example, if the change within the field sensor data (or the relationship between the field sensor data and the information in the information map 258) is within a selected range, or less than a defined amount, or below a threshold, then a new prediction model is not generated by the prediction model generator 210. As a result, the prediction map generator 212 does not generate a new prediction map 264, a prediction control zone map 265, or both. However, if the change within the field sensor data is outside of the selected range, greater than the defined amount, or above the threshold, for example, then the prediction model generator 210 generates a new prediction model using all or a portion of the newly received field sensor data used by the prediction map generator 212 to generate a new prediction map 264. At block 320, the change in the field sensor data, such as the size of the amount of data that is outside of the selected range or the size of the change in the relationship between the field sensor data and the information in the information map 258, can be used as a trigger that results in the generation of a new prediction model and prediction map. Continuing the example described above, the threshold, range, and defined amount can be set to a default value, set by an operator or user through interaction with a user interface, set by an automated system, or otherwise set.

[0086] Other learning trigger criteria can also be used. For example, if the prediction model generator 210 switches to a different information map (different from the initially selected information map 258), the switch to the different information map can trigger relearning by the prediction model generator 210, the prediction map generator 212, the control zone generator 213, the control system 214, or other. In another example, a transition of the agricultural harvester 100 to a different terrain or to a different control zone can also be used as a learning trigger criterion.

[0087] In some cases, the operator 260 can also edit the prediction map 264 or the prediction control zone map 265 or both. The editing can change values on the prediction map 264, change the size, shape, location, or existence of control zones on the prediction control zone map 265, or both. Block 321 shows that the edited information can be used as a learning trigger criterion.

[0088] In certain cases, the operator 260 can also observe that the automatic control of the controllable subsystem is not as the operator desires. In this case, the operator 260 can provide a manual adjustment to the controllable subsystem, which reflects that the operator 260 desires the controllable subsystem to operate differently than commanded by the control system 214. Thus, the manual change to the setting by the operator 260 can cause one or more of the following: the prediction model generator 210 to relearn the model, the prediction map generator 212 to regenerate the map 264, the control zone generator 213 to regenerate one or more control zones on the prediction control zone map 265, and the control system 214 to relearn the control algorithm or perform machine learning on one or more of the controller components 232-246 in the control system 214, based on the adjustment by the operator 260, as shown in block 322. Block 324 represents using other trigger learning criteria.

[0089] In other examples, relearning can 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 shown in block 326.

[0090] If relearning is triggered (whether based on a learning trigger criterion or based on the passage of a time interval, as shown in block 326), one or more of the prediction model generator 210, the prediction map generator 212, the control zone generator 213, and the control system 214 perform machine learning to generate new prediction models, new prediction maps, new control zones, and new control algorithms, respectively, based on the learning trigger criterion. The new prediction models, the new prediction maps, and the new control algorithms are generated using any additional data collected since the last performance of the learning operation. Performance of the relearning is indicated by block 328.

[0091] If the harvesting operation has been completed, the operation moves from block 312 to block 330, in which one or more of the prediction map 264, the prediction control zone map 265, and the prediction model generated by the prediction model generator 210 are stored. The prediction map 264, the prediction control zone map 265, and the prediction model can be stored locally on the data storage device 202, or transmitted to a remote system for later use using the communication system 206.

[0092] It will be noted that while some examples herein describe the prediction model generator 210 and the prediction map generator 212 receiving an information map when generating a prediction model and a functional prediction map, respectively, in other examples, the prediction model generator 210 and the prediction map generator 212 can receive other types of maps when generating a prediction map and a functional prediction map, respectively, including a prediction map such as a functional prediction map generated during a harvesting operation.

[0093] Figure 4 is Figure 1 A block diagram of a portion of the agricultural harvester 100 shown in FIG. 1. In particular, Figure 4 Examples of the prediction model generator 210 and the prediction map generator 212 are shown in particular detail. Figure 4 Information flow between the different components shown is also illustrated. The prediction model generator 210 receives one or more of a vegetation index map 332, a crop moisture map 335, a terrain map 337, a soil property map 339, a predicted yield map 341, or a predicted biomass map 343 as an information map. The vegetation index map 332 includes georeferenced vegetation index values. The crop moisture map 335 includes georeferenced crop moisture values. The terrain map 337 includes georeferenced terrain characteristic values. The soil property map 339 includes georeferenced soil property values.

[0094] The predicted yield map 341 includes georeferenced predicted yield values. The predicted yield map 341 can be generated using the processes described in Figure 2 The predicted yield map 341 can also be generated in other ways. The predicted biomass map 343 includes georeferenced predicted biomass values. The predicted biomass map 343 can be generated using the processes described in

[0095] The predicted biomass map 343 includes georeferenced predicted biomass values. The predicted biomass map 343 can be generated using the processes described in Figure 2 The predicted biomass map 343 can also be generated in other ways. The predicted biomass map 343 includes georeferenced predicted biomass values. The predicted biomass map 343 can be generated using the processes described in

[0096] The prediction model generator 210 also receives a geographic location 334 or an indication of a geographic location from the geographic location sensor 204. The field sensor 208 illustratively includes a power characteristic sensor, such as a power sensor 336, and a processing system 338. The power sensor 336 senses a power characteristic of one or more components of the agricultural harvester 100. In some cases, the power sensor 336 can be located on the agricultural harvester 100. The processing system 338 processes sensor data generated from the power sensor 336 to generate processed data, some examples of which are described below. The power sensor 336 can include, but is not limited to, one or more of a voltage sensor, a current sensor, a torque sensor, a fluid pressure sensor, a fluid flow sensor, a force sensor, a bearing load sensor, and a rotation sensor. The output of one or more of these or other sensors can be combined to determine one or more power characteristics.

[0097] The present discussion is directed to examples in which the power sensor 336 is one or more of the sensors listed above. It should be understood that these are merely examples, and other examples of the power sensor 336 are also contemplated herein. As shown, the example prediction model generator 210 includes one or more of a vegetation index to power characteristic model generator 342, a crop moisture to power characteristic model generator 343, a terrain to power characteristic model generator 344, a soil property characteristic to power characteristic model generator 345, a yield to power characteristic model generator 346, and a biomass to power characteristic model generator 347. In other examples, the prediction model generator 210 can include additional components, fewer components, or different components than those shown in the example of FIG. 3. Figure 4 As shown, the example prediction model generator 210 includes one or more of a vegetation index to power characteristic model generator 342, a crop moisture to power characteristic model generator 343, a terrain to power characteristic model generator 344, a soil property characteristic to power characteristic model generator 345, a yield to power characteristic model generator 346, and a biomass to power characteristic model generator 347. In other examples, the prediction model generator 210 can include additional components, fewer components, or different components than those shown in the example of FIG. 3. Figure 4 As shown, the example prediction model generator 210 includes one or more of a vegetation index to power characteristic model generator 342, a crop moisture to power characteristic model generator 343, a terrain to power characteristic model generator 344, a soil property characteristic to power characteristic model generator 345, a yield to power characteristic model generator 346, and a biomass to power characteristic model generator 347. In other examples, the prediction model generator 210 can include additional components, fewer components, or different components than those shown in the example of FIG. 3.

[0098] The model generator 342 identifies a relationship between the power characteristic at the geographic location corresponding to where the power sensor 336 sensed the power characteristic and the vegetation index value from the vegetation index map 332 corresponding to the same location in the field where the power characteristic was sensed. Based on this relationship established by the model generator 342, the model generator 342 generates a predictive power model 350. The predictive power model 350 is used by the prediction map generator 212 to predict the power characteristic at a different location in the field based on the vegetation index value contained in the vegetation index map 332 at the same location that is georeferenced.

[0099] The model generator 343 identifies a relationship between the power characteristic at the geographic location corresponding to where the power sensor 336 sensed the power characteristic and the crop moisture value from the crop moisture map 335 corresponding to the same location in the field where the power characteristic was sensed. Based on this relationship established by the model generator 343, the model generator 343 generates the predictive power model 350. The predictive power model 350 is used by the predictive map generator 212 to predict the power characteristic at a different location in the field based on the crop moisture value contained in the crop moisture map 335 at the same location geographically referenced.

[0100] The model generator 344 identifies a relationship between the power characteristic at the geographic location corresponding to where the power sensor 336 sensed the power characteristic and the terrain feature value from the terrain map 337 corresponding to the same location in the field where the power characteristic was sensed. Based on this relationship established by the model generator 344, the model generator 344 generates the predictive power model 350. The predictive power model 350 is used by the predictive map generator 212 to predict the power characteristic at a different location in the field based on the terrain feature value contained in the terrain map 337 at the same location geographically referenced.

[0101] The model generator 345 identifies a relationship between the power characteristic at the geographic location corresponding to where the power sensor 336 sensed the power characteristic and the soil property value from the soil property map 339 corresponding to the same location in the field where the power characteristic was sensed. Based on this relationship established by the model generator 345, the model generator 345 generates the predictive power model 350. The predictive power model 350 is used by the predictive map generator 212 to predict the power characteristic at a different location in the field based on the soil property value contained in the soil property map 339 at the same location geographically referenced.

[0102] The model generator 346 identifies a relationship between the power characteristic at the geographic location corresponding to where the power sensor 336 sensed the power characteristic and the yield value from the yield map 341 corresponding to the same location in the field where the power characteristic was sensed. Based on this relationship established by the model generator 346, the model generator 346 generates the predictive power model 350. The predictive power model 350 is used by the predictive map generator 212 to predict the power characteristic at a different location in the field based on the yield value contained in the predictive yield map 341 at the same location geographically referenced.

[0103] The model generator 347 identifies a relationship between the power characteristics sensed at the geographic locations corresponding to the power sensor 336 and the biomass values from the biomass map 343 corresponding to the same locations in the field where the power characteristics were sensed. Based on this relationship established by the model generator 347, the model generator 347 generates a predictive power model 350. The predictive power model 350 is used by the predictive map generator 212 to predict the power characteristics at different locations in the field based on the biomass values included in the predictive biomass map 343 at the same locations in the field.

[0104] In view of the above, the predictive model generator 210 can be operable to generate a plurality of predictive power models, such as one or more of the predictive power models generated by the model generators 342, 343, 344, 345, 346, and 347. In another example, two or more of the predictive power models described above can be combined into a single predictive power model that predicts two or more power characteristics based on different values at different locations in the field. Any one or combination of these power models is collectively represented in Figure 4 by the power model 350.

[0105] The predictive power model 350 is provided to the predictive map generator 212. In Figure 4 example, the predictive map generator 212 includes a crop engagement component map generator 351, a header power map generator 352, a feeder power map generator 353, a threshing power map generator 354, a separator power map generator 355, a residue handling power map generator 356, and a propulsion power map generator 357. In other examples, the predictive map generator 212 can include additional, fewer, or different map generators. Thus, in some examples, the predictive map generator 212 can include other items 358, which can include other types of map generators for generating power maps of other types of power characteristics.

[0106] The crop engagement component map generator 351 receives the predictive power model 350, which predicts a power characteristic based on values in one or more of the vegetation index map 332, the crop moisture map 335, the terrain map 337, the soil properties map 339, the predicted yield map 341, or the predicted biomass map 343, and generates a predictive map that predicts the power characteristic of a crop engagement component at different locations in the field. For example, the crop engagement component can include a cutter and a reel, and the crop engagement component map generator 351 generates a map that estimates the power used by the reel and the cutter based on the predictive power model 350 that defines a relationship between crop moisture and power used by the reel and the cutter.

[0107] The header power map generator 352 receives the predicted power model 350 that predicts power characteristics based on values in one or more of the vegetation index map 332, the crop moisture map 335, the terrain map 337, the soil properties map 339, the predicted yield map 341, or the predicted biomass map 343 and generates a predicted map of power characteristics of a header at different locations in the predicted field. For example, the crop engagement components can include one or more of a cutter, a reel, a belt conveyor belt, an unloading auger conveyor, a collection component, a stalk handling component, and a header positioning actuator, and the header power map generator 352 generates a map of estimated power used by one or more of the reel, the cutter, the belt conveyor belt, the unloading auger conveyor, the collection component, the stalk handling component, and the header positioning actuator based on the predicted power model 350 that defines a relationship between crop moisture and terrain and power used by one or more of the reel, the cutter, the belt conveyor belt, the unloading auger conveyor, and the header positioning actuator.

[0108] The feeder power map generator 353 receives the predicted power model 350 that predicts power characteristics based on values in one or more of the vegetation index map 332, the crop moisture map 335, the terrain map 337, the soil properties map 339, the predicted yield map 341, or the predicted biomass map 343 and generates a predicted map of power characteristics of a feeder at different locations in the predicted field.

[0109] The threshing power map generator 354 receives the predicted power model 350 that predicts power characteristics based on values in one or more of the vegetation index map 332, the crop moisture map 335, the terrain map 337, the soil properties map 339, the predicted yield map 341, or the predicted biomass map 343, and generates a predicted map of power characteristics of the threshing subsystem at different locations in the predicted field. For example, the threshing subsystem can include one or more threshing cylinders, concave adjustment actuators, and beater, and the threshing power map generator 352 generates a map of estimated power used by the one or more threshing cylinders, concave adjustment actuators, and beater based on the predicted power model 350 that defines a relationship between the vegetation index and the power used by the one or more threshing cylinders, concave adjustment actuators, and beater. Or for example, the threshing subsystem can include a threshing cylinder and a set of concaves at a given gap, and the threshing power map generator 352 generates a map of estimated power used by the threshing cylinder with the given set of concaves at the given gap based on the predicted power model 350 that defines a relationship between the predicted biomass and the power used by the threshing cylinder with the given set of concaves. Or for example, the threshing subsystem can include one or more beaters in a given configuration, and the threshing power map generator 352 generates a map of estimated power used by the one or more beaters in the given configuration based on the predicted power model 350 that defines a relationship between the predicted biomass and the power used by the one or more beaters in the given configuration.

[0110] The separator power map generator 355 receives the predicted power model 350 that predicts power characteristics based on values in one or more of the vegetation index map 332, the crop moisture map 335, the terrain map 337, the soil properties map 339, the predicted yield map 341, or the predicted biomass map 343, and generates a predicted map of power characteristics of the separator subsystem at different locations in the predicted field. For example, the separator subsystem can include one or more fans, screens, chaffer, and stalkers, and the separator power map generator 355 generates a map of estimated power used by the one or more fans, screens, chaffer, and stalkers based on the predicted power model 350 that defines a relationship between the predicted yield value and the power used by the one or more fans, screens, chaffer, and stalkers. For example, the separator subsystem can include one or more fans running at a given speed and screens, chaffer, and stalkers in a given configuration, and the separator power map generator 355 generates a map of estimated power used by the one or more fans running at the given speed and the screens, chaffer, and stalkers in the given configuration based on the predicted power model 350 that defines a relationship between the predicted yield value and the power used by the one or more fans running at the given speed and the screens, chaffer, and stalkers in the given configuration.

[0111] Residue treatment power map generator 356 receives the predicted power model 350 that predicts a power characteristic based on values in one or more of the vegetation index map 332, the crop moisture map 335, the terrain map 337, the soil properties map 339, the predicted yield map 341, or the predicted biomass map 343 and generates a predicted map of the power characteristic of the residue treatment subsystem at different locations in the predicted field. For example, residue treatment power map generator 356 generates a map of the estimated power used by a residue spreader based on the predicted power model 350 that defines a relationship between predicted biomass values and the power used by the residue spreader. For example, residue treatment power map generator 356 generates a map of the estimated power used by a residue chopper based on the predicted power model 350 that defines a relationship between predicted yield values and the power used by the residue chopper.

[0112] Propulsion power map generator 357 receives the predicted power model 350 that predicts a power characteristic based on values in one or more of the vegetation index map 332, the crop moisture map 335, the terrain map 337, the soil properties map 339, the predicted yield map 341, or the predicted biomass map 343 and generates a predicted map of the power characteristic of the propulsion subsystem at different locations in the predicted field. For example, propulsion power map generator 357 generates a map of the estimated power used by a propulsion subsystem based on the predicted power model 350 that defines a relationship between terrain map values and the power used by the propulsion system.

[0113] The predicted map generator 212 outputs one or more predicted power maps 360 that predict one or more power characteristics. Each of the predicted power maps 360 predicts a respective power characteristic at different locations in the field. Each of the generated predicted power maps 360 can be provided to the control zone generator 213, the control system 214, or both. The control zone generator 213 generates control zones and merges the control zones into functional predicted maps 360. One or more functional predicted maps can be provided to the control system 214 that generates control signals based on the one or more functional predicted maps (with or without control zones) to control one or more of the controllable subsystems 216.

[0114] Figure 5is a flowchart of an example of the operations of the prediction model generator 210 and the prediction map generator 212 in generating the prediction power model 350 and the prediction power map 360. At block 362, the prediction model generator 210 and the prediction map generator 212 receive one or more of the vegetation index map 332, the crop moisture map 335, the terrain map 337, the soil properties map 339, the predicted yield map 341, the predicted biomass map 343, or some other map 363. At block 364, the processing system 338 receives one or more sensor signals from the power sensors 336. As discussed above, the power sensors 336 can include, but are not limited to, a voltage sensor 371, a current sensor 373, a torque sensor 375, a hydraulic pressure sensor 377, a hydraulic flow sensor 379, a force sensor 381, a bearing load sensor 383, a rotation sensor 385, or other types of power sensors 370.

[0115] At block 372, the processing system 338 processes the one or more received sensor signals to generate data indicative of power characteristics. As shown at block 374, the power characteristics can be identified at a machine-wide level. For example, the total power used by the entire agricultural harvester. This level of power usage can be used to calculate fuel consumption, efficiency, etc. As shown at block 376, the power characteristics can be identified at a subsystem level. For example, characteristics at this level can be used to allocate power across the subsystems. As shown at block 378, the power characteristics can be identified at a component level. The sensor data can include other levels as well as other data as indicated by block 380.

[0116] At block 382, the prediction model generator 210 also obtains a geographic location corresponding to the sensor data. For example, the prediction model generator 210 can obtain the geographic location from the geographic location sensor 204 and determine the precise geographic location at which the sensor data 340 was captured or derived based on machine delays, machine speed, etc.

[0117] At block 384, the prediction model generator 210 generates one or more prediction power models, such as the power model 350, that model a relationship between vegetation index values, crop moisture values, soil properties values, predicted yield values, or predicted biomass values obtained from information maps, such as the information maps 258, and power characteristics or related characteristics sensed by the field sensors 208. For example, the prediction model generator 210 can generate a prediction power model that models a relationship between vegetation index values and sensed characteristics that include power usage indicated by sensor data obtained from the field sensors 208.

[0118] At block 386, the predicted power model, such as predicted power model 350, is provided to the predicted map generator 212, which generates a predicted power map 360 that maps predicted power characteristics based on the vegetation index map, the crop moisture map, the soil property map, the predicted yield map or the predicted biomass map, and the predicted power model 350. For example, in some examples, the predicted power map 360 predicts power usage / needs for various different subsystems. Further, the predicted power map 360 can be generated during the course of the agricultural operation. Thus, as the agricultural harvester moves through the field performing the agricultural operation, the predicted power map 360 is generated as the agricultural operation is being performed.

[0119] At block 394, the predicted map generator 212 outputs the predicted power map 360. At block 391, the predicted map generator 212 outputs the predicted power map for presentation to the operator 260 and possible interaction by the operator 260. At block 393, the predicted map generator 212 can configure the map for consumption by the control system 214. At block 395, the predicted map generator 212 can also provide the map 360 to the control zone generator 213 for generating control zones. At block 397, the predicted map generator 212 can also otherwise configure the map 360. The predicted power map 360 (with or without control zones) is provided to the control system 214. At block 396, the control system 214 generates control signals based on the predicted power map 360 to control the controllable subsystems 216.

[0120] As can be seen, the present system employs an information map that maps characteristics such as vegetation index values, crop moisture values, soil property values, predicted yield values, or predicted biomass values, or information from prior operations to different locations in the field. The present system also uses one or more field sensors that sense field sensor data indicative of a power characteristic, such as power usage, power need, or power loss, and generates a model that models the relationship between the characteristic sensed using the field sensor or a related characteristic and the characteristic mapped in the information map. Thus, the present system generates a functional prediction map using the model, the field data, and the information map, and the generated functional prediction map can be configured for use by a control system, presented to a local or remote operator or other user, or both. For example, the control system can use the map to control one or more systems of the combine harvester.

[0121] Figure 6A is Figure 1 A block diagram of example portions of the agricultural harvester 100 shown in FIG. 1. In particular, Figure 6AExamples of the prediction model generator 210 and the prediction map generator 212 are shown, in particular. In the illustrated example, the information map is one or more of a historical power map 333, a predicted power map 360, or a priori operating map 400. The a priori operating map 400 can include power characteristic values sensed at different locations in the field during a previous agricultural operation.

[0122] Also, in the example shown in FIG. 3, the field sensors 208 can include one or more of a temperature sensor 401, an operator input sensor 405, and a processing system 406. The field sensors 208 can also include other sensors 408. Figure 6A Figure 6B Examples of the other sensors 408 are shown.

[0123] The temperature sensor 401 senses a temperature of a given subsystem. In some examples, the temperature sensor 401 senses a coolant fluid temperature, a hydraulic fluid temperature, a lubricant, a surface of the agricultural harvester 100 (e.g., an inverter surface), a battery or electronic power device; a moving mechanical component (e.g., a bearing or a gear); an exhaust gas or other gas, an air temperature within an enclosed portion of the agricultural harvester 100, or some other temperature.

[0124] The operator input sensor 405 illustratively senses various operator inputs. The inputs can be setting inputs or other control inputs, such as steering inputs and other inputs, used to control settings on the agricultural harvester 100. Thus, when the operator 260 changes a setting or provides a command input through the operator interface mechanism 218, such input is detected by the operator input sensor 405, which provides a sensor signal indicative of the sensed operator input.

[0125] The processing system 406 can receive sensor signals from one or more of the temperature sensor 401, the operator input sensor 405, and the other sensor(s) 408, and generate an output indicative of the sensed variable. For example, the processing system 406 can receive a sensor input from the temperature sensor 401 and generate an output indicative of the temperature. The processing system 406 can also receive input from the operator input sensor 405 and generate an output indicative of the sensed operator input.

[0126] ​The prediction model generator 210 can include a power-to-temperature model generator 410, a power-to-operator command model generator 414, and a power-to-sensor data model generator 441. In other examples, the prediction model generator 210 can include additional, fewer, or other model generators 415. The prediction model generator 210 can receive the geographic location indicator 334 from the geographic location sensor 204 and generate prediction models 426 that model relationships between information in one or more of the information maps 258 and one or more of the following items: a temperature sensed by the temperature sensor 401, an operator input command sensed by the operator input sensor 405, and another agricultural characteristic sensed by the other sensor(s) 408.

[0127] The power-to-temperature model generator 410 generates a prediction model 426 that reflects a relationship between power characteristics reflected on the historical power map 333, the predicted power map 360, or the prior operation map 400, or any combination thereof, and a temperature sensed by the temperature sensor 401. The power-to-temperature model generator 410 generates a prediction model 426 that corresponds to this relationship.

[0128] The power-to-operator command model generator 414 generates a model that models a relationship between power characteristics reflected on the historical power map 333, the predicted power map 360, or the prior operation map 400, or any combination thereof, and an operator input command sensed by the operator input sensor 405. The power-to-operator command model generator 414 generates a prediction model 426 that corresponds to this relationship.

[0129] The power-to-sensor data model generator 441 generates a model that models a relationship between power characteristics reflected on the historical power map 333, the predicted power map 360, or the prior operation map 400, or any combination thereof, and sensor data sensed by one or more of the field sensors 208. The power-to-sensor data model generator 441 generates a prediction model 426 that corresponds to this relationship.

[0130] The prediction models 426 generated by the prediction model generator 210 can include one or more of the prediction models that can be generated by the power-to-temperature model generator 410, the power-to-operator command model generator 414, the power-to-sensor data model generator 441, and other model generators that can be included as part of the other items 415.

[0131] In examples in which the prediction map generator 212 includes the prediction temperature map generator 416, the prediction sensor data map generator 420, and the prediction operator command map generator 432, the prediction map generator 212 generates the predicted temperature map 460, the predicted sensor data map 480, and the predicted operator command map 492, respectively. Figure 6A In examples in which the prediction map generator 212 includes the prediction temperature map generator 416, the prediction sensor data map generator 420, and the prediction operator command map generator 432, the prediction map generator 212 generates the predicted temperature map 460, the predicted sensor data map 480, and the predicted operator command map 492, respectively.

[0132] The predicted temperature map generator 416 receives the prediction model 426 modeling the relationship between the power characteristics and temperature (e.g., the prediction model generated by the power to temperature model generator 410) and one or more of the information maps 258. The predicted temperature map generator 416 generates a functional predicted temperature map 425 of the temperature of one or more components of the agricultural harvester 100 at different locations in the field based on one or more power characteristics in one or more of the information maps 258 at those locations in the field and based on the prediction model 426.

[0133] The predicted operator command map generator 422 receives the prediction model 426 modeling the relationship between the power characteristics and the operator command inputs detected by the operator input sensors 405 (e.g., the prediction model generated by the power to command model generator 414) and generates a functional predicted operator command map 440 of the operator command inputs at different locations in the field based on the power characteristic values from the historical power map 333 or the predicted power map 360 and the prediction model 426.

[0134] The predicted sensor data map generator 420 receives the prediction model 426 modeling the relationship between the power characteristics and one or more characteristics detected by the field sensors 408 (e.g., the prediction model generated by the power to sensor data model generator 441) and one or more of the information maps 258. The predicted sensor data map generator 420 generates a functional predicted sensor data map 429 of the sensor data (or the characteristic indicated by the sensor data) at different locations in the field based on one or more power characteristics in one or more of the information maps 258 at those locations in the field and based on the prediction model 426.

[0135] The predicted map generator 212 outputs one or more of the functional predicted maps 425, 429, and 440. Each of the functional predicted maps 425, 429, and 440 can be provided to the control zone generator 213, the control system 214, or both. The control zone generator 213 can generate control zones and incorporate the control zones into each of the maps 425, 429, and 440. Any or all of the functional predicted maps 425, 429, and 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 216 based on one or all of the functional predicted maps 425, 429, and 440. Any or all of the maps 425, 429, and 440 (with or without control zones) can be presented to the operator 260 or another user.

[0136] Figure 6Bis a block diagram showing some examples of real-time (live) sensors 208. Figure 6B Some or different combinations of the sensors shown in FIG. 9 can have the sensors 336 and the processing system 338 simultaneously. Figure 6B Some of the possible live sensors 208 shown in FIG. 9 are shown and described relative to the previous figures, and are similarly numbered. Figure 6B The live sensors 208 are shown to include operator input sensors 980, machine sensors 982, harvested material property sensors 984, field and soil property sensors 985, environmental characteristic sensors 987, and they can include a wide variety of other sensors 226. The operator input sensors 980 can be sensors that sense operator input through the operator interface mechanisms 218. Thus, the operator input sensors 980 can sense user movements of levers, joysticks, steering wheels, buttons, dials, or pedals. The operator input sensors 980 can also sense user interactions with other operator input structures, such as interactions with touch-sensitive screens, with microphones that utilize voice recognition, or any of a variety of other operator input mechanisms.

[0137] The machine sensors 982 can sense different characteristics of the agricultural harvester 100. For example, as discussed above, the machine sensors 982 can include the machine speed sensor 146, the separator loss sensor 148, the clean grain camera 150, the forward view image capture mechanism 151, the loss sensor 152, or the geo-location sensor 204, examples of which are described above. The machine sensors 982 can also include machine setting sensors 991 that sense machine settings. The above references to the machine sensors 146, 148, 150, 151, 152, 204, and 991 are incorporated by reference. Figure 1Some examples of machine settings are described. A front equipment (e.g., header) position sensor 993 can sense a position of the header 102, reel 164, cutter 104, or other front equipment relative to a frame of the agricultural harvester 100. For example, the sensor 993 can sense a height of the header 102 above the ground. The machine sensors 982 can also include a front equipment (e.g., header) orientation sensor 995. The sensor 995 can sense an orientation of the header 102 relative to the agricultural harvester 100 or relative to the ground. The machine sensors 982 can include a stability sensor 997. The stability sensor 997 senses a vibration or bounce motion (and amplitude) of the agricultural harvester 100. The machine sensors 982 can also include a residue setting sensor 999 configured to sense whether the agricultural harvester 100 is configured to chop residue, produce a windrow, or process residue in another manner. The machine sensors 982 can include a clean grain bin fan speed sensor 951 that senses a speed of the clean grain fan 120. The machine sensors 982 can include a concave gap sensor 953 that senses a gap between the rotor 112 and the concave 114 on the agricultural harvester 100. The machine sensors 982 can include a chaffer gap sensor 955 that senses a size of openings in the chaffer sieve 122. The machine sensors 982 can include a threshing rotor speed sensor 957 that senses a rotor speed of the rotor 112. The machine sensors 982 can include a rotor pressure sensor 959 that senses a pressure used to drive the rotor 112. The machine sensors 982 can include a screen gap sensor 961 that senses a size of openings in the screen 124. The machine sensors 982 can include a MOG moisture sensor 963 that senses a moisture level of MOG passing through the agricultural harvester 100. The machine sensors 982 can include a machine orientation sensor 965 that senses an orientation of the agricultural harvester 100. The machine sensors 982 can include a material feed rate sensor 967 that senses a feed rate of material as it travels through the feedhouse 106, clean grain elevator 130, or other places in the agricultural harvester 100. The machine sensors 982 can include a biomass sensor 969 that senses biomass traveling through the feedhouse 106, the separator 116, or other places in the agricultural harvester 100. The machine sensors 982 can include a fuel consumption sensor 971 that senses a fuel consumption rate of the agricultural harvester 100 over time. The machine sensors 982 can include a power utilization sensor 973 that senses a power utilization in the agricultural harvester 100 (such as which subsystems are utilizing power), or a rate at which the subsystems are utilizing power, or a power distribution between subsystems in the agricultural harvester 100. The machine sensors 982 can include a tire pressure sensor 977 that senses an inflation pressure in the tires 144 of the agricultural harvester 100. The machine sensors 982 can include a variety of other machine performance sensors or machine characteristic sensors (as shown by block 975).Machine performance and machine characteristic sensors 975 can sense machine performance or characteristics of the agricultural harvester 100.

[0138] The harvested material property sensors 984 can sense characteristics of the severed crop material as the crop material is being processed by the agricultural harvester 100. Crop properties can include things such as crop type, crop moisture, grain quality (e.g., broken grain), MOG levels, grain constituents (such as starch and protein), MOG moisture, and other crop material properties. Other sensors can sense stalk "toughness," adhesion of corn to the ear, and other characteristics that can be beneficially used to control processing to achieve better grain capture, reduced grain damage, reduced power consumption, reduced grain loss, and the like.

[0139] Field and soil property sensors 985 can sense characteristics of the field and soil. Field and soil properties can include soil moisture, soil compaction, presence and location of standing water, soil type, and other soil and field characteristics.

[0140] Environmental characteristic sensors 987 can sense one or more environmental characteristics. Environmental characteristics can include things such as wind direction and speed, precipitation, fog, dust levels or other obscuring matter, or other environmental characteristics.

[0141] Figure 7 A flowchart illustrating one example of the operation of the prediction model generator 210 and the prediction map generator 212 in generating one or more prediction models 426 and one or more functional prediction maps 436, 437, 438, and 440 is shown. At block 442, the prediction model generator 210 and the prediction map generator 212 receive an information map 258. The information map 258 can be the historical power map 333, the predicted power map 360, or a prior operation map 400 created using data obtained during a prior operation in the field. Other maps can be received and indicated with block 401.

[0142] At block 444, the prediction model generator 210 receives sensor signals containing sensor data from the field sensors 208. The field sensors can be one or more of the temperature sensors 401 or other sensors 408. The temperature sensors 401 sense temperature. The prediction model generator 210 can also receive other field sensor inputs (as indicated by block 452).

[0143] At block 454, the processing system 406 processes the data contained in the one or more sensor signals received from the one or more field sensors 208 to obtain processed data 409, as Figure 6AThe data contained in the one or more sensor signals can be in a raw format that is processed to receive processed data 409. For example, a temperature sensor signal includes resistance data that can be processed into temperature data. In other examples, the processing can include digitizing, encoding, formatting, scaling, filtering, or classifying the data. The processed data 409 can be indicative of one or more of a temperature sensor, a concave gap, a residue handling characteristic, a crop engagement characteristic, or an operator input command. The processed data 409 is provided to the predictive model generator 210.

[0144] Returning to Figure 7 At block 456, the predictive model generator 210 also receives a geographic location 334 from the geographic location sensor 204, as shown in FIG. 4. The geographic location 334 can be associated with the geographic location from which the sensed variable sensed by the field sensor 208 is obtained. For example, the predictive model generator 210 can obtain the geographic location 334 from the geographic location sensor 204 and determine the precise geographic location from which the processed data 409 is derived based on machine delays, machine speed, etc. Figure 6A

[0145] At block 458, the predictive model generator 210 generates one or more predictive models 426 that model the relationship between the mapped values in the information graph and the characteristics represented in the processed data 409. For example, in some cases, the mapped values in the information graph can be power characteristics and the predictive model generator 210 generates a predictive model using the mapped values of the information graph and the characteristics sensed by the field sensor 208 (as represented in the processed data 490) or related characteristics, such as characteristics related to the characteristics sensed by the field sensor 208.

[0146] 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 can be a functional predictive temperature map generator 425, a functional predictive sensor data map 429, a functional predictive operator command map 440, or any combination of these maps. The functional predictive temperature map generator 425 predicts a desired temperature at different locations in the field. The functional predictive sensor data map 429 predicts a sensor data value or a characteristic value indicated by a sensor data value at different locations in the field. The functional predictive operator command map 440 predicts a possible operator command input at different locations in the field. Further, one or more of the functional predictive maps 425, 429, and 440 can be generated during the course of the agricultural operation. Thus, as the agricultural harvester 100 moves through the field performing the agricultural operation, the one or more predictive maps 425, 429, and 440 are generated as the agricultural operation is performed.

[0147] ​At block 468, the prediction map generator 212 outputs one or more functional prediction maps 425, 429, and 440. At block 470, the prediction map generator 212 can configure the maps for presentation to the operator 260 or another user and possible interaction by the operator 260 or another user. At block 472, the prediction map generator 212 can configure the maps for use by the control system 214. At block 474, the prediction map generator 212 can provide one or more prediction maps 425, 429, and 440 to the control zone generator 213 for generating control zones. At block 476, the prediction map generator 212 otherwise configures one or more prediction maps 425, 429, and 440. In an example where one or more functional prediction maps 425, 429, and 440 are provided to the control zone generator 213, the one or more functional prediction maps 425, 429, and 440 and the control zones included therein (represented by the corresponding maps 265, as described above) can be presented to the operator 260 or another user or also provided to the control system 214.

[0148] At block 478, the control system 214 then generates control signals to control the controllable subsystems based on one or more functional prediction maps 436, 437, 438, and 440 (or the functional prediction maps 425, 429, and 440 with control zones) and inputs from the geo-location sensor 204.

[0149] In an example in which the control system 214 receives a function prediction map, the path planning controller 234 controls the steering subsystem 252 to cause the agricultural harvester 100 to steer. In another example in which the control system 214 receives a function prediction map, the residue system controller 244 controls the residue subsystem 138. In another example in which the control system 214 receives a function prediction map, the setting controller 232 controls the threshing machine settings of the threshing machine 110. In another example in which the control system 214 receives a function prediction map, the setting controller 232 or another controller 246 controls the material handling subsystem 125. In another example in which the control system 214 receives a function prediction map, the setting controller 232 controls the crop cleanout subsystem. In another example in which the control system 214 receives a function prediction map, the machine cleanout controller 245 controls the machine cleanout subsystem 254 on the agricultural harvester 100. In another example in which the control system 214 receives a function prediction map, the communication system controller 229 controls the communication system 206. In another example in which the control system 214 receives a function prediction map, the operator interface controller 231 controls the operator interface mechanisms 218 on the agricultural harvester 100. In another example in which the control system 214 receives a function prediction map, the header deck position controller 242 controls the machine / header actuators to control the header deck on the agricultural harvester 100. In another example in which the control system 214 receives a function prediction map, the belt conveyor belt controller 240 controls the machine / header actuators to control the belt conveyor belt on the agricultural harvester 100. In another example in which the control system 214 receives a function prediction map, the cooling controller 235 controls the cooling subsystem 255 on the agricultural harvester 100. For example, the cooling controller 235 can adjust the cooling fan speed. Or for example, the cooling controller 235 can adjust the cooling fan pitch. Or for example, the cooling controller 235 can adjust the fluid flow through a radiator or other heat dissipating device. In another example in which the control system 214 receives a function prediction map, the other controller 246 controls other controllable subsystems 256 on the agricultural harvester 100.

[0150] Figure 8A block diagram illustrating one example of a control zone generator 213 is shown. The control zone generator 213 includes a work machine actuator (WMA) selector 486, a control zone generation system 488, and a dynamic regime generation system 490. The control zone generator 213 can also include other items 492. The control zone generation system 488 includes a control zone criteria identifier component 494, a control zone boundary definition component 496, a target setting identifier component 498, and other items 520. The dynamic regime generation system 490 includes a dynamic regime criteria identification component 522, a dynamic regime boundary definition component 524, a setting resolver identifier component 526, and other items 528. Before describing the overall operation of the control zone generator 213 in more detail, a brief description of some of the items in the control zone generator 213 and their respective operations will first be provided.

[0151] The agricultural harvester 100 or other work machine can have a plurality of different types of controllable actuators that perform different functions. The controllable actuators on the agricultural harvester 100 or other work machine are collectively referred to as work machine actuators (WMAs). Each WMA can be independently controlled based on values on the function prediction map, or the WMAs can be controlled in groups based on one or more values on the function prediction map. Thus, the control zone generator 213 can generate control zones corresponding to each individually controllable WMA, or to groups of WMAs that are controlled in coordination with one another.

[0152] The WMA selector 486 selects a WMA or group of WMAs for which a corresponding control zone is to be generated. The control zone generation system 488 then generates a control zone for the selected WMA or group of WMAs. Different criteria can be used in identifying the control zone for each WMA or group of WMAs. For example, for one WMA, the WMA response time can be used as a criterion for defining the boundaries of a control zone. In another example, a wear characteristic (e.g., how much a particular actuator or mechanism wears due to its movement) can be used as a criterion for identifying the boundaries of a control zone. The control zone criteria identifier component 494 identifies the particular criteria that will be used to define a control zone for the selected WMA or group of WMAs. The control zone boundary definition component 496 processes values on the function prediction map under analysis to define the boundaries of a control zone on the function prediction map under analysis based on the values on the function prediction map under analysis and based on the control zone criteria for the selected WMA or group of WMAs.

[0153] The target setting identifier component 498 sets the value of the target setting that will be used to control the WMA or WMA group in the different control zones. For example, if the selected WMA is the propulsion system 250 and the function prediction map in the analysis is the function prediction speed map 438, the target setting in each control zone can be a target speed setting based on the speed values contained in the function prediction speed map 238 within the identified control zone.

[0154] In some examples, where the agricultural harvester 100 is controlled based on the current or future location of the agricultural harvester 100, multiple target settings are possible for a WMA at a given location. In this case, the target settings can have different values and can compete with each other. Therefore, the target settings need to be resolved so that only a single target setting is used to control the WMA. For example, where the WMA is an actuator that is controlled in the propulsion system 250 to control the speed of the agricultural harvester 100, there can be multiple different competing sets of criteria that are considered by the control zone generation system 488 when identifying the control zones and the target settings for the selected WMA in the control zones. For example, different target settings for controlling the speed of the machine can be generated based on, for example, detected or predicted feed rate values, detected or predicted fuel efficiency values, detected or predicted grain loss values, or a combination of these values. However, at any given time, the agricultural harvester 100 cannot travel over the ground at multiple speeds simultaneously. Rather, at any given time, the agricultural harvester 100 travels at a single speed. Therefore, one of the competing target settings is selected to control the speed of the agricultural harvester 100.

[0155] Therefore, in some examples, the dynamic zone generation system 490 generates dynamic zones to resolve the multiple different competing target settings. The dynamic zone criteria identification component 522 identifies criteria for establishing a dynamic zone for the selected WMA or WMA group on the function prediction map in the analysis. Some criteria that can be used to identify or define a dynamic zone include, for example, crop type or crop class based on a planting map, or another source of crop type or crop class, weed type, weed intensity, soil type, or crop status such as whether the crop is laid flat, partially laid flat, or standing. Just as each WMA or WMA group can have a corresponding control zone, different WMA or WMA groups can have a corresponding dynamic zone. The dynamic zone boundary definition component 524 identifies the boundaries of the dynamic zone on the function prediction map in the analysis based on the dynamic zone criteria identified by the dynamic zone criteria identification component 522.

[0156] In some examples, the dynamic zones can overlap one another. For example, a crop class dynamic zone can overlap some or all of a crop status dynamic zone. In such examples, different dynamic zones can be assigned to a priority hierarchy such that, in the event of overlap of two or more dynamic zones, the dynamic zone assigned a higher position or importance in the priority hierarchy is prioritized over the dynamic zone with a lower position or importance in the priority hierarchy. The priority hierarchy of the dynamic zones can be set manually or can be set automatically using a rules-based system, a model-based system, or other system. As one example, in the event of overlap of a lodged crop dynamic zone and a crop class dynamic zone, the lodged crop dynamic zone can be assigned a greater importance in the priority hierarchy than the crop class dynamic zone such that the lodged crop dynamic zone is prioritized.

[0157] Further, for a given WMA or group of WMAs, each dynamic zone can have a unique settings resolver. The settings resolver identifier component 526 identifies a particular settings resolver for each dynamic zone identified on the functional prediction map under analysis and identifies the particular settings resolver for the selected WMA or group of WMAs.

[0158] Once the settings resolver for a particular dynamic zone is identified, the settings resolver can be used to resolve a competing target setting in which more than one target setting is identified based on the control zone. Different types of settings resolvers can have different forms. For example, the settings resolver identified for each dynamic zone can include a human selection resolver in which the competing target setting is presented to an operator or other user for resolution. In another example, the settings resolver can include a neural network or other artificial intelligence or machine learning system. In such a case, the settings resolver can resolve the competing target setting based on a predicted quality metric or historical quality metric corresponding to each of the different target settings. As an example, an increased vehicle speed setting can reduce the time to harvest a field and reduce the corresponding time-based labor and equipment costs, but can increase grain loss. A decreased vehicle speed setting can increase the time to harvest a field and increase the corresponding time-based labor and equipment costs, but can reduce grain loss. When grain loss or harvesting time is selected as the quality metric, the predicted value or historical value of the selected quality metric can be used to resolve the speed setting given two competing vehicle speed setting values. In certain cases, the settings resolver can be a set of threshold rules that can be used in place of or in addition to the dynamic zones. An example of a threshold rule can be expressed as follows:

[0159] If the predicted biomass value within 20 feet of header 20 of agricultural harvester 100 is greater than x kilograms (where x is a selected or predetermined value), then the target setting value based on the feed rate, rather than other competing target setting values, is used, otherwise the target setting value based on the grain loss, rather than other competing target setting values, is used.

[0160] The setting resolver can be a logical component that executes logical rules in identifying the target setting. For example, the setting resolver can resolve the target setting while attempting to minimize the harvesting time or minimize the total harvesting cost or maximize the harvested grain, or other variables calculated as a function of different candidate target settings. The harvesting time can be minimized when the amount of harvesting completed is reduced to or below a selected threshold. The total harvesting cost can be minimized when the total harvesting cost is reduced to or below a selected threshold. The harvested grain can be maximized when the amount of harvested grain is increased to or above a selected threshold.

[0161] Figure 9 is a flowchart illustrating one example of the operation of control zone generator 213 in generating control zones and dynamic zones for control zone generator 213 receiving a graph for zone processing, e.g., a graph in analysis.

[0162] At block 530, control zone generator 213 receives a graph in analysis for processing. In one example, as shown at block 532, the graph in analysis is a functional prediction graph. For example, the graph in analysis can be one of functional prediction graphs 436, 437, 438, or 440. Block 534 indicates that the graph in analysis can also be other graphs.

[0163] At block 536, the WMA selector 486 selects a WMA or group of WMAs for which a control zone is to be generated on the map in the analysis. At block 538, the control zone criteria identification component 494 obtains the control zone definition criteria for the selected WMA or group of WMAs. Block 540 indicates such an example in which the control zone criteria are or include the wear characteristics of the selected WMA or group of WMAs. Block 542 indicates such an example in which the control zone definition criteria are or include the magnitude and variation of the input source data, such as the magnitude and variation of values on the map in the analysis or the magnitude and variation of inputs from various field sensors 208. Block 544 indicates such an example in which the control zone definition criteria are or include physical machine characteristics, such as the physical dimensions of the machine, the speed of different subsystem operations, or other physical machine characteristics. Block 546 indicates such an example in which the control zone definition criteria are or include the responsiveness of the selected WMA or group of WMAs in reaching a set value of a new command. Block 548 indicates such an example in which the control zone definition criteria are or include a machine performance metric. Block 550 indicates such an example in which the control zone definition criteria are or include operator preferences. Block 552 indicates such an example in which the control zone definition criteria are also or include other items. Block 549 indicates such an example in which the control zone definition criteria are time-based, meaning that the agricultural harvester 100 will not cross the boundary of a control zone until a selected amount of time has elapsed since the agricultural harvester 100 entered a particular control zone. In some cases, the selected amount of time can be a minimum amount of time. Thus, in some cases, the control zone definition criteria can prevent the agricultural harvester 100 from crossing the boundary of a control zone until at least the selected amount of time has elapsed. Block 551 indicates such an example in which the control zone definition criteria are based on a selected size value. For example, control zone definition criteria based on a selected size value can exclude the definition of control zones that are smaller than the selected size. In some cases, the selected size can be a minimum size.

[0164] At block 554, the dynamic zone criteria identification component 522 obtains the dynamic zone definition criteria for the selected WMA or group of WMAs. Block 556 indicates such an example in which the dynamic zone definition criteria are based on manual input from the operator 260 or another user. Block 558 shows such an example in which the dynamic zone definition criteria are based on crop type or crop class. Block 560 shows such an example in which the dynamic zone definition criteria are based on weed type or weed intensity or both. Block 562 shows such an example in which the dynamic zone definition criteria are based on or include crop status. Block 564 indicates such an example in which the dynamic zone definition criteria are also or include other criteria. For example, the dynamic zone definition criteria can be based on terrain characteristics or soil characteristics.

[0165] At block 566, the control zone boundary definition component 496 generates the boundaries of the control zones on the map in the analysis based on the control zone criteria. The dynamic zone boundary definition component 524 generates the boundaries of the dynamic zones on the map in the analysis based on the dynamic zone criteria. Block 568 indicates examples in which zone boundaries are identified for both control zones and dynamic zones. Block 570 shows that the target setting identifier component 498 identifies the target setting for each of the control zones. Control zones and dynamic zones can also be generated in other ways, and this is indicated by block 572.

[0166] At block 574, the setting resolver identifier component 526 identifies the setting resolver for the selected WMA in each dynamic zone defined by the dynamic zone boundary definition component 524. As discussed above, the dynamic zone resolver can be a human resolver 576, an artificial intelligence or machine learning system resolver 578, a resolver based on the predicted or historical quality of each competing target setting 580, a rule-based resolver 582, a performance criteria-based resolver 584, or other resolver 586.

[0167] At block 588, the WMA selector 486 determines whether there are more WMAs or groups of WMAs to process. If there are additional WMAs or groups of WMAs that need to be processed, the process returns to block 436 in which the next WMA or group of WMAs for which control zones and dynamic zones are to be defined is selected. When there are no additional WMAs or groups of WMAs for which control zones or dynamic zones are to be generated left, the process moves to block 590 in which the control zone generator 213 outputs a map for each of the WMA or groups of WMAs with control zones, target settings, dynamic zones, and setting resolvers. As discussed above, the output map can be presented to the operator 260 or another user; the output map can be provided to the control system 214; or the output map can be output in other ways.

[0168] Figure 10 One example is shown in which the control system 214 controls the operation of the agricultural harvester 100 based on the map output by the control zone generator 213. Thus, at block 592, the control system 214 receives the map of the work site. In some cases, the map can be a functional prediction map that can include control zones and dynamic zones (as shown in block 594). In some cases, the received map can be a functional prediction map that excludes control zones and dynamic zones. Block 596 indicates examples in which the received map of the work site can be an information map with control zones and dynamic zones identified thereon. Block 598 indicates examples in which the received map can include multiple different maps or multiple different map layers. Block 610 indicates examples in which the received map can also take other forms.

[0169] At block 612, the control system 214 receives a sensor signal from the geo-location sensor 204. The sensor signal from the geo-location sensor 204 can include data indicative of a geo-location 614 of the agricultural harvester 100, a speed 616 of the agricultural harvester 100, a heading 618 of the agricultural harvester 100, or other information 620. At block 622, the zone controller 247 selects a dynamic zone, and at block 624, the zone controller 247 selects a control zone on the map based on the geo-location sensor signal. At block 626, the zone controller 247 selects a WMA or group of WMAs to be controlled. At block 628, the zone controller 247 obtains one or more target settings for the selected WMA or group of WMAs. The target settings obtained for the selected WMA or group of WMAs can come from a variety of different sources. For example, block 630 illustrates an example in which one or more of the target settings for the selected WMA or group of WMAs are based on input from the control zone on the map from the job site. Block 632 illustrates an example in which one or more of the target settings are obtained from manual input by the operator 260 or another user. Block 634 illustrates an example in which the target settings are obtained from the field sensors 208. Block 636 illustrates an example in which one or more of the target settings are obtained from one or more sensors on other machines simultaneously operating in the same field as the agricultural harvester 100 or from one or more sensors on machines that have operated in the same field in the past. Block 638 illustrates an example in which the target settings are also obtained from other sources.

[0170] At block 640, the zone controller 247 accesses the setting resolver for the selected dynamic zone and controls the setting resolver to resolve the competing target settings into resolved target settings. As discussed above, in some cases, the setting resolver can be a human resolver, in which case the zone controller 247 controls the operator interface mechanism 218 to present the competing target settings to the operator 260 or another user for resolution. In some cases, the setting resolver can be a neural network or other artificial intelligence or machine learning system, and the zone controller 247 submits the competing target settings to the neural network, artificial intelligence, or machine learning system for selection. In certain cases, the setting resolver can be based on predicted quality metrics or historical quality metrics, based on threshold rules, or based on a logic component. In any of these latter examples, the zone controller 247 executes the setting resolver to obtain the resolved target settings based on predicted quality metrics or historical quality metrics, based on threshold rules, or in the case of using a logic component.

[0171] At block 642, with the resolved target settings identified by the zone controller 247, the zone controller 247 provides the resolved target settings to other controllers in the control system 214, which generate control signals based on the resolved target settings and apply the control signals to the selected WMA or WMA group. For example, with the selected WMA being a machine or header actuator 248, the zone controller 247 provides the resolved target settings to the settings controller 232 or the header / actual controller 238 or both to generate control signals based on the resolved target settings, and those generated control signals are applied to the machine or header actuator 248. At block 644, if additional WMAs or additional WMA groups are to be controlled at the current geographic location of the agricultural harvester 100 (as detected at block 612), the process returns to block 626, where the next WMA or WMA group is selected. The process represented by blocks 626 through 644 continues until all WMAs or WMA groups to be controlled at the current geographic location of the agricultural harvester 100 have been addressed. If no additional WMAs or WMA groups remain to be controlled at the current geographic location of the agricultural harvester 100, the process proceeds to block 646, where the zone controller 247 determines whether additional control zones to be considered exist in the selected dynamic zone. If additional control zones to be considered exist, the process returns to block 624, where the next control zone is selected. If no additional control zones need to be considered, the process proceeds to block 648, where a determination is made as to whether additional dynamic zones need to be considered. The zone controller 247 determines whether additional dynamic zones need to be considered. If additional dynamic zones need to be considered, the process returns to block 622, where the next dynamic zone is selected.

[0172] At block 650, the zone controller 247 determines whether the operation being performed by the agricultural harvester 100 is complete. If not, the zone controller 247 determines whether the control zone criteria have been met to continue processing, as shown at block 652. For example, as mentioned above, the control zone definition criteria can include criteria defining when the agricultural harvester 100 can cross the control zone boundary. For example, whether the agricultural harvester 100 can cross the control zone boundary can be defined by a selected time period, meaning that the agricultural harvester 100 is prevented from crossing the zone boundary until the selected amount of time has elapsed. In this case, at block 652, the zone controller 247 determines whether the selected time period has elapsed. Additionally, the zone controller 247 can continuously perform the processing. Thus, the zone controller 247 does not wait for any particular time period before continuing to determine whether the operation of the agricultural harvester 100 is complete. At block 652, the zone controller 247 determines that it is time to continue processing, and then the processing continues at block 612, where the zone controller 247 again receives input from the geo-location sensor 204. It should also be understood that the zone controller 247 can use a multiple-input, multiple-output controller to simultaneously control the WMAs and WMA groups, rather than sequentially controlling the WMAs and WMA groups.

[0173] Figure 11 FIG. 27 is a block diagram illustrating one example of an operator interface controller 231. In the illustrated example, the operator interface controller 231 includes an operator input command processing system 654, other controller interaction system 656, a speech processing system 658, and an action signal generator 660. The operator input command processing system 654 includes a speech handling system 662, a touch gesture processing system 664, and other items 666. The other controller interaction system 656 includes a controller input processing system 668 and a controller output generator 670. The speech processing system 658 includes a trigger detector 672, a recognition component 674, a synthesis component 676, a natural language understanding system 678, a dialog management system 680, and other items 682. The action signal generator 660 includes a visual control signal generator 684, an audio control signal generator 686, a haptic control signal generator 688, and other items 690. A brief description of some of the items in the operator interface controller 231 and their associated operations is provided before describing the operation of the example operator interface controller 231 in handling various operator interface actions, which is illustrated in FIG. 28. Figure 11

[0174] ​The operator input command processing system 654 detects operator inputs on the operator interface mechanisms 218 and processes these command inputs. The speech handling system 662 detects speech inputs and processes interactions with the speech processing system 658 to process speech command inputs. The touch gesture processing system 664 detects touch gestures on touch sensitive elements in the operator interface mechanisms 218 and processes these command inputs.

[0175] The other controller interaction system 656 processes interactions with other controllers in the control system 214. The controller input processing system 668 detects and processes inputs from other controllers in the control system 214 and the controller output generator 670 generates outputs and provides these outputs to other controllers in the control system 214. The speech processing system 658 recognizes speech inputs, determines the meaning of these inputs, and provides outputs indicating the meaning of the verbal inputs. For example, the speech processing system 658 can recognize a speech input from the operator 260 as a set change command where the operator 260 is commanding the control system 214 to change a setting of a controllable subsystem 216. In such an example, the speech processing system 658 recognizes the content of the verbal command, identifies the meaning of the command as a set change command, and provides the meaning of the input back to the speech handling system 662. The speech handling system 662 in turn interacts with the controller output generator 670 to provide command outputs to the appropriate controller in the control system 214 to complete the verbal set change command.

[0176] The voice processing system 658 can be invoked in a variety of different ways. For example, in one example, the voice handling system 662 continuously provides input from a microphone (as one of the operator interface mechanisms 218) to the voice processing system 658. The microphone detects voice from the operator 260, and the voice handling system 662 provides the detected voice to the voice processing system 658. The trigger detector 672 detects a trigger that indicates that the voice processing system 658 is invoked. In some cases, when the voice processing system 658 receives continuous voice input from the voice handling system 662, the speech recognition component 674 performs continuous speech recognition on all speech spoken by the operator 260. In some cases, the voice processing system 658 is configured to be invoked using a wake-up word. That is, in some cases, the operation of the voice processing system 658 can be initiated based on recognizing a selected spoken word, referred to as a wake-up word. In such examples, where the recognition component 674 recognizes the wake-up word, the recognition component 674 provides an indication to the trigger detector 672 that the wake-up word has been recognized. The trigger detector 672 detects that the voice processing system 658 has been invoked or triggered by the wake-up word. In another example, the voice processing system 658 can be invoked by the operator 260 actuating an actuator on a user interface mechanism, such as by touching an actuator on a touch-sensitive display screen, by pressing a button, or by providing another trigger input. In such examples, when the trigger input via the user interface mechanism is detected, the trigger detector 672 can detect that the voice processing system 658 has been invoked. The trigger detector 672 can also detect that the voice processing system 658 has been invoked in other ways.

[0177] Once the voice processing system 658 is invoked, voice input from the operator 260 is provided to the speech recognition component 674. The speech recognition component 674 recognizes linguistic elements in the voice input, such as words, phrases, or other linguistic units. The natural language understanding system 678 identifies a meaning of the recognized voice. The meaning can be a natural language output, a command output that identifies a command reflected in the recognized voice, a value output that identifies a value in the recognized voice, or any of a variety of other outputs that reflect an understanding of the recognized voice. For example, more generally, the natural language understanding system 678 and the voice processing system 568 can understand a meaning of voice recognized in the context of the agricultural harvester 100.

[0178] In some examples, the speech processing system 658 can also generate output that is presented to the user via the navigational operator 260 based on the speech input. For example, the dialog management system 680 can generate and manage a dialog with the user in order to identify what the user wants to do. The dialog box can disambiguate user commands, identify one or more particular values needed to perform the user's command, or obtain other information from the user or provide other information to the user, or both. The synthesis component 676 can generate speech synthesis that can be presented to the user via an audio operator interface mechanism such as a speaker. Thus, the dialog managed by the dialog management system 680 can be exclusively a spoken dialog or a combination of a visual dialog and a spoken dialog.

[0179] The action signal generator 660 generates action signals to control the operator interface mechanisms 218 based on output from one or more of the operator input command processing system 654, the other controller interaction system 656, and the speech processing system 658. The visual control signal generator 684 generates control signals to control visual items in the operator interface mechanisms 218. The visual items can be lights, display screens, warning indicators, or other visual items. The audio control signal generator 686 generates output to control audio elements of the operator interface mechanisms 218. The audio elements include speakers, audible alert 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 vibratory elements that can be used to vibrate, for example, the operator's seat, steering wheel, pedals, or joystick used by the operator. The haptic elements can include tactile feedback or force feedback elements that provide tactile or force feedback to the operator via the operator interface mechanisms. The haptic elements can also include a wide variety of other haptic elements.

[0180] Figure 12 is a flowchart showing one example of the operation of the operator interface controller 231 in generating an operator interface display on the operator interface mechanisms 218, which can include a touch-sensitive display screen. Figure 12 One example of how the operator interface controller 231 can detect and process operator interaction with the touch-sensitive display screen is also shown.

[0181] At block 692, the operator interface controller 231 receives a map. Block 694 indicates an example in which the map is a function prediction map, while block 696 indicates an example in which the map is another type of map. At block 698, the operator interface controller 231 receives input from the geo-location sensor 204 identifying a geo-location of the agricultural harvester 100. As shown in block 700, the input from the geo-location sensor 204 can include a heading and a position of the agricultural harvester 100. Block 702 indicates an example in which the input from the geo-location sensor 204 includes a speed of the agricultural harvester 100, while block 704 indicates an example in which the input from the geo-location sensor 204 includes other items.

[0182] At block 706, the visual control signal generator 684 in the operator interface controller 231 controls the touch-sensitive display screen in the operator interface mechanism 218 to generate a display showing all or a portion of the field represented by the received map. Block 708 indicates that the displayed field can include a current position marker showing a current position of the agricultural harvester 100 relative to the field. Block 710 indicates an example in which the displayed field includes a next work unit marker identifying a next work unit (or area on the field) in which the agricultural harvester 100 is to operate. Block 712 indicates an example in which the displayed field includes a coming area display portion showing areas that have not yet been processed by the agricultural harvester 100, while block 714 indicates an example in which the displayed field includes a previously visited display portion representing areas of the field that have already been processed by the agricultural harvester 100. Block 716 indicates an example in which the displayed field displays various characteristics of the field having geo-referenced locations on the map. For example, if the received map is a power map, the displayed field can show different power characteristics present in the field geo-referenced within the displayed field. The mapped characteristics can be shown in previously visited areas (as shown in block 714), coming areas (as shown in block 712), and next work units (as shown in block 710). Block 718 indicates an example in which the displayed field includes other items as well.

[0183] Figure 13 is a diagram showing one example of a user interface display 720 that can be generated on a touch-sensitive display screen. In other embodiments, the user interface display 720 can be generated on other types of displays. The touch-sensitive display screen can be mounted in an operator's compartment of the agricultural harvester 100 or on a mobile device or elsewhere. Before continuing with the description of the flowchart shown in Figure 12 the user interface display 720 will be described.

[0184] In Figure 13In the example shown in FIG. 7, the user interface display 720 shows that the touch- sensitive display includes display features for operating the microphone 722 and the speaker 724. Thus, the touch-sensitive display can be communicably coupled to the microphone 722 and the speaker 724. The box 726 indicates that the touch-sensitive display can include a variety of user interface control actuators, such as buttons, keypads, soft keypads, links, icons, switches, etc. The operator 260 can actuate the user interface control actuators to perform various functions.

[0185] In Figure 13 In the example shown in FIG. 7, the user interface display 720 includes a field display portion 728 that displays at least a portion of the field in which the agricultural harvester 100 is operating. The field display portion 728 is shown with a current position marker 708 that corresponds to the current position of the agricultural harvester 100 in the portion of the field shown in the field display portion 728. In one example, the operator can control the touch-sensitive display to zoom in on a portion of the field display portion 728 or to pan or scroll the field display portion 728 to display different portions of the field. A next work unit 730 is shown as a field region directly in front of the current position marker 708 of the agricultural harvester 100. The current position marker 708 can also be configured to identify a direction of travel of the agricultural harvester 100, a speed of travel of the agricultural harvester 100, or both. In Figure 13 In the example shown in FIG. 7, 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 the direction of travel of the agricultural harvester 100.

[0186] The size of the next work unit 730 labeled on the field display portion 728 can vary based on a variety of different criteria. For example, the size of the next work unit 730 can vary based on the speed of travel of the agricultural harvester 100. Thus, when the agricultural harvester 100 is traveling faster, the area of the next work unit 730 can be larger than if the agricultural harvester 100 is traveling slower. The field display portion 728 is also shown displaying a previously visited region 714 and an upcoming region 712. The previously visited region 714 represents an area that has already been harvested, while the upcoming region 712 represents an area that still needs to be harvested. The field display portion 728 is also shown displaying different characteristics of the field. In Figure 13 In the example shown in FIG. 7, the graph being displayed is a temperature graph. Thus, a plurality of different temperature markers are displayed on the field display portion 728. There is a set of temperature display markers 732 shown in the previously visited region 714. There is also a set of temperature display markers 732 shown in the upcoming region 712, and a set of temperature display markers 732 shown in the next work unit 730. Figure 13The temperature display indicia 732 is shown to be composed of different symbols that indicate similar temperature regions. In the example shown in FIG. 3, the! symbol indicates a high temperature region; the * symbol indicates a medium temperature region; and the # symbol indicates a low temperature region. Thus, the field display portion 728 shows different measured or predicted temperatures at different regions within the field. As previously mentioned, the display indicia 732 can be composed of different symbols, and as described below, the symbols can be any display feature, such as different colors, shapes, patterns, intensities, text, icons, or other display features. In some cases, each location of the field can have a display indicia associated therewith. Thus, in some cases, a display indicia can be provided at each location of the field display portion 728 to identify the nature of the characteristic mapped for each particular location of the field. Thus, the present disclosure includes providing a display indicia, such as (as in the present example) the loss level display indicia 732 at one or more locations on the field display portion 728 to identify the nature, degree, etc. of the characteristic being displayed to identify the characteristic at the corresponding location in the field being displayed. Figure 11

[0187] In the example of FIG. 3, the user interface display 720 also has a control display portion 738. The control display portion 738 allows the operator to view information and interact with the user interface display 720 in various ways. Figure 13

[0188] The actuators and display indicia in the portion 738 can be displayed as, for example, separate items, a fixed list, a scrollable list, a drop-down menu, or a drop-down list. In the example of FIG. 3, the control display portion 738 includes a plurality of display indicia 740 that are displayed in a fixed list. The display indicia 740 can be displayed in any order, and can be displayed in any manner. In the example of FIG. 3, the display indicia 740 are displayed in a vertical list. In some cases, the display indicia 740 can be displayed in a horizontal list. In some cases, the display indicia 740 can be displayed in a grid or other arrangement. In some cases, the display indicia 740 can be displayed in a drop-down menu or drop-down list. Figure 13 ​​In the example shown, display portion 738 shows information corresponding to three different temperatures of the three symbols mentioned above. Display portion 738 also includes a set of touch-sensitive actuators that operator 260 can interact with by touching the set of touch-sensitive actuators. For example, operator 260 can touch a touch-sensitive actuator with a finger to activate the corresponding touch-sensitive actuator. Above display portion 738 are engine tab 762, propulsion tab 764, clean grain tab 766, residue tab 768, and other tab 770. Activating one of the tabs can modify which values are displayed in portions 728 and 738. For example, as shown, engine tab 762 is activated, and thus the values mapped on portion 728 and displayed in portion 738 correspond to the temperature of the engine of agricultural harvester 100. When operator 260 touches tab 764, touch gesture processing system 664 updates portions 728 and 738 to display temperatures related to propulsion subsystem 250. When operator 260 touches tab 766, touch gesture processing system 664 updates portions 728 and 738 to display temperatures related to threshing subsystem 254. When operator 260 touches tab 768, touch gesture processing system 664 updates portions 728 and 738 to display temperatures related to residue subsystem 138. When operator 260 touches tab 770, touch gesture processing system 664 updates portions 728 and 738 to display temperatures related to another set of components of agricultural harvester 100.

[0189] Column 746 displays a symbol corresponding to each temperature category being tracked on the field display portion 728. The designator column 748 shows a designator (which can be a textual designator or other designator) that identifies the temperature category. Without limitation, the temperature symbol in column 746 and the designator in column 748 can include any display feature, such as a different color, shape, pattern, intensity, text, icon, or other display feature. The values displayed in column 750 can be predicted temperature values or temperature values measured by the field sensors 208. In one example, the operator 260 can select a particular portion of the field display portion 728 for which to display the values in column 750. Thus, the values in column 750 can correspond to the values in display portions 712, 714, or 730. Column 752 displays action thresholds. The action thresholds in column 752 can be thresholds that correspond to the measured values in column 750. If the measured values in column 750 satisfy the corresponding action threshold in column 752, the control system 214 takes the action identified in column 754. In some cases, the measured value can satisfy the corresponding action threshold by satisfying or exceeding the corresponding action threshold. In one example, the operator 260 can select a threshold, for example, by touching the threshold in column 752, in order to change the threshold. Once selected, the operator 260 can change the threshold. The threshold in column 752 can be configured such that the designated action is performed when the measured value 750 exceeds the threshold, equals the threshold, or is less than the threshold.

[0190] Similarly, the operator 260 can touch the action identifier in column 754 to change the action to be taken. There can be a variety of actions that can be taken when the threshold is satisfied. For example, at the bottom of column 754, a reduce cooling fan speed is identified as the action to be taken if the measured value in column 750 satisfies the threshold in column 752.

[0191] The actions that can be set in column 754 can be any of a variety of different types of actions. For example, the actions can include an inhibit action that, when executed, prevents the agricultural harvester 100 from further harvesting in the area. The actions can include a speed change action that, when executed, changes the speed of travel of the agricultural harvester 100 through the field. The actions can include a setting change action to change the settings of an internal actuator or another WMA or group of WMAs, or to implement a setting change action to change the settings of the header. These are merely examples, and a wide variety of other actions are contemplated herein.

[0192] The display markers shown on the user interface display 720 can be visually controlled. Visually controlling the interface display 720 can be performed to capture the attention of the operator 260. For example, the display markers can be controlled to modify the intensity, color, or pattern of the displayed markers. Additionally, the flashing of the display markers can be controlled. As an example, a description of changes to the visual appearance of the display markers is provided. Therefore, other aspects of the visual appearance of the display markers can be changed. Thus, the display markers can be modified in a desired manner in various situations to, for example, capture the attention of the operator 260.

[0193] Now back Figure 12 The flowchart continues to describe the operation of the operator interface controller 231. In block 760, the operator interface controller 231 detects input for setting a flag and controls the touch-sensitive user interface display 720 to display the flag on the field display section 728. The detected input can be operator input (as shown in 762) or input from another controller (as shown in 764). In block 766, the operator interface controller 231 detects a field sensor input indicating a measured characteristic of the field from one of the field sensors 208. In block 768, the vision control signal generator 684 generates control signals to control the user interface display 720 to display actuators for modifying the user interface display 720 and for modifying machine control. For example, block 770 indicates that one or more actuators for setting or modifying values ​​in columns 739, 746, and 748 can be displayed. Therefore, the user can set flags and modify the characteristics of these flags. Block 772 indicates that the action thresholds in column 752 are displayed. Box 776 indicates that the action in column 754 is displayed, and box 778 indicates that the field data of the measurement in column 750 is displayed. Box 780 indicates that various other information and actuators can also be displayed on the user interface display 720.

[0194] In box 782, the operator input command processing system 654 detects and processes operator input corresponding to the interaction performed by operator 260 with the user interface display 720. If the user interface mechanism displayed on the user interface display 720 is a touch-sensitive display, the interaction input performed by operator 260 with the touch-sensitive display can be a touch gesture 784. In some cases, the operator interaction input can be input using a clicking device 786 or other operator interaction inputs 788.

[0195] At block 790, the operator interface controller 231 receives a signal indicating an alarm condition. For example, block 792 indicates that a signal can be received by the controller input processing system 668 indicating that the detected value in column 750 satisfies a threshold condition present in column 752. As explained previously, the threshold condition can include a value below the threshold, at the threshold, or above the threshold. Block 794 shows that the action signal generator 660 can respond to receiving the alarm condition by generating a visual alarm using the visual control signal generator 684, generating an audio alarm using the audio control signal generator 686, generating a haptic alarm using the haptic control signal generator 688, or warning the operator 260 using any combination of these. Similarly, as shown in block 796, the controller output generator 670 can generate outputs to other controllers in the control system 214 so that these controllers perform the corresponding actions identified in column 754. Block 798 shows that the operator interface controller 231 can also detect and process alarm conditions in other ways.

[0196] Block 900 shows that the voice handling system 662 can detect and process inputs that invoke the voice processing system 658. Block 902 shows that performing voice processing can include using the dialog management system 680 to have a conversation with the operator 260. Block 904 shows that the voice processing can include providing signals to the controller output generator 670 so that control operations are automatically performed based on the voice input.

[0197] Table 1 below shows an example of a conversation between the operator interface controller 231 and the operator 260. In Table 1, the operator 260 invokes the voice processing system 658 using a trigger word or wake-up word that is detected by the trigger detector 672. In the example shown in Table 1, the wake-up word is “Johnny”.

[0198] Table 1

[0199] Operator: “Johnny, tell me about the current power utilization.”

[0200] Operator interface controller: “The power utilization across the machines is 90%.”

[0201] Operator: “Johnny, what should I do about the current power utilization?”

[0202] Operator interface controller: “If the machine speed is increased by 1 MPH, the power utilization can increase to 95%.”

[0203] Table 2 shows an example in which the speech synthesis component 676 provides output to the audio control signal generator 686 to provide auditory updates intermittently or periodically. The interval between updates can be time-based (such as every five minutes), or coverage or distance-based (such as every five acres), or anomaly-based (such as when a measured value is greater than a threshold).

[0204] Table 2

[0205] Operator interface controller: "Power utilization has averaged 80% over the last 10 minutes."

[0206] Operator interface controller: "The next 1 acre is projected to have a power utilization of 82%."

[0207] Operator interface controller: "Warning: power utilization is below 80%. Machine speed is increasing."

[0208] The example shown in Table 3 shows that some actuators or user input mechanisms on the touch-sensitive display 720 can be supplemented with voice dialog. The example in Table 3 shows that the action signal generator 660 can generate action signals to automatically mark weed patches in a field being harvested.

[0209] Table 3

[0210] Human: "Johnny, mark weed patch."

[0211] Operator interface controller: "Weed patch marked."

[0212] The example shown in Table 4 shows that the action signal generator 660 can have a dialog with the operator 260 to start and stop marking of weed patches.

[0213] Table 4

[0214] Human: "Johnny, start marking weed patch."

[0215] Operator interface controller: "Marking weed patch."

[0216] Human: "Johnny, stop marking weed patch."

[0217] Operator interface controller: "Weed patch marking stopped."

[0218] The example shown in Table 5 shows that the action signal generator 160 can generate signals to mark weed patches in a different manner than shown in Tables 3 and 4.

[0219] Table 5

[0220] Human: "Johnny, mark the next 100 feet as a weed patch."

[0221] Operator interface controller: "Next 100 feet marked as weed patch."

[0222] Returning again Figure 12 to FIG. 9, block 906 shows that the operator interface controller 231 can also detect and handle cases for otherwise outputting messages or other information. For example, the other controller interaction system 656 can detect input from other controllers indicating that an alert or output message should be presented to the operator 260. Block 908 shows that the output can be an audio message. Block 910 shows that the output can be a visual message, and block 912 shows that the output can be a haptic message. Until the operator interface controller 231 determines that the current harvesting operation is complete (as shown in block 914), the process returns to block 698, in which the geographic position of the harvester 100 is updated, and the process continues as described above to update the user interface display 720.

[0223] Once the operation is complete, any desired values that were 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 different parts of the prediction model generator 210, the prediction map generator 212, the control zone generator 213, the control algorithm, or other items. The saved desired values are indicated by block 916. These values can be saved locally on the agricultural harvester 100, or the values can be saved at a remote server location or sent to another remote system.

[0224] As can be seen, the information map is obtained by the agricultural harvester and shows power characteristic values at different geographic locations of the field being harvested. On-board sensors of the harvester sense characteristics having values indicative of agricultural characteristics as the agricultural harvester moves through the field. The prediction map generator generates a prediction map that predicts control values for different locations in the field based on the values of the power characteristics in the information map and the agricultural characteristics sensed by the on-board sensors. The control system controls the controllable subsystems based on the control values in the prediction map.

[0225] The control values are values that actions can be based on. As described herein, the control values can include any value (or a characteristic indicated by or derived from the value) that can be used to control the agricultural harvester 100. The control values can be any value indicative of an agricultural characteristic. The control values can be predicted values, measured values, or detected values. The control values can include any value provided by a map, such as any one of the maps described herein, for example, the control values can be values provided by the information map, values provided by the prior information map, or values provided by a prediction map, such as a functional prediction map. The control values can also include any one of the characteristics indicated by or derived from values detected by any one of the sensors described herein. In other examples, the control values can be provided by an operator of the agricultural machine, such as a command input by an operator of the agricultural machine.

[0226] The current discussion has mentioned processors and servers. In some examples, the processors and servers include computer processors with associated memory and timing circuitry, not separately shown. The processors and servers are part of the system or device of which they are a functional part, and are activated by, and facilitate the functionality of, the other components or items in these systems.

[0227] Also, a number of user interface displays have been discussed. The displays can take a variety of different forms, and can have a variety of different user-actuatable operator interface structures disposed thereon. For example, the user-actuatable operator interface structures can be text boxes, check boxes, icons, links, drop-down menus, search boxes, etc. The user-actuatable operator interface structures can also be actuated in a variety of different ways. For example, the user-actuatable operator interface structures can be actuated using an operator interface mechanism such as a pointing device, (such as a trackball or mouse, a hardware button, a switch, a joystick or keyboard, a thumb switch or thumb pad, etc.), a virtual keyboard or other virtual actuator. Further, where the screen on which the user-actuatable operator interface structures are displayed is a touch-sensitive screen, the user-actuatable operator interface structures can be actuated using touch gestures. Also, the user-actuatable operator interface structures can be actuated using voice commands using voice recognition functionality. The voice recognition can be implemented using a voice detection device such as a microphone and software for recognizing the detected voice and performing commands based on the received voice.

[0228] A number of data stores have also been discussed. It should be noted that the data stores can each be divided into a number of data stores. In some examples, one or more of the data stores can be local to the system accessing the data store, all of the data stores can be located remotely from the system utilizing the data stores, or one or more data stores can be local while others are remote. All of these configurations are contemplated by the present disclosure.

[0229] Also, the figures show multiple blocks with functionality attributed to each block. It should be noted that the functionality attributed to multiple different blocks can be performed by fewer components using fewer blocks. Also, more blocks can be used showing that the functionality can be distributed among more components. In different examples, some functionality can be added and some functionality can be removed.

[0230] It should be noted that the above discussion has described a variety of different systems, components, logic, and interactions. It should be understood that any or all of such systems, components, logic, and interactions can be implemented by hardware items, such as processors, memories, or other processing components, including but not limited to artificial intelligence components (such as neural networks, some of which are described below) that perform functions associated with those systems, components, logic, or interactions. Moreover, any or all of the systems, components, logic, and interactions can be implemented by software that is loaded into memory and subsequently executed by a processor or server or other computing component, as described below. Any or all of the systems, components, logic, and interactions can also be implemented by different 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.

[0231] Figure 14 is a block diagram of an agricultural harvester 600, which can be similar to the agricultural harvester 100 shown in Figure 2 The agricultural harvester 600 communicates with elements in the remote server architecture 500. In some examples, the remote server architecture 500 can provide computing, software, data access, and storage services that do not require end users to be aware of the physical location or configuration of the system that delivers the services. In various examples, the remote server can deliver services over a wide area network, such as the Internet, using appropriate protocols. For example, the remote server can deliver an application over a wide area network, and the application can be accessed through a web browser or any other computing component. Figure 2 The software or components shown in

[0232] In the example shown in Figure 14 some items are similar to the items shown in Figure 2 and these items are similarly numbered. Figure 14 It is specifically shown that the prediction model generator 210 or the prediction map generator 212 or both can be located at a server location 502 that is remote from the agricultural harvester 600. Thus, in the example shown in Figure 14 the agricultural harvester 600 accesses the system through the remote server location 502.

[0233] Figure 14 Another example of a remote server architecture is also depicted. Figure 14 It is shown that Figure 2 Some of the elements of the system 200 can be arranged at a remote server location 502, while other elements can be located elsewhere. As an example, the data store 202 can be placed at a location separate from the location 502 and accessed via a remote server at the location 502. Regardless of where these elements are located, these elements can be accessed by the agricultural harvester 600 directly over a network, such as a wide area network or a local area network, these elements can be hosted by a service at a remote site, or these elements can be provided as a service, or accessed by a connectivity service that resides at a remote location. Further, data can be stored at any location, and stored data can be accessed or forwarded to an operator, user, or system. For example, a physical carrier wave can be used instead of, or in addition to, an electromagnetic wave carrier. In some examples, another machine, such as a fuel truck or other mobile machine or vehicle, can have an automatic, semi-automatic, or manual information collection system in the event of poor or non-existent wireless telecommunication service coverage. When the combine harvester 600 approaches the machine containing the information collection system, such as a fuel truck, before refueling, the information collection system collects information from the combine harvester 600 using any type of temporary, ad hoc wireless connection. The collected information can then be forwarded to another network when the machine containing the received information reaches a location where wireless telecommunication service coverage or other wireless coverage is available. For example, the fuel truck can enter an area with wireless communication coverage when it travels to a location to refuel other machines or at a main fuel storage location. All of these architectures are contemplated herein. Further, information can be stored on the agricultural harvester 600 until the agricultural harvester 600 enters an area with wireless communication coverage. The agricultural harvester 600 itself can transmit the information to another network.

[0234] It will also be noted that Figure 2 Elements of the system 200, or portions thereof, can be provided on a variety of different devices. One or more of these devices can include an on-board computer, an electronic control unit, a display unit, a server, a desktop computer, a laptop computer, a tablet computer, or other mobile device, such as a palmtop computer, a cellular telephone, a smart phone, a multimedia player, a personal digital assistant, and the like.

[0235] In some examples, the remote server architecture 500 can include network security measures. Without limitation, these measures include encryption of data on storage devices, encryption of data sent between network nodes, authentication of personnel or processes accessing data, and the use of a ledger to record metadata, data, data transfers, data access, and data transformations. In some examples, the ledger can be distributed and immutable (e.g., implemented as a blockchain).

[0236] Figure 15 is a simplified block diagram of one illustrative example of a handheld or mobile computing device that can be used as a handheld device 16 of a user or customer in which the present system (or a portion thereof) can be deployed. For example, the mobile device can be deployed in an operator's cab of an agricultural harvester 100 for use in generating, processing, or displaying the graphs discussed above. Figures 16-17 is an example of a handheld or mobile device.

[0237] Figure 15 An overall block diagram of components of a client device 16 is provided that can run some of the components shown in Figure 2 In the device 16, a communications link 13 is provided that allows the handheld device to communicate with other computing devices, and in some examples a channel for automatically receiving information (e.g., by scanning) is provided. Examples of the communications link 13 include allowing communication over one or more communication protocols, such as wireless services for providing cellular access to a network, and protocols for providing local wireless connectivity to a network.

[0238] In other examples, the application can be received on a removable Secure Digital (SD) card that is connected to the interface 15. The interface 15 and the communications link 13 are in communication with the processor 17 (which can also implement the processor or server from other figures) along a bus 19 that is also connected to memory 21 and input / output (I / O) components 23, as well as a clock 25 and a location system 27.

[0239] In one example, the I / O components 23 are provided to facilitate input and output operations. The I / O components 23 of various examples of the device 16 can include input components, such as buttons, touch sensors, optical sensors, microphones, touch screens, proximity sensors, accelerometers, orientation sensors, and output components, such as display devices, speakers, and / or printer ports. Other I / O components 23 can also be used.

[0240] The clock 25 illustratively includes a real-time clock component that outputs time and date. Illustratively, it can also provide timing functions for the processor 17.

[0241] The location system 27 illustratively includes components for outputting a current geographic location of the device 16. This can include, for example, a global positioning system (GPS) receiver, a LORAN system, a dead reckoning system, a cellular triangulation system, or other positioning system. The location system 27 can also include, for example, mapping software or navigation software that generates desired maps, navigation routes, and other geographic functions.

[0242] The memory 21 stores an operating system 29, network settings 31, applications 33, application configuration settings 35, data stores 37, communication drivers 39, and communication configuration settings 41. The memory 21 can include all types of tangible volatile and non-volatile computer-readable memory devices. The memory 21 can also include computer storage media (described below). The memory 21 stores computer readable instructions that, when executed by the processor 17, cause the processor to perform computer-implemented steps or functions according to the instructions. The processor 17 can also be activated by other components to facilitate their functions.

[0243] Figure 16 One example is shown in which the device 16 is a tablet computer 600. In Figure 16 the computer 601 is shown with a user interface display screen 602. The screen 602 can be a touch screen that receives input from a pen or stylus or a pen-enabled interface. The tablet computer 600 can also use an on-screen virtual keyboard. Of course, the computer 601 can also be attached to a keyboard or other user input device, such as by a suitable attachment structure such as a wireless link or a USB port. The computer 601 can also illustratively receive voice input.

[0244] Figure 17 Similarly to Figure 16 , except that the device is a smart phone 71. The smart phone 71 has a touch sensitive display 73 that displays icons or widgets 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. Generally speaking, the smart phone 71 builds on the concept of the mobile phone and provides more advanced computing and connectivity capabilities. Other types of devices 16 can be used, however, such as a laptop computer, a personal digital assistant, a media player, a game player, a television, a remote control, a vehicle, a camera, a handheld gaming console, a handheld media player, a handheld computer, a handheld device having a combination of media player and gaming console, a handheld device having a combination of media player and computer, a handheld device having a combination of gaming console and computer, a handheld device having a combination of media player and computer and gaming console, a handheld device having a combination of media player and computer and television, a handheld device having a combination of media player and computer and television and gaming console, a handheld device having a combination of media player and computer and television and remote control, a handheld device having a combination of media player and computer and television and remote control and gaming console, a handheld device having a combination of media player and computer and television and remote control and gaming console and vehicle, a handheld device having a combination of media player and computer and television and remote control and gaming console and vehicle and camera, etc.

[0245] Note that other forms of the device 16 are possible.

[0246] Figure 18 is one example of a computing environment in which Figure 2 the elements of the system 10 can be deployed. Reference is made to Figure 18An example 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 computer 810 can include, but are not limited to, a processing unit 820 (which can include a processor or server from the previous figures), a system memory 830, and a system bus 821 that couples various system components including the system memory to the processing unit 820. 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 using any of a variety of bus architectures. Regarding Figure 2 The described memory and programs can be deployed in Figure 18 corresponding portions of

[0247] Computer 810 typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by computer 810 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media can comprise computer storage media and communication media. Computer storage media is different from, and does not include, a modulated data signal or carrier wave. It includes hardware storage media including both volatile and nonvolatile, 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. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer 810. Communication media can embody computer readable instructions, data structures, program modules or other data in a modulated data signal or carrier wave. The term "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.

[0248] The system memory 830 includes computer storage media in the form of volatile and / or nonvolatile memory such as read only memory (ROM) 831 and random access memory (RAM) 832. A basic input / output system 833 (BIOS), containing the basic routines that help to transfer information between elements within the computer 810, such as during start-up, is typically stored in ROM 831. RAM 832 typically contains data and / or program modules that are immediately accessible to and / or presently being operated on by processing unit 820. By way of example, and not limitation, as Figure 18 Operating system 834, application programs 835, other program modules 836, and program data 837 are shown.

[0249] The computer 810 can also include other removable / non-removable volatile / nonvolatile computer storage media. By way of example, and not limitation, Figure 18 Hard disk drive 841 is shown as reading from or writing to non-removable, nonvolatile magnetic media, e.g., a not shown hard disk drive, or to optical disk drives 855 and 856, which read from or write to optical media. Hard disk drive 841 is typically connected to system bus 821 by a not shown non-removable memory interface, such as interface 840, and optical drives 855 and 856 are typically connected to system bus 821 by a removable memory interface, such as interface 850.

[0250] Alternatively, or in addition, the functionality described herein can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (ASICs), Application-specific Standard Products (e.g., ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0251] The drives and their associated computer storage media discussed above and illustrated in Figure 18 provide storage of computer readable instructions, data structures, program modules and other data for the computer 810. In this regard, the Figure 18In this diagram, hard disk drive 841 is shown storing operating system 844, application programs 845, other program modules 846, and program data 847. Note that these components may be the same as or different from operating system 834, application programs 835, other program modules 836, and program data 837.

[0252] Users can input commands and information to computer 810 through input devices such as keyboard 862, microphone 863, and pointing devices 861 (such as mouse, trackball, or touchpad). Other input devices (not shown) may include joysticks, game controllers, satellite dishes, scanners, etc. These and other input devices are typically connected to processing unit 820 via user input interface 860 coupled to the system bus, but may also be connected via other interfaces and bus structures. Visual display 891 or other types of display devices are also connected to system bus 821 via an interface such as video interface 890. In addition to the monitor, the computer may also include other peripheral output devices, such as speakers 897 and printers 896, which can be connected via peripheral output interface 895.

[0253] Computer 810 operates in a networked environment using logical connections (such as Controller Area Network (CAN), Local Area Network (LAN), or Wide Area Network (WAN)) to one or more remote computers (such as remote computer 880).

[0254] When used in a LAN networking environment, computer 810 connects to LAN 871 via a network interface or adapter 870. When used in a WAN networking environment, computer 810 typically includes a modem 872 or other devices for establishing communication over a WAN 873 (such as the Internet). In a networking environment, program modules can be stored in remote memory storage devices. For example, Figure 18 This demonstrates that remote application 885 can reside on remote computer 880.

[0255] It should also be noted that the different examples described in this article can be combined in different ways. That is, parts of one or more examples can be combined with parts of one or more other examples. All of this is considered in this article.

[0256] Example 1 is an agricultural operating machine, comprising:

[0257] A communication system that receives an information map including values ​​of power characteristics corresponding to different geographical locations in the field;

[0258] A geolocation sensor that detects the geographical location of agricultural machinery;

[0259] a field sensor that detects a value of an agricultural property corresponding to a geographic location;

[0260] a prediction map generator that generates a functional predicted agricultural map of the field that maps predicted control values to different geographic locations in the field based on the values of the power property in the information map and based on the value of the agricultural property;

[0261] a controllable subsystem; and

[0262] a control system that generates control signals to control the controllable subsystem based on the geographic location of the agricultural work machine and based on the control values in the functional predicted agricultural map.

[0263] Example 2 is the agricultural work machine of any or all preceding examples, wherein the prediction map generator comprises:

[0264] a predicted temperature map generator that generates a functional predicted temperature map that maps predicted temperature values to different geographic locations in the field.

[0265] Example 3 is the agricultural work machine of any or all preceding examples, wherein the control system comprises:

[0266] a cooling controller that generates a cooling subsystem control signal based on the detected geographic location and the functional predicted temperature map and controls a cooling subsystem as the controllable subsystem based on the cooling subsystem control signal.

[0267] Example 4 is the agricultural work machine of any or all preceding examples, wherein the control system controls the cooling subsystem to adjust a cooling fan speed.

[0268] Example 5 is the agricultural work machine of any or all preceding examples, wherein the control system controls the cooling subsystem to adjust a cooling fan pitch.

[0269] Example 6 is the agricultural work machine of any or all preceding examples, wherein the prediction map generator comprises:

[0270] a predicted operator command map generator that generates a functional predicted operator command map that maps predicted operator commands to different geographic locations in the field.

[0271] Example 7 is the agricultural work machine of any or all preceding examples, wherein the control system comprises:

[0272] a setting controller that generates an operator command control signal indicative of an operator command based on the detected geographic location and the functional predicted operator command map and controls the controllable subsystem to execute the operator command based on the operator command control signal.

[0273] Example 8 is the agricultural work machine of any or all preceding examples, wherein the information map comprises a historical power map mapping historical power characteristic values to different geographic locations in the field.

[0274] Example 9 is the agricultural work machine of any or all preceding examples, wherein the control system further comprises:

[0275] an operator interface controller generating a user interface graphical representation of the functional predictive field map, the user interface graphical representation comprising a field portion having one or more indicia indicating the predicted control values at one or more geographic locations on the field portion.

[0276] Example 10 is the agricultural work machine of any or all preceding examples, wherein the operator interface controller generates the user interface graphical representation to include an interactive display portion displaying a value display portion indicating a selected value, an interactive threshold display portion indicating an action threshold, and an interactive action display portion indicating a control action to be taken when one of the predicted control values satisfies the action threshold related to the selected value, the control system generating a control signal to control the controllable subsystem based on the control action.

[0277] Example 11 is a computer-implemented method of controlling an agricultural work machine, comprising

[0278] obtaining an information map comprising values of power characteristics corresponding to different geographic locations in a field;

[0279] detecting a geographic location of the agricultural work machine;

[0280] detecting, with an on-site sensor, a value of an agricultural characteristic corresponding to the geographic location;

[0281] generating, based on the values of the power characteristics in the information map and based on the value of the agricultural characteristic, a functional predictive field map of the field mapping predicted control values to different geographic locations in the field; and

[0282] controlling a controllable subsystem based on the geographic location of the agricultural work machine and based on the control values in the functional predictive field map.

[0283] Example 12 is the computer-implemented method of any or all preceding examples, wherein generating the functional predictive map comprises:

[0284] generating a functional predictive temperature map mapping predicted temperature values to different geographic locations in the field.

[0285] Example 13 is the computer-implemented method of any or all preceding examples, wherein controlling the controllable subsystem comprises:

[0286] generating cooling subsystem control signals based on the detected geographic locations and the function predicted temperature map; and

[0287] controlling the cooling subsystem as a controllable subsystem based on the cooling subsystem control signals.

[0288] Example 14 is the computer-implemented method of any or all preceding examples, wherein controlling the cooling subsystem as a controllable subsystem based on the cooling subsystem control signals comprises:

[0289] controlling a fan speed of the cooling subsystem.

[0290] Example 15 is the computer-implemented method of any or all preceding examples, wherein controlling the cooling subsystem as a controllable subsystem based on the cooling subsystem control signals comprises:

[0291] controlling a fan pitch of the cooling subsystem.

[0292] Example 16 is the computer-implemented method of any or all preceding examples, wherein generating the function prediction map comprises:

[0293] generating a function prediction operator command map that maps predicted operator commands to different geographic locations in the field.

[0294] Example 17 is the computer-implemented method of any or all preceding examples, wherein controlling the controllable subsystem comprises:

[0295] generating operator command control signals indicative of operator commands based on the detected geographic locations and the function prediction operator command map; and

[0296] controlling the controllable subsystem to perform the operator commands based on the operator command control signals.

[0297] Example 18 is the computer-implemented method of any or all preceding examples, and further comprising:

[0298] generating a predictive agricultural model that models a relationship between the power property and the agricultural property at the geographic location based on the value of the power property at the geographic location in the information map and the value of the agricultural property sensed by the field sensor at the geographic location, wherein generating the function prediction agricultural map comprises generating the function prediction agricultural map based on the values of the power property in the information map and based on the predictive agricultural model.

[0299] Example 19 is an agricultural work machine, comprising:

[0300] a communication system that receives an information map comprising values of a power property corresponding to different geographic locations in a field;

[0301] a geographic position sensor that detects a geographic position of the agricultural work machine;

[0302] a field sensor that detects a value of an agricultural property corresponding to the geographic position;

[0303] a predictive model generator that generates a predictive agricultural model modeling a relationship between the power property and the agricultural property based on the value of the power property at the geographic position in the information map and the value of the agricultural property at the geographic position sensed by the field sensor;

[0304] a predictive map generator that generates a functional predictive agricultural map of the field mapping predictive control values to different geographic positions in the field based on the values of the power property in the information map and based on the predictive agricultural model;

[0305] a controllable subsystem; and

[0306] a control system that generates a control signal to control the controllable subsystem based on the geographic position of the agricultural work machine and based on the control values in the functional predictive agricultural map.

[0307] Example 20 is the agricultural work machine of any or all preceding examples, wherein the control system comprises:

[0308] a cooling controller that generates a cooling control signal based on the detected geographic position and the functional predictive agricultural map and controls a cooling subsystem as the controllable subsystem based on the cooling control signal.

[0309] Although the subject matter has been described in language specific to structural features or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. An agricultural work machine (100), comprising: a communication system (206) that receives an information map (258) comprising values of power characteristics corresponding to different geographic locations in a field; a geographic location sensor (204) that detects a geographic location of the agricultural work machine; a field sensor (208) that detects a temperature value corresponding to the geographic location; a predicted map generator (212) that generates a functional predicted temperature map of the field that maps predicted temperature values to different geographic locations in the field based on the values of power characteristics in the information map (258) and based on the temperature value corresponding to the geographic location detected by the field sensor; a controllable subsystem (216); and a control system (214) that generates control signals to control the controllable subsystem (216) based on the geographic location of the agricultural work machine (100) and based on the predicted temperature values in the functional predicted temperature map. The control system comprises:

2. The agricultural work machine of claim 1, wherein, a cooling controller that generates cooling subsystem control signals based on the detected geographic location and the functional predicted temperature map and controls a cooling subsystem as the controllable subsystem based on the cooling subsystem control signals. The control system controls the cooling subsystem to adjust cooling fan speed.

3. The agricultural work machine of claim 2, wherein, The control system controls the cooling subsystem to adjust cooling fan spacing.

4. The agricultural work machine of claim 2, wherein, The information map comprises a historical power map that maps historical power characteristic values to different geographic locations in the field.

5. The agricultural work machine of claim 1, wherein, 6. A computer-implemented method of controlling an agricultural work machine (100), comprising: obtaining an information map (258) comprising values of power characteristics corresponding to different geographic locations in a field; detecting a geographic location of the agricultural work machine (100); detecting a temperature value corresponding to the geographic location with a field sensor (208); generating a functional predicted temperature map of the field that maps predicted temperature values to different geographic locations in the field based on the values of power characteristics in the information map (258) and based on the temperature value corresponding to the geographic location detected by the field sensor; and controlling a controllable subsystem (216) based on the geographic location of the agricultural work machine (100) and based on the temperature values in the functional predicted temperature map.

7. An agricultural work machine (100), comprising: a communication system (206) that receives an information map (258) comprising values of power characteristics corresponding to different geographic locations in a field; a geographic location sensor (204) that detects a geographic location of the agricultural work machine; a field sensor (208) that detects a temperature value corresponding to the geographic location; ​ ​ a prediction model generator (210) that generates a predictive agricultural model based on values of power characteristics at the geographic locations in the information map (258) and on temperature values corresponding to the geographic locations detected by the field sensors (208), the predictive agricultural model modeling a relationship between the values of power characteristics and the temperature values; a prediction map generator (212) that generates a functional predictive temperature map of the field that maps predictive temperature values to different geographic locations in the field based on the values of power characteristics in the information map (258) and based on the predictive agricultural model; a controllable subsystem (216); and a control system (214) that generates control signals to control the controllable subsystem (216) based on a geographic location of the agricultural work machine (100) and based on temperature values in the functional predictive temperature map.

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