Graph generation and control system

Abstract

One or more information maps are obtained by an agricultural work machine. The one or more information maps map values of one or more agricultural properties at different geographic locations of a field. Onboard sensors on the agricultural work machine sense the agricultural properties as the agricultural work machine moves through the field. A prediction map generator generates a prediction map of predicted agricultural properties at different locations in the field based on relationships between values in the one or more information maps and the agricultural properties sensed by the onboard sensors. 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] The above discussion is provided only as general background information and does not intend to aid in the determination of the scope of the subject matter claimed. SUMMARY

[0004] One or more information maps are obtained by the agricultural work machine. The one or more information maps map values of one or more agricultural properties at different geographic locations of a field. Onboard sensors of the agricultural work machine sense the agricultural properties as the agricultural work machine moves through the field. A prediction map generator generates a prediction map of predicted agricultural properties at different locations in the field based on relationships between values in the one or more information maps and the agricultural properties sensed by the onboard sensors. The prediction map can be output and used for automated machine control.

[0005] 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

[0006] Figure 1 is a partial schematic view of an example of a combine harvester.

[0007] Figure 2 is a block diagram showing some parts of an agricultural harvester in more detail according to some examples of the present disclosure.

[0008] Figures 3A-3B is a flowchart showing an example of the operation of an agricultural harvester in generating a map.

[0009] Figure 4A is a block diagram showing one example of a prediction model generator and a prediction map generator.

[0010] Figure 4B is a block diagram showing one example of a prediction model generator in more detail.

[0011] Figure 5is a flowchart showing an example of operations of an agricultural harvester in receiving a map, detecting characteristics using field sensors, and generating a functional prediction map used in presenting or in controlling the agricultural harvester during a harvesting operation.

[0012] Figure 6 is a block diagram of one example of a control area generator.

[0013] Figure 7 is a flowchart showing one example of operations of a control area generator.

[0014] Figure 8 is a flowchart showing one example of operations using a control area.

[0015] Figure 9 is a block diagram of one example of an operator interface controller.

[0016] Figure 10 is a flowchart showing one example of operations of an operator interface controller.

[0017] Figure 11 is a schematic diagram of one example of a user interface display.

[0018] Figure 12 is a block diagram showing one example of an agricultural harvester in communication with a remote server environment.

[0019] Figures 13-15 An example of a mobile device that can be used in an agricultural harvester is shown.

[0020] Figure 16 is a block diagram showing one example of a computing environment that can be used in an agricultural harvester. DETAILED DESCRIPTION

[0021] 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 disclosure are fully contemplated as would occur to one skilled in the art to which the disclosure relates. In particular, it is fully contemplated that the features, components, and / or steps described with respect to one example can be combined with the features, components, and / or steps described with respect to other examples of the present disclosure.

[0022] The present specification relates to generating a prediction map using field data acquired contemporaneously with an agricultural operation in conjunction with data from a map.

[0023] In some examples, the prediction map can be used to control an agricultural work machine, such as an agricultural harvester. As discussed above, performance of an agricultural harvester can be reduced or otherwise affected under different conditions. For example, based on the terrain of a field, performance of a harvester (or other agricultural machine) can be detrimentally affected. When navigating on a side slope, the terrain can cause the machine to pitch and roll by an amount. Without limitation, the machine pitch or roll can affect grain loss, internal material distribution, grain quality, and tailings characteristics. For example, grain loss can be affected by terrain characteristics that cause the agricultural harvester 100 to pitch or roll. Increased pitch can cause grain to be discharged from the rear more quickly, decreased pitch can leave grain in the machine, and roll elements can overload the sides of the cleaning system and cause more grain loss from those sides. Similarly, grain quality can be affected by pitch and roll, and similar to grain loss, material other than grain that is left in or exits the machine based on pitch or roll can affect the quality output. In another example, terrain characteristics that affect pitch will have an effect on the amount of material that enters the tailings system, affecting the tailings sensor output. Considering this level of pitch and time can be related to how much the tailings volume increases, and can be helpful in estimating what needs to be controlled to predict this level and make adjustments. In other examples, characteristics such as genotype, vegetation index, yield, biomass, and weed characteristics (e.g., weed type or weed intensity) can affect other characteristics such as tailings, crop loss, grain quality, and internal material distribution.

[0024] The terrain map illustratively maps the elevation of the ground at different geographic locations in a field of interest. Since ground slope indicates a change in elevation, having two or more elevation values allows for the calculation of slope across an area having known elevation values. By having more areas with known elevation values, a greater slope interval size can be achieved. When an agricultural harvester traverses a terrain in a known direction, the pitch and roll of the agricultural harvester can be determined from the slope of the ground (i.e., areas of change in elevation). When referred to below, terrain characteristics can include, but are not limited to, elevation, slope (e.g., including machine direction relative to slope), and ground profile (e.g., roughness).

[0025] In some examples, a predicted biomass map can be used to control an agricultural work machine (e.g., an agricultural harvester). As used herein, biomass refers to the amount of above-ground vegetation material in a given area or location. Typically, this amount is measured in terms of weight, e.g., weight per given area, such as tons per acre. Various characteristics can be indicative of biomass (referred to herein as biomass characteristics), and can be used to predict biomass over a field of interest. For example, biomass characteristics can include various crop characteristics, such as crop height (height of the crop above the surface of the field), crop density (amount of crop material in a given space, which can be derived from crop mass and crop volume), crop mass (e.g., weight of the crop or weight of a crop component), or crop volume (how much of a given area or location is occupied by the crop, i.e., space occupied or contained by the crop). In another example, biomass characteristics can include various machine characteristics of an agricultural harvester, such as machine settings or operating characteristics. For example, force (e.g., fluid pressure or torque) used to drive a threshing rotor of an agricultural harvester can be indicative of biomass.

[0026] As an agricultural harvester engages areas of a field where biomass varies, performance of the agricultural harvester can be affected. For example, if machine settings of the agricultural harvester are set according to an expected or desired throughput, variations in biomass can cause the throughput to vary, and thus the machine settings can not be able to most effectively process vegetation including crops. As noted above, an operator can attempt to predict biomass ahead of the machine. Further, some systems (e.g., feedback control systems) adjust forward ground speed of the agricultural harvester in a reactive manner to attempt to maintain a desired throughput. This can be done by attempting to identify biomass based on sensor input (e.g., from sensors that sense variables indicative of biomass). However, such an arrangement is prone to error and can be too slow to react to upcoming biomass variations to effectively change operation of the machine to control throughput, e.g., by changing forward speed of the harvester. For example, such systems are typically reactive in that adjustments to machine settings are made only after the machine encounters vegetation in an attempt to further reduce errors in, e.g., feedback control systems.

[0027] Some current systems provide vegetation index maps. Vegetation index maps illustratively map values of a vegetation index (which can be indicative of vegetation growth) over different geographic locations in a field of interest. One example of a vegetation index includes the normalized difference vegetation index (NDVI). Many other vegetation indices exist 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 vegetation. Without limitation, these bands can be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum.

[0028] The vegetation index map can be used to identify the presence and location of vegetation. In some examples, the vegetation index map enables identification and georeferencing of crops in the presence of bare ground, crop residue, or other plant life, such as weeds. In other examples, the vegetation index map is able to detect characteristics, such as crop growth and crop health or vigor, at different geographic locations in a field of interest.

[0029] The seed genotype map maps the genotype (e.g., hybrid, cultivar, species, etc.) of seeds planted at different locations in a field. The seed genotype map can be generated by a planter or by a machine performing a subsequent operation (e.g., a sprayer with optical detectors that detect plant genotype).

[0030] The predicted yield map includes georeferenced predicted yield values.

[0031] The predicted weed map includes one or more of predicted weed characteristics that are georeferenced, such as weed intensity values or weed type values. The weed intensity values can include, but are not limited to, at least one of weed population, weed growth stage, weed size, weed biomass, weed moisture, or weed health. The weed type values can include, but are not limited to, an indication of weed type, such as an identification of a weed species.

[0032] Accordingly, the present discussion is directed to a system that receives at least one or more of a topography map, a seed genotype map, a vegetation index map, a yield map, a biomass map, and a weed map, and also uses in-field sensors to detect values indicative of one or more of internal material distribution, grain loss or crop loss, characteristics of tailings, and grain quality during a harvesting operation. The system generates models that model one or more relationships between characteristics derived from the received maps and output values from the in-field sensors. The one or more models are used to generate functional prediction maps that predict characteristics, such as characteristics sensed by the one or more in-field sensors or related characteristics at different geographic locations in a field, based on the one or more prior information maps. The functional prediction maps generated during a harvesting operation can be used to automatically control a harvester during the harvesting operation. The functional prediction maps can also be provided to an operator or another user.

[0033] Figure 1is 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 description applies to other types of harvesters as well, such as cotton harvesters, sugarcane harvesters, self-propelled forage harvester, swathers, or other agricultural work machines. Thus, the present disclosure is intended to encompass the various types of harvesters described and is therefore not limited to combine harvesters. Moreover, the present disclosure is directed to other types of work machines, such as agricultural seeding and spraying machines, construction equipment, forestry equipment, and lawn care equipment, in which generation of a predictive map can be applied. Thus, the present disclosure is intended to encompass these various types of harvesters and other work machines and is therefore not limited to combine harvesters.

[0034] As Figure 1 shown, the agricultural harvester 100 schematically includes an operator cab 101, which 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 at 104. The agricultural harvester 100 also includes a feederhouse 106, a feeder accelerator 108, and a threshing machine generally indicated at 110. The feederhouse 106 and the feeder 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 in a direction generally indicated by arrow 109 about the axis 105. Thus, a vertical position of the header 102 above the ground 111 on which the header 102 travels (header height) is controllable by actuating the actuators 107. Although not shown in Figure 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. 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. The roll angle refers to an orientation of the header 102 about a fore-aft longitudinal axis of the agricultural harvester 100.

[0035] The threshing machine 110 illustratively includes a threshing rotor 112 and a set of concaves 114. Additionally, the agricultural harvester 100 also includes a separator 116. The agricultural harvester 100 also includes a cleaning subsystem or cleaning device (collectively, cleaning subsystem 118) that includes a cleaning fan 120, a chaffer 122, and a sieve 124. The material handling subsystem 125 also includes a discharge beater 126, a tailings elevator 128, a clean grain elevator 130, and a discharge auger 134 and 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 and right cleaning subsystems, a separator, etc., which are not shown in Figure 1 FIG. 1.

[0036] 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 and roll angles of the header 102 and implement the input settings by controlling associated actuators (not shown) that operate to change the tilt and roll angles 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 others. 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 and roll angle errors) with a responsiveness determined based on a level of sensitivity. If the level of sensitivity is set at a higher level of sensitivity, the control system responds to smaller header position errors and attempts to reduce detected errors more quickly than if the level of sensitivity is at a lower level of sensitivity.

[0037] 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 residue is moved by the unloading beater 126 toward the residue subsystem 138. The portion of the residue that is conveyed to the residue subsystem 138 is chopped by the residue chopper 140 and spread on the field by the spreader 142. In other configurations, the residue is released from the agricultural harvester 100 into a pile. In other examples, the residue subsystem 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.

[0038] The grain falls to the cleaning subsystem 118. The chaffer sieve 122 separates some of the larger pieces of material from the grain, and the screen 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. Residue is removed from the cleaning subsystem 118 by the airflow generated by the cleaning fan 120. The cleaning fan 120 directs air up through the screen and the chaffer sieve along an airflow path. The airflow carries residue in the agricultural harvester 100 rearward toward the residue handling subsystem 138.

[0039] 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 through a tailings elevator or another transport device to a separate re-threshing mechanism, where the tailings are also re-threshed.

[0040] Although not shown in Figure 1 In some examples, the agricultural harvester 100 can include one or more adjustable material engagement elements disposed in a material flow path within the agricultural harvester 100. These adjustable material engagement elements can include, but are not limited to, vanes such as paddles, or other adjustable members, which can be adjustably moved (e.g., tilted, pivoted, etc.) to direct material within the flow path. The adjustable material engagement elements can direct at least a portion of the material flow right or left relative to the flow direction, for example, toward the left or right cleaning subsystems, toward the left or right separators, or toward various other components and subsystems of the agricultural harvester, which can include left and right, as described above. In some examples, the direction can be lateral or fore-aft relative to the material flow direction from an area of greater material depth to an area of lesser material depth. These adjustable material engagement elements can be controlled via actuators (e.g., hydraulic, electric, pneumatic, etc.) to control the material distribution within the agricultural harvester 100.

[0041] Figure 1Also shown in one example, 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 which may be in the form of a stereo camera or a monocular camera, and one or more loss sensors 152 disposed in a cleaning subsystem 118. The ground speed sensor 146 senses the travel speed of the agricultural harvester 100 on the ground. The ground speed sensor 146 can sense the travel speed 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 may be used to sense the travel speed, such as a global positioning system (GPS), dead reckoning system, long-range navigation (LORAN) system, Doppler velocity sensor, or various other systems or sensors that provide an indication of travel speed. The ground speed sensor 146 may also include a direction sensor (e.g., a compass, magnetometer, gravity sensor, gyroscope, GPS navigation) to determine the two-dimensional or three-dimensional direction of travel in conjunction with the speed. Thus, when the harvester 100 is located on a slope, the orientation of the harvester 100 relative to the slope is known. For example, the orientation of the harvester 100 may include ascending, descending, or lateral movement on the slope. As mentioned in this disclosure, machine speed or ground speed may also include the two-dimensional or three-dimensional direction of travel.

[0042] Loss sensor 152 schematically provides an output signal indicating the amount of grain loss occurring on the right and left sides of cleaning subsystem 118. In some examples, sensor 152 is an impact sensor that counts grain impacts per unit time or per unit distance traveled to provide an indication of grain loss occurring at cleaning subsystem 118. The impact sensors for the right and left sides of cleaning subsystem 118 can provide individual signals or combined or aggregated signals. In some examples, sensor 152 may include a single sensor, rather than providing a separate sensor for each cleaning subsystem 118.

[0043] Separator loss sensor 148 provides indication of the left and right separators (in Figure 1 (Not shown separately) The grain loss signal in the separator. The separator loss sensor 148 can be associated with the left and right separators and can provide separate grain loss signals or combined or aggregated signals. In some cases, various different types of sensors can also be used to sense grain loss in the separator.

[0044] 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 cleaning 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 gap sensor that senses a size of openings in the chaffer 122; a screen gap sensor that senses a size of openings in the screen 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 elsewhere 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 elsewhere 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. An internal material distribution sensor can sense a material distribution of an interior of the agricultural harvester 100.

[0045] Examples of sensors for detecting or sensing power properties include, but are not limited to, voltage sensors, current sensors, torque sensors, hydraulic pressure sensors, hydraulic flow sensors, force sensors, bearing load sensors, and rotational sensors. Power properties can be measured at different levels of granularity. For example, power usage can be sensed at a machine level, at a subsystem level, or through individual components of a subsystem.

[0046] Examples of sensors for detecting internal material distribution include, but are not limited to, one or more cameras, capacitive sensors, electromagnetic or ultrasonic time-of-flight reflection sensors, signal attenuation sensors, weight or mass sensors, material flow sensors, and the like. These sensors can be placed at one or more locations in the agricultural harvester 100 to sense the distribution of material in the agricultural harvester 100 during operation of the agricultural harvester 100.

[0047] Examples of sensors for detecting or sensing the pitch or roll of the agricultural harvester 100 include accelerometers, gyroscopes, inertial measurement units, gravity sensors, magnetometers, and the like. These sensors can also indicate the slope of the terrain in which the agricultural harvester 100 is currently located.

[0048] Before describing how the agricultural harvester 100 develops a functional prediction map and uses the functional prediction 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 prior information map and combining information from the prior information map with georeferenced sensor signals generated by in-field sensors, where the sensor signals can indicate one or more of a field characteristic, a crop property characteristic, a grain characteristic, or an agricultural harvester 100 characteristic. The field characteristic can include, but is not limited to, characteristics of the field such as slope, weed characteristics (e.g., weed strength or weed type), soil moisture, and surface quality. The crop property characteristic can include, but is not limited to, crop height, crop moisture, grain quality, crop density, and crop status. The grain characteristic can include, but is not limited to, grain moisture, grain size, grain test weight; and the agricultural harvester 100 characteristic can include, but is not limited to, heading, loss level, job quality, fuel consumption, internal material distribution, tailings characteristics, and power utilization. The relationship between the characteristic values obtained from the in-field sensor signals and the prior information map values is identified, and this relationship is used to generate a new functional prediction map 263. The functional prediction map 263 predicts values at different geographic locations in the field, and one or more of these values can be used to control the machine. In some cases, the functional prediction map 263 can be presented to a user, such as an operator of an agricultural work machine, which can be an agricultural harvester. The functional prediction map 263 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 prediction map 263 to perform editing operations and other user interface operations. In some cases, both the functional prediction map and the prior information map can be used to control the agricultural work machine (such as an agricultural harvester), presented to an operator or other user, and presented to the operator or user for the operator or user to interact with.

[0049] In reference to Figure 2and FIG. 3 describe general methods, after which reference is made to FIG. 4 and Figure 5 A more specific method for generating a functional prediction map 263 is described, which functional prediction map can be presented to an operator or user, or used to control the agricultural harvester 100, or both. Again, while the present discussion is directed to agricultural harvesters, and particularly combine harvesters, 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, a data store 202, a geo-location sensor 204, a communication system 206, and one or more field sensors 208 that sense one or more agricultural properties while the harvesting operation is in progress. Agricultural properties can include any property that can have an effect on the harvesting operation. Some examples of agricultural properties include properties of the agricultural harvester, the field, the plants on the field, and the weather. Other types of agricultural properties are also included. 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 during the course of the agricultural operation. The prediction model generator 210 schematically includes a priori 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 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 clean-up 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 clean-up subsystem 254, and the subsystems 216 can include a variety of other subsystems 256.

[0051] Figure 2It is also shown that the agricultural harvester 100 can receive a prior information map 258. As described below, the prior map information map 258 includes, for example, a topographic map from a prior operation in the field (e.g., a range scan operation completed by an unmanned aerial vehicle from a known altitude), a topographic map sensed by an airplane, a topographic map sensed by a satellite, a topographic map sensed by a ground vehicle such as a GPS-equipped planter, etc. The prior information map 258 can also include one or more of a seed genotype map, a vegetation index (VI) map, a yield map, a biomass map, or a weed map. However, the prior map information can also include other types of data obtained prior to the harvesting operation or maps from prior operations. For example, a topographic map can be retrieved from a remote source such as the United States Geological Survey (USGS). Figure 2 It is also shown that an operator 260 can operate 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, control 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 described above 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 prior information map 258 can be downloaded onto the agricultural harvester 100 using the communication system 206 or otherwise and stored in the data storage 202. 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 a combination 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 geographic position sensor 204 illustratively senses or detects a geographic position or location of the agricultural harvester 100. The geographic position sensor 204 can include, but is not limited to, a global navigation satellite system (GNSS) receiver that receives signals from GNSS satellite transmitters. The geographic position sensor 204 can also include a real-time kinematic (RTK) component configured to improve the accuracy of position data derived from GNSS signals. The geographic position sensor 204 can include a dead reckoning system, a cellular triangulation system, or any of a variety of other geographic position sensors.

[0054] The field sensors 208 can be any of the sensors described above with reference to Figure 1 The field sensors 208 include on-board sensors 222 mounted on the on-board agricultural harvester 100. Such sensors can include, for example, a speed sensor (e.g., a GPS, a speedometer, or a compass), an image sensor inside the agricultural harvester 100 such as one or more clean grain cameras mounted to identify material distribution in the agricultural harvester 100 (e.g., in a residue subsystem or a cleaning system), a grain loss sensor, a tailings characteristic sensor, and a grain quality sensor. The field sensors 208 also include remote field sensors 224 that capture field information. Field data includes data acquired from sensors mounted on the harvester or data acquired by any sensor in which data is detected during a harvesting operation.

[0055] The predictive model generator 210 generates models that indicate relationships between values sensed by the field sensors 208 and characteristics mapped to the field by the prior information map 258. For example, if the prior information map 258 maps terrain characteristics to different locations in the field and the field sensors 208 sense values indicative of internal material distribution, the prior information variable to field variable model generator 228 generates a predictive model that models the relationship between terrain characteristics and internal material distribution. The predictive model can also be generated based on characteristics from one or more of the prior information map 258 and one or more multiple field data values generated by the field sensors 208. The predictive map generator 212 then generates a functional prediction map 263 that predicts values of characteristics such as internal material distribution, tailings characteristics, loss, or grain quality sensed by the field sensors 208 at different locations in the field based on the prior information map 258 using the predictive models generated by the predictive model generator 210.

[0056] In some examples, the type of values in the function prediction 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 function prediction map 263 can have different units than the data sensed by the field sensors 208. In some examples, the type of values in the function prediction 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 function prediction map 263. In some examples, the type of data in the function prediction map 263 can be different than the type of data in the prior information map 258. In some cases, the type of data in the function prediction map 263 can have different units than the data in the prior information map 258. In some examples, the type of data in the function prediction map 263 can be different than the type of data in the prior information map 258, but related to the type of data in the prior information map 258. For example, in some examples, the type of data in the prior information map 258 can dictate the type of data in the function prediction map 263. In some examples, the type of data in the function prediction 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 prior information map 258. In some examples, the type of data in the function prediction 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 prior information map 258. In some examples, the type of data in the function prediction 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 prior information map 258, and different than the other.

[0057] The prediction map generator 212 can use the characteristics in the prior information map 258 and the models generated by the prediction model generator 210 to generate a function prediction map 263 that predicts characteristics at different locations in the field. The prediction map generator 212 therefore outputs a prediction map 264.

[0058] As Figure 2As shown, the prediction map 264 predicts values of sensed characteristics (sensed by the field sensors 208) or characteristics related to the sensed characteristics at various locations on the field based on the prior information values in the prior information map 258 at the locations and using a prediction model. For example, if the prediction model generator 210 has generated a prediction model that indicates a relationship between terrain characteristics and grain quality, the prediction map generator 212 generates the prediction map 264 that predicts values of grain quality at different locations on the field given the terrain characteristics at the different locations on the field. The terrain characteristics at the locations obtained from the terrain map and the relationship between the terrain characteristics and the grain quality characteristics obtained from the prediction model are used to generate the prediction map 264. The predicted grain quality can be used by the control system to adjust, for example, one or more of the sieve and chaffer opening, rotor operation, concave gap (i.e., the spacing between the threshing rotor and the concave), or the cleaning fan speed.

[0059] Some variations in the type of data mapped in the prior information map 258, the type of data sensed by the field sensors 208, and the type of data predicted on the prediction map 264 will now be described. These are merely examples to illustrate that the data types can be the same or different.

[0060] In some examples, the type of data in the prior information map 258 is different than the type of data sensed by the field sensors 208, but the type of data in the prediction map 264 is the same as the type of data sensed by the field sensors 208. For example, the prior information map 258 can be a terrain map, and the variable sensed by the field sensors 208 can be a grain quality characteristic. The prediction map 264 can then be a predicted machine map that maps predicted machine characteristic values to different geographic locations in the field.

[0061] Further, in some examples, the type of data in the prior information map 258 is different than the type of data sensed by the field sensors 208, and the type of data in the prediction map 264 is different than both the type of data in the prior information map 258 and the type of data sensed by the field sensors 208. For example, the prior information map 258 can be a terrain map, and the variable sensed by the field sensors 208 can be machine pitch / roll. The prediction map 264 can then be a predicted internal distribution map that maps predicted internal distribution values to different geographic locations in the field.

[0062] In some examples, the prior information map 258 is from a previous operation through the field, and the data type is different from the data type sensed by the in-field sensor 208, but the data type in the prediction map 264 is the same as the data type sensed by the in-field sensor 208. For example, the prior information map 258 can be a seed genotype map generated during planting, and the variable sensed by the in-field sensor 208 can be loss. The prediction map 264 can then be a predicted loss map that maps predicted grain loss values to different geographic locations in the field. In another example, the prior information map 258 can be a seeding genotype map, and the variable sensed by the in-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.

[0063] In some examples, the prior information map 258 is from a previous operation through the field, and the data type is the same as the data type sensed by the in-field sensor 208, and the data type in the prediction map 264 is also the same as the data type sensed by the in-field sensor 208. For example, the prior information map 258 can be a yield map generated during the previous year, and the variable sensed by the in-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 examples, the prediction model generator 210 can use relative yield differences in the geographically referenced prior information map 258 from the previous year to generate a prediction model that models a relationship between relative yield differences on the prior information map 258 and yield values sensed by the in-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.

[0064] In some examples, the prediction map 264 can be provided to a control zone generator 213. The control zone generator 213 groups consecutive individual point data values on the prediction map 264 into control zones. The control zones can include two or more consecutive portions of a field, such as a region, for which a control parameter corresponding to the control zone used to control a controllable subsystem is constant. For example, the response time to change a setting of a controllable subsystem 216 can not be satisfactory to respond to changes in values contained in a map, such as the prediction 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 subsystem 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 subsystem 216 or group of controllable subsystems 216. The control zones can be added to the prediction map 264 to obtain a prediction control zone map 265. The prediction control zone map 265 can thus be similar to the prediction map 264 except that the prediction control zone map 265 includes control zone information defining the control zones. Thus, as described herein, a functional prediction map 263 can or can not include control zones. Both the prediction map 264 and the prediction control zone map 265 are functional prediction maps 263. In one example, the functional prediction map 263 does not include control zones, such as the prediction map 264. In another example, the functional prediction map 263 does include control zones, such as the prediction control zone map 265. In some examples, if an intercrop production system is implemented, multiple crops can be present in the field at the same time. In this case, the prediction map generator 212 and the control zone generator 213 are able to identify the location and characteristics of the two or more crops and then generate the prediction map 264 and the prediction control zone map 265 accordingly.

[0065] It should also be understood that the control zone generator 213 can cluster values to generate control zones and that the control zones can be added to the prediction control zone map 265 or a separate map showing only the generated control zones. In some examples, the control zones can be used for control or calibration of the agricultural harvester 100 or both. In other examples, the control zones can be presented to the operator 260 and used for control or calibration of the agricultural harvester 100, and in other examples, the control zones can be presented to the operator 260 or another user only or stored for later use.

[0066] 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 harvesters 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.

[0067] In some examples, the prediction map 264 can be provided to the route / task generator 267. The route / task generator 267 plots a travel path for the agricultural harvester 100 to travel during the harvesting operation based on the prediction map 264. The travel path can also include machine control settings corresponding to locations along the travel path. For example, if the travel path goes up a hill, the travel path can include a control indicating to direct power to the propulsion system to maintain the speed or feed rate of the agricultural harvester 100 at a point before the hill. In some examples, the route / task generator 267 analyzes different orientations of the agricultural harvester 100 and the predicted machine characteristics generated from the prediction map 264 for a plurality of different travel routes and selects a route that has a desired outcome (e.g., a fast harvesting time or a desired power usage or material distribution uniformity).

[0068] 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 displayed power utilization on the maps, for example, 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 other functions 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 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, when the agricultural harvester 100 approaches a descending terrain with an estimated speed value above a selected threshold, the feed rate controller 236 can reduce the speed of the machine 100 to maintain a constant speed of the biomass through the agricultural harvester 100. 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. For example, when the agricultural harvester 100 approaches a descending terrain with an estimated speed value above a selected threshold, the belt conveyor belt controller 240 can increase the speed of the belt conveyor belt to prevent the accumulation of material on the belt.The header position controller 242 can generate control signals to control the position of the header deck 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 cleanout controller 245 can generate control signals to control the machine cleanout subsystem 254. For example, when the agricultural harvester 100 is about to travel laterally on a slope, in which case the internal material distribution is estimated to be disproportionately located on one side of the cleanout subsystem 254, the machine cleanout controller 245 can adjust the cleanout subsystem 254 to address or correct for the disproportionate material. 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.

[0069] Figure 3A and Figure 3B FIG. 3 (collectively referred to herein as FIG. 3) illustrates a flowchart diagram that illustrates one example of operations of the agricultural harvester 100 in generating the prediction map 264 and the prediction control zone map 265 based on the prior information map 258.

[0070] At 280, the agricultural harvester 100 receives a prior information map 258. Examples of the prior information map 258 or receiving the prior information map 258 are discussed with respect to blocks 281, 282, 284, and 286. As discussed above, as shown in block 282, the prior information map 258 maps values of a variable corresponding to a first characteristic to different locations in the field. As shown in block 281, receiving the prior information map 258 can include selecting one or more of a plurality of possible prior information maps available. For example, one prior information map can be a topographical profile map generated from aerial phase profilometry images. Another prior information map can be a map generated during a previous pass through the field, which can be performed by a different machine in the field such as a sprayer or other machine performing a previous operation. 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 during a previous field operation by a GPS receiver mounted on the equipment piece. For example, the data can be collected in a laser radar range scanning operation a year ago, or earlier in the current growing season, or other time. The data can be based on data detected or received in a manner different than using laser radar range scanning. For example, a drone equipped with a fringe projection profilometry system can detect the profile or elevation of the terrain. Or for example, some terrain characteristics can be estimated based on weather patterns (e.g., ruts formed due to erosion or clod breakage in freeze-thaw cycles). In some examples, the prior information map 258 can be created by combining data from multiple sources such as the sources listed above. Or for example, the data for the prior information map 258 (e.g., a topographical map) can be transmitted to the agricultural harvester 100 using the communication system 206 and stored in the data storage 202. The data for the prior information map 258 can also be provided to the agricultural harvester 100 in other ways using the communication system 206, and this is represented by block 286 in the flowchart of FIG. 3. In some examples, the prior information map 258 can be received by the communication system 206.

[0071] At the start of a harvesting operation, the field sensors 208 generate sensor signals indicative of one or more field data values indicative of machine characteristics, such as power usage, machine speed, internal material distribution, grain loss, tail characteristics (e.g., tail level, tail flow, tail volume, and tail composition), or grain quality. Examples of field sensors 208 are discussed with respect to blocks 222, 290, and 226. As explained above, the field sensors 208 include on-board sensors 222, remote field sensors 224, such as UAV-based sensors that fly over the field each time to gather field data (as shown in block 290), or other types of field sensors specified by the field sensors 226. In some examples, data from the on-board sensors is georeferenced using position, heading, or speed data from the geo-location sensors 204.

[0072] The prediction model generator 210 controls the prior information variables to the field variables model generator 228 to generate a model that models the relationship between the mapped values contained in the prior information map 258 and the field values sensed by the field sensors 208, as shown in block 292. The characteristics or data types represented by the mapped values in the prior information map 258 and the field values sensed by the field sensors 208 can be the same characteristics or data types or different characteristics or data types.

[0073] The relationship or model generated by the prediction model generator 210 is provided to the prediction map generator 212. The prediction map generator 212 uses the prediction model and the prior information map 258 to generate a prediction map 264 that predicts values of the characteristics sensed by the field sensors 208 at different geographic locations in the field being harvested, or different characteristics related to the characteristics sensed by the field sensors 208, as shown in block 294.

[0074] It should be noted that in some examples, the prior information map 258 can include two or more different maps or two or more different layers of a single map. Each of the two or more different maps or each of the two or more different layers of a single map maps a different type of variable to a geographic location in the field. In such examples, the prediction model generator 210 generates a prediction model that models a relationship between the on-site data and each of the different variables mapped by the two or more different maps or the two or more different layers. Similarly, the on-site 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 a relationship between each type of variable mapped by the prior information map 258 and each type of variable sensed by the on-site sensors 208. The prediction map generator 212 can use the prediction model and each of the maps or layers in the prior information map 258 to generate a functional prediction map that predicts values of each sensed characteristic (or a characteristic related to the sensed characteristic) sensed by the on-site sensors 208 at different locations in the field being harvested.

[0075] 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 region generator 213 or to both. Some examples of different ways in which the prediction map 264 can be configured or output are described with respect to blocks 296, 293, 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.

[0076] The route / task generator 267 plots a travel path for the agricultural harvester 100 to travel during a harvesting operation based on the prediction map 204, as indicated by block 293. The control region generator 213 can partition the prediction map 264 into control regions based on values on the prediction map 264. Consecutively geolocated values within a threshold of each other can be grouped into control regions. 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 regions can be based on responsiveness of the control system 214, controllable subsystems 216, or 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 region generator 213 can configure the prediction control region 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 region map 265 or both can include one or more of the predicted values on the prediction map 264 in relation to geographic locations, control regions on the prediction control region map 265 in relation to geographic locations, and the set values or control parameters used based on the predicted values on the prediction map 264 or the regions on the prediction control region 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 regions on the prediction control region 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 the information is presented to more than one location, an authentication or 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. 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 elements are visible on the physical display device, and which values the corresponding person can change. For example, a local operator of the machine 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 at a remote location can be able to see the prediction map 264 on a display, but not make 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 change the prediction map 264 that is used in machine control. This is one example of an authorization hierarchy that can be implemented. The prediction map 264 or the prediction control region map 265 or both can also be configured in other ways, as indicated by block 297.

[0077] At block 298, inputs from the geo-location sensor 204 and other field sensors 208 are received by the control system. Block 300 represents the control system 214 receiving input from the geo-location sensor 204 identifying the geo-location of the agricultural harvester 100. Block 302 represents the control system 214 receiving sensor input indicative of the trajectory or heading of the agricultural harvester 100, and block 304 represents the control system 214 receiving the speed of the agricultural harvester 100. Block 306 represents the control system 214 receiving other information from the various field sensors 208.

[0078] 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, and 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, the controllable subsystems 216 that are controlled, and the timing of the control signals can be based on various latencies of the crop flow through the agricultural harvester 100 and responsiveness of the controllable subsystems 216.

[0079] As an example, the generated prediction map 264 in the form of a functional prediction crop loss map can be used to control one or more subsystems 216. For example, the functional prediction loss map can include crop loss values that are geographically referenced to locations within the field being harvested. The crop loss values from the functional prediction loss map can be extracted and used to control fan speed to ensure that the clean-out fans 120 are able to minimize crop loss through the clean-out subsystem 118 as the agricultural harvester 100 moves through the field. The foregoing example involving the use of a prediction crop loss map is provided by way of example only. Thus, a wide variety of other control signals can be generated using values obtained from a prediction machine map or other types of prediction maps to control one or more of the controllable subsystems 216.

[0080] At block 312, a determination is made as to whether the harvesting operation has been completed. If harvesting has not been completed, the process proceeds to block 314 where the reading of field sensor data from the geo-location sensor 204 and the field sensors 208 (and possibly other sensors) continues.

[0081] In some examples, at block 316, the agricultural harvester 100 can also detect a learning trigger criterion 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.

[0082] The learning trigger criterion 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 models when a threshold amount of field sensor data is obtained from the field sensors 208. In such examples, an amount of field sensor data received from the field sensors 208 that exceeds a 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 harvesting operations, receiving a threshold amount of field sensor data from the field sensors 208 triggers creating a new relationship 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 a threshold amount of field sensor data for triggering creation of a new prediction model.

[0083] In other examples, the learning trigger criterion can be based on an extent of change in the field sensor data from the field sensors 208 relative to previous values or relative to a threshold. For example, if a change within the field sensor data (or a relationship between the field sensor data and information in the prior information map 258) is within a 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, for example, if the change within the field sensor data exceeds the range, or exceeds a predetermined amount or threshold, or for example, if the relationship between the field sensor data and information in the prior information map 258 changes by a predetermined amount, 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 a size of the amount of data that exceeds a selected range or a size of the change in the relationship between the field sensor data and information in the prior information map 258, can be used as a trigger to cause generation of a new prediction model and prediction map. The threshold, range, and defined amount can be set as a default, or set by an operator or user through interaction with a user interface, or set by an automated system, or otherwise.

[0084] Other learning trigger criteria can also be used. For example, if the prediction model generator 210 switches to a different prior information map (different from the initially selected prior information map 258), the switch to the different prior information map can trigger relearning by the prediction model generator 210, the prediction map generator 212, the control region 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 region can also be used as a learning trigger criterion.

[0085] In some cases, the operator 260 can also edit the prediction map 264 or the prediction control region map 265 or both. The editing can change values on the prediction map 264, or change the size, shape, location, or existence of control regions, or values on the prediction control region map 265, or both. Block 321 shows that the edited information can be used as a learning trigger criterion.

[0086] 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 operator-initiated adjustments to the controllable subsystem that reflect that the operator 260 desires the controllable subsystem to operate differently than commanded by the control system 214. Thus, the operator-initiated changes to the settings by the operator 260 can result in adjustments based on the operator 260 (as shown by block 322), the prediction model generator 210 relearning the model, the prediction map generator 212 regenerating the prediction map 264, the control region generator 213 regenerating the control regions on the prediction control region map 265, and the control system 214 relearning its control algorithm or performing machine learning on one of the controller components 232-246 in the control system 214. Block 324 represents using other trigger learning criteria.

[0087] In other examples, the 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. This is shown by block 326.

[0088] If relearning is triggered (whether based on a learning trigger criterion or based on the passage of a time interval, as shown by block 326), one or more of the prediction model generator 210, the prediction map generator 212, the control region generator 213, and the control system 214 perform machine learning to generate new prediction models, new prediction maps, new control regions, 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 time the learning operation was performed. Performing the relearning is indicated by block 328.

[0089] 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.

[0090] It will be noted that while some examples herein describe the prediction model generator 210 and the prediction map generator 212 receiving a prior 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 model and a functional prediction map, respectively, including prediction maps, such as a functional prediction map generated during a harvesting operation.

[0091] Figure 4A is Figure 1 A block diagram of a portion of the agricultural harvester 100 shown in FIG. 1. In particular, Figure 4A An example of the prediction map generator 212 is shown in particular more detail. Figure 4AInformation flow between the illustrated different components is also illustrated. The prediction model generator 210 receives a received information map 259. The information map 259 includes values of agricultural properties corresponding to different geographic locations in a field. In some examples, the information map 259 can be the prior information map 258. In some examples, the information map 259 can be a prediction map including predicted values of agricultural properties corresponding to different geographic locations in a field, e.g., a functional prediction map generated using the method described in FIG. 3. In some examples, the information map 259 can include one or more of a topography map 332, a seed genotype map 335, a VI map 336, a yield map 338, a biomass map 340, or a weed map 342. The prediction model generator 210 also receives geographic locations 334, or an indication of geographic locations, from the geographic location sensor 204. The field sensor 208 detects values of agricultural properties indicative of properties of the processed material. In some examples, the processed material can include grain or other crops, chaff, and MOG. The field sensor 208 can thus include one or more of a chaff property sensor 344 that senses chaff properties, a loss sensor 346 that senses properties indicative of grain or crop loss, a grain quality sensor 348 that senses properties indicative of grain quality, or an internal distribution sensor 350 that senses properties indicative of internal distribution of the processed material in the agricultural harvester 100, and a processing system 352. In some cases, one or more of the sensors 344, 346, 348, and 350 can be located on-board the agricultural harvester 100. The processing system 352 processes sensor data generated by one or more of the sensors 344, 346, 348, and 350 to generate processed data 354, some examples of which are described below.

[0092] In some examples, one or more of the sensors 344, 346, 348, and 350 can generate electronic signals indicative of the properties sensed by the sensors. The processing system 352 processes one or more of the sensor signals obtained via the sensors to generate processed data identifying one or more properties. The properties identified by the processing system 352 can include internal material distribution, loss, grain quality, or chaff properties.

[0093] The in-field sensor 208 can be or include an optical sensor, such as a camera disposed to view an interior portion of the agricultural harvester that is processing agricultural material. Thus, in some examples, the processing system 352 is operable to detect an interior distribution of agricultural material through the agricultural harvester 100 based on images captured by the interior distribution sensor 350. In other examples, the process camera can be the clean grain camera 150 and the processing system 352 is operable to detect grain quality. In other examples, the process camera can be configured to capture images of tailings material and the processing system 352 is operable to detect tailings characteristics. In other examples, the loss sensor 346 can be or include a separator loss sensor 148 or a loss sensor 152 that senses losses in the cleaning system 118 and the processing system 352. The loss sensor 346 is operable to detect crop losses.

[0094] In other examples, the in-field sensor 208 can be or include a GPS sensor that senses a location of the machine. In this case, the processing system 352 can also derive a speed and direction from the sensor signal. In another example, the in-field sensor 208 can include one or more MOG moisture sensors that detect a moisture characteristic of MOG in one or more subsystems on the agricultural harvester 100. In this case, the processing system 352 can detect and output MOG moisture information.

[0095] Other machine characteristics and sensors can also be used. In some examples, raw or processed data from the sensors 344, 346, 348, and 350 can be presented to the operator 260 via the operator interface mechanism 218. The operator 260 can be located on the agricultural harvester 100 or at a remote location.

[0096] Figure 4B is a block diagram showing one example of the prediction model generator 210 in more detail. In this example, the prediction model generator 210 includes a data collection module 402, a feature extraction module 404, a model generation module 406, and a model evaluation module 408. Figure 4BIn the illustrated example, the predictive model generator 210 can include a terrain characteristic to residue characteristic model generator 356, a terrain characteristic to grain quality model generator 358, a terrain characteristic to loss model generator 360, a terrain characteristic to internal distribution model generator 362, a vegetation index to residue characteristic model generator 364, a vegetation index to grain quality model generator 366, a vegetation index to loss model generator 368, a vegetation index to internal distribution model generator 370, a genotype to residue characteristic model generator 372, a genotype to grain quality model generator 374, a genotype to loss model generator 376, a genotype to internal distribution model generator 378, a yield to residue characteristic model generator 380, a yield to grain quality model generator 382, a yield to loss model generator 384, a yield to internal distribution model generator 386, a biomass to residue characteristic model generator 388, a biomass to grain quality model generator 390, a biomass to loss model generator 392, a biomass to internal distribution model generator 394, a weed characteristic to residue characteristic model generator 396, a weed characteristic to grain quality model generator 398, a weed characteristic to loss model generator 400, a weed characteristic to internal distribution model generator 402, a combined model generator 404, and other items 406. Figure 4B Each of the model generators illustrated in FIG. 3 generates a model that models a relationship between a value on an information map and a value sensed by a field sensor 208. The combined model generator 404 can generate one or more models based on data from different combinations of one or more information maps 259 and one or more field sensors 208.

[0097] The terrain-to-spoil characteristics model generator 356 receives the processed data 354 and the terrain map 332 and models the relationship between the terrain characteristics on the terrain map 332 and the spoil characteristics sensed by the spoil characteristics sensor 334. The spoil level can be affected by the degree of tilt of the agricultural harvester 100 in the fore-aft (tilt) and side-to-side (roll) directions. In one example, machine tilt factors indicative of the orientation of the agricultural harvester 100 are derived from the terrain map 332, although the machine tilt factors can also be obtained from machine orientation sensors on the agricultural harvester 100. There can be different relationships between the spoil level and the pitch-up angle, and the pitch-down angle and the roll angle. Thus, in one example, the terrain-to-spoil characteristics model generator 356 can generate multiple different models, each modeling a different relationship, or a single model modeling some or all of the relationships. For example, the model generator 356 can generate a model modeling the relationship between the spoil level and the pitch-up. The model generator 356 can generate a separate model modeling the relationship between the spoil level and the pitch-down. In another example, multiple relationships can be modeled by a single model. Similarly, the longer the agricultural harvester 100 spends in a given orientation (e.g., pitch-up, pitch-down, etc.), the greater the accumulation of spoil volume or spoil level at different locations in the agricultural harvester 100. Thus, the model generator 356 can also generate a model modeling the relationship between the rate of increase or decrease in the spoil level and the tilt conditions, such that the spoil level can be more accurately predicted over time. Furthermore, assuming that these settings affect the spoil level and the rate of change of the spoil level, the model generator 356 can generate models that take into account the chaffer, screen, and fan speed settings. Furthermore, the type of material in the spoil (e.g., the composition of the material) can also be affected by the tilt of the agricultural harvester. The types of material that can be identified in the spoil can include clean or free grain, unthreshed grain, and MOG types (e.g., small, large, green, etc.). The composition can also include other things, such as the type of material in the spoil or the relative amounts of different materials in the spoil. Thus, the model generator 356 can generate models modeling the relationship between the type or composition of material in the spoil and the tilt of the agricultural harvester 100.

[0098] The terrain characteristic to grain quality model generator 358 can generate a model that models the relationship between terrain characteristics on the terrain map 332 and grain quality sensed by the grain quality sensor 348. Grain quality in the agricultural harvester 100 can be affected by the degree of tilt of the agricultural harvester 100 in the fore-aft direction and in the side-to-side direction. Tilt factors that indicate the orientation of the agricultural harvester 100 can be derived from the terrain map 332 or from orientation sensors on the agricultural harvester 100. There can be different relationships between grain quality characteristics and the pitch angle and the roll angle. Thus, the model generator 358 can generate different models that model these different relationships or a single model that models multiple relationships. Similarly, different grain quality characteristics can be modeled by separate models or can be part of a cumulative model. Such grain quality characteristics can include clean grain, broken grain, unthreshed grain, MOG level, and MOG type. Further, the longer the agricultural harvester 100 spends in a particular direction, the greater the impact that can be had on the grain quality characteristics. Thus, the model generator 358 can generate a model that models the relationship between the rate of change of the grain quality characteristics and the tilt conditions, such that the grain quality characteristics can be more accurately predicted over longer periods of time. Further, the grain sieve, the screen, the fan speed, the separator speed, the threshing machine speed, and the pocket gap settings can influence the grain quality characteristics as well as the change and rate of change of the grain quality characteristics. Thus, the model generator 358 can generate separate models that model the relationship between one or more of the grain sieve, the screen, the fan speed, the separator speed, the threshing machine speed, and the pocket gap and the grain quality characteristics and the rate of change of the grain quality characteristics, or the model generator 358 can generate a cumulative model that models more than one of these relationships.

[0099] Grain loss from the agricultural harvester 100 can be heavily influenced by the slope on which the agricultural harvester 100 is operating. As a result, grain loss can be heavily influenced by the orientation of the agricultural harvester 100. Pitch of the agricultural harvester 100 affects the residence time of grain on the cleaning subsystem 118 and can affect the degree to which grain is effectively separated. The lateral inclination (or roll direction) of the agricultural harvester 100 can determine how much grain is building up on or overloading one side of the cleaning subsystem 118, thus essentially underutilizing the other side of the cleaning subsystem 118 and leading to loss on one side of the agricultural harvester 100 due to material building up on one side of the cleaning subsystem 118. While this phenomenon is repeatable, the grain levels inside the agricultural harvester 100 vary depending on the severity of the incline, the amount of time the agricultural harvester 100 spends on the slope, the machine settings, and the crop conditions. As a result, the terrain characteristics to loss model generator 360 models the relationship between the terrain characteristics from the map 332 and the output of the loss sensor 346.

[0100] Similarly, the terrain characteristics to internal distribution model generator 362 can generate a model that models the relationship between the terrain characteristics (e.g., slope, which can determine the orientation of the agricultural harvester 100) on the map 332 and the internal distribution of material within the agricultural harvester 100. The internal distribution can affect the loss and other items within the agricultural harvester 100. Furthermore, the amount of time on the slope affects the loss, the rate of change of loss, the material distribution, and the rate of change of material distribution. As a result, the terrain characteristics to loss model generator 360 can model the relationship between the amount of time the agricultural harvester 100 is in a given orientation and the loss. The terrain characteristics to internal distribution model 362 can model the relationship between the amount of time the agricultural harvester 100 is in a given orientation and the internal material distribution within the agricultural harvester 100.

[0101] The vegetation index to tailings characteristics model generator 364 can generate a model that models the relationship between the characteristics on the VI map 336 and the tailings characteristics sensed by the tailings characteristics sensor 334. The model generator 364 can also receive input from other sensors, such as a grain moisture sensor or a MOG moisture sensor. The amount of crop material processed by the agricultural harvester 100 can be estimated or indicated by the characteristics on the VI map 336. This can also affect the tailings characteristics, such as the tailings composition, the tailings level, the tailings flow, or the tailings volume in the agricultural harvester 100. As a result, the vegetation index to tailings characteristics model generator 364 models the relationship between the vegetation index characteristics on the VI map 336 and the output from the tailings characteristics sensor 344.

[0102] The vegetation index to grain quality model generator 366 can generate a model that models the relationship between the VI characteristics on the VI map 336 and the output of the grain quality sensor 348. The grain quality characteristics sensed by the grain quality sensor 348 can include clean grain, broken grain, unthreshed grain, MOG levels, and MOG types that enter the clean grain bin, as described above. For example, when harvesting canola, it can be difficult to generate a fan speed that retains all of the grain but blows out all of the pods, stems, and pith, among other things. The efficiency with which the fan performs this operation can depend on the biomass of the plant material, which itself can depend on the moisture content of the plant, which can be indicated by the VI characteristics. Thus, the model generator 366 can use the relationship between the characteristics on the VI map 336 and the grain quality characteristics sensed by the sensor 348 to generate a model that models this relationship.

[0103] Further, when more biomass or more grain is passing through the agricultural harvester 100 at a particular time, this can result in a higher loss value. Likewise, the magnitude and frequency of the changes in biomass can also result in a loss value. For example, a higher crop volume or density for a short time can have a short-lived impact, but if the higher crop volume or density is repeated frequently, this can result in a higher loss value. Thus, the vegetation index to loss model generator 368 can generate a model that models the relationship between the values on the VI map 336 and the output of the loss sensor 346.

[0104] As described above, the amount of biomass processed by the agricultural harvester 100 can also affect the internal distribution of material within the agricultural harvester 100. Higher levels of biomass can result in higher levels of material in different areas of the agricultural harvester 100. Thus, the model generator 370 can generate a model that models the relationship between the characteristics on the VI map 336 and the output of the internal distribution sensor 350.

[0105] Different plant genotypes have different characteristics that can themselves indicate how well the grain is separated from the MOG or how solid the parts of the plant (e.g., the cob or the grain) are. These characteristics can affect the characteristics of the tailings, such as the composition of unthreshed grain in the tailings, the amount of MOG in the tailings, whether the MOG is broken into larger or smaller pieces, among other things. Thus, the genotype to tailings characteristics model generator 372 can generate a relationship between the genotype values on the seed genotype map 335 and the tailings characteristics sensor values generated by the tailings characteristics sensor 334.

[0106] Similarly, different genotypes can perform differently in terms of grain quality. For example, different genotypes can result in different amounts of broken grain, unthreshed grain, MOG levels, and MOG types given a set of machine settings on the agricultural harvester 100. Thus, the genotype-to-grain quality model generator 374 generates a model that models the relationship between the seed genotype characteristics on the seed genotype map 335 and the grain quality characteristics sensed by the grain quality sensor 348.

[0107] The size or quality of the grain can also differ by genotype. This can result in different levels of loss because larger grains can have a higher tendency to bounce out of the agricultural harvester 100, while smaller grains can have a higher tendency to be blown out by the cleaning fan. Different genotypes can also have different plant compositions, thus affecting the level of loss due to how the crop breaks apart during processing within the agricultural harvester 100. Thus, the genotype-to-loss model generator 376 can generate a model that models the relationship between the seed genotype characteristics on the map 335 and the output of the loss sensor 346.

[0108] Different genotypes can also result in different internal distributions. For example, crops with different relative maturities at the time of harvest can have different MOG moisture levels, which can result in more or less material being processed by the agricultural harvester 100 at any given time. Thus, the genotype-to-internal distribution model generator 378 can generate a model that models the relationship between the seed genotype characteristics on the map 335 and the output of the internal distribution sensor 350. The map 335 and the output of the internal distribution sensor 350.

[0109] Yield can also affect the tailings characteristics. High-yield areas in a field can produce more tailings with different compositions than low-yield areas. Thus, the yield-to-tailings characteristics model generator 380 can generate a model that models the relationship between the predicted yield values on the yield map 338 and the output from the tailings characteristics sensor 334.

[0110] Yield can also affect the grain quality. For example, it can be more difficult to separate MOG from grain in areas with increased yield, resulting in more MOG in the clean grain bin 132 of the agricultural harvester 100. Thus, the yield-to-grain quality model generator 382 can generate a model that models the relationship between the predicted yield values on the yield map 338 and the grain quality characteristics sensed by the grain quality sensor 348.

[0111] Yield can also affect losses. As more grain passes through the agricultural harvester 100, it can result in higher levels of loss. Thus, high yield areas can also produce high loss levels. The yield-to-loss model generator 384 can therefore generate a model that models the relationship between predicted yield values on the yield map 338 and loss values output by the loss sensor 346.

[0112] Yield can also affect the internal distribution of material within the agricultural harvester 100. High yield areas are typically accompanied by high levels of biomass being processed by the agricultural harvester 100. The high levels of biomass typically accompanying high yield areas can affect the amount and distribution of material within the agricultural harvester 100. Thus, the yield-to-internal distribution model generator 386 can generate a model that models the relationship between predicted yield values on the yield map 338 and internal distribution characteristics sensed by the internal distribution sensor 350.

[0113] The amount of biomass being processed by the agricultural harvester 100 can also affect tailings characteristics. When the agricultural harvester 100 is processing more biomass at a given time, it can result in greater volumes of tailings and the composition of the tailings can also be affected. In areas of heavy crop and thus increased levels of biomass, the likelihood of there being more unthreshed grain in the tailings can increase if the machine settings on the agricultural harvester 100 are not adjusted to account for the increased biomass. In areas of light crop and thus decreased levels of biomass, it can result in increased chaffer load, leading to increased tailings, unless the machine settings on the agricultural harvester 100 are adjusted to account for the decreased biomass. Thus, the biomass-to-tailings characteristics model generator 388 can generate a model that models the relationship between biomass characteristic values on the biomass map 340 and tailings characteristics sensed by the tailings characteristics sensor 344.

[0114] Biomass can also affect grain quality. Higher levels of biomass can affect threshing and cleaning, meaning that there can be more unthreshed grain. Thus, more grain that is not sufficiently cleaned can make its way into the clean grain tank. Thus, the biomass-to-grain quality model generator 390 can generate a model that models the relationship between biomass characteristics on the biomass map 340 and grain quality characteristics sensed by the grain quality sensor 348.

[0115] Biomass can also be related to grain loss. For example, higher levels of biomass typically mean increased MOG in the agricultural harvester 100, which can result in increased grain loss. Thus, the biomass-to-loss model generator 392 can generate a model that models the relationship between biomass characteristics on the biomass map 340 and loss characteristics sensed by the loss sensor 346.

[0116] The biomass level can also be related to the internal distribution of material within the agricultural harvester 100. For example, variations in the biomass level processed by the harvester 100 can result in uneven levels of material distribution in the agricultural harvester 100, such that the material level can be variable at each location within the agricultural harvester, for example, with an increase in material at one location and a decrease in material at another location due to variations in the biomass processed by the agricultural harvester 100. Accordingly, the biomass to internal distribution model generator 394 can generate a model that models the relationship between the biomass characteristics on the biomass map 340 and the internal distribution characteristics sensed by the internal distribution sensors 350.

[0117] The tailings characteristics can be strongly influenced by the weed characteristics, such as the amount of weeds (e.g., weed intensity) that are brought into the agricultural harvester 100. Weed material is generally tougher and greener than crop material and, as a result, is more likely to reach the tailings system, which can result in high tailings volumes and clogging in the agricultural harvester 100. Accordingly, the weed characteristics to tailings characteristics model generator 396 can generate a model that models the relationship between the weed characteristics on the weed map 342 and the tailings characteristics sensed by the tailings characteristics sensors 344. Further, when the agricultural harvester 100 spends more time in areas with relatively higher weed intensity, relative to other areas of the field, this can result in an increase in the tailings level. Accordingly, the model generator 396 can generate a model that models the relationship between the tailings change rate and the size of the locations in the field with relatively higher weed intensity.

[0118] The weed characteristics (e.g., weed intensity or weed type) can also be related to the grain quality. For example, higher weed intensity levels can result in higher MOG levels in the cleaning chamber, which increases the amount of MOG that is delivered to the clean grain tank. Accordingly, the weed characteristics to grain quality model generator 398 can generate a model that models the relationship between the weed characteristics on the weed map 342 and the grain quality characteristics sensed by the grain quality sensors 348.

[0119] The weed characteristics (e.g., weed intensity and weed type) can also be related to the loss. For example, higher weed intensity can result in heavier MOG levels, which can increase the grain loss. Accordingly, the weed characteristics to loss model generator 400 can generate a model that models the relationship between the weed characteristics on the weed map 342 and the loss characteristics sensed by the loss sensors 346.

[0120] The weed characteristics (e.g., weed intensity or weed type) can also be related to the internal distribution of material within the agricultural harvester 100. Thus, the weed characteristics to internal distribution model generator 402 can generate a model that models the relationship between the weed characteristics values on the weed map 342 and the internal distribution characteristics sensed by the internal distribution sensor 350.

[0121] Referring again to Figure 4A , the prediction map generator 212 can include one or more of a tailings characteristics map generator 410, a loss map generator 412, a grain quality map generator 414, and an internal distribution map generator 416. Multiple examples of different combinations of the field sensors 208 and the information maps 259 will be described below.

[0122] The present chronology is directed to an example in which the field sensors 208 are internal distribution sensors 350 that sense the internal material distribution in the agricultural harvester 100. It should be understood that this is merely one example, and other examples of the sensors mentioned above as field sensors 208 are considered herein, as are other information maps 259. The prediction model generator 210 (shown in greater detail in Figure 4B ) identifies relationships between the material distribution detected at a geographic location corresponding to the location at which the sensor data was obtained (e.g., the material distribution in the agricultural harvester 100 that can be identified based on the sensor signals from the internal distribution sensors 350) in the processed data 354 and the characteristics from one or more of the information maps 259 that correspond to the same location in the field at which the material distribution was detected in the processed data 354. Based on the relationships established by the prediction model generator 210, the prediction model generator 210 generates a prediction model 408. The internal distribution map generator 416 uses the prediction model 408 to predict the material distribution in the agricultural harvester 100 at a same location in the field based on the terrain characteristics contained in the information maps 259 that are geographically referenced at different locations in the field.

[0123] The present chronology is directed to an example in which the machine sensors 208 are grain loss sensors 346. It should be understood that this is merely one example, and other examples of the sensors mentioned above as field sensors 208, as well as other information maps 259 are considered herein. The prediction model generator 210 (shown in greater detail in Figure 4B(As shown in more detail below) Identifies the relationship between grain loss detected at geographic locations corresponding to the geolocation of sensor data in processed data 354 and characteristics from Infographic 259 corresponding to the same geolocated location in the field where grain loss is located. Based on this relationship established by prediction model generator 210, prediction model generator 210 generates prediction model 408. Loss map generator 412 uses prediction model 408 to predict grain loss at the same location in the field based on georeferenced characteristics contained in Infographic 259 at different locations in the field.

[0124] This discussion is based on the example where the field sensor 208 is the tail material characteristic sensor 344. It should be understood that this is merely an example, and other examples of field sensors, such as sensor 336 and other information in Figure 259, are also considered herein. Predictive model generator 210 (in...) Figure 4B (As shown in more detail below) Identifies the relationship between tailings characteristics detected at geographic locations corresponding to the locations where sensor data is geolocated in processed data 354 and characteristics from Infographic 259 corresponding to the same geographic location in the field where the tailings characteristics are geolocated. Based on this relationship established by prediction model generator 210, prediction model generator 210 generates prediction model 408. Tailings characteristic map generator 410 uses prediction model 408 to predict tailings characteristics at the same location in the field based on georeferenced characteristics contained at different locations in the field in Infographic 259.

[0125] This discussion is based on the example where the field sensor 208 is a grain quality sensor 348. It should be understood that this is merely an example, and other examples of field sensors, such as sensor 208, as well as other information in Figure 259, are also considered herein. Predictive model generator 210 (in...) Figure 4B (As shown in more detail below) Identifies the relationship between the grain quality detected at a geographic location corresponding to the location where the sensor data is geolocated in the processed data 354 and the characteristics from Infographic 259 corresponding to the same geographic location in the field where the grain quality is geolocated. Based on this relationship established by the prediction model generator 210, the prediction model generator 210 generates a prediction model 408. The grain quality map generator 414 uses the prediction model 408 to predict the grain quality at the same location in the field based on the georeferenced characteristics contained at different locations in the field in Infographic 259.

[0126] Predictive model generator 210 is operable to generate multiple predictive models, such as those derived from... Figure 4BThe model generator shown generates one or more predictive models. In another example, two or more of the above predictive models can be combined into a single predictive model that predicts two or more characteristics, such as internal material distribution, tailings characteristics, losses, and grain quality, based on characteristics from one or more of the data from different locations in the field in Infographic 259. Any one or a combination of these machine models is generated by... Figure 4A The machine model 408 in the model is represented together.

[0127] The prediction machine model 408 is provided to the prediction graph generator 212. Figure 4A In one example, the prediction graph generator 212 includes an internal distribution graph generator 416, a loss graph generator 412, a tailings characteristic graph generator 410, and a grain quality graph generator 414. In other examples, the prediction graph generator 212 may include additional, fewer, or different graph generators. Therefore, in some examples, the prediction graph generator 212 may include additional items 417, which may include other types of graph generators to generate graphs for other types of characteristics.

[0128] The tailings characteristic map generator 410 exemplarily generates a predicted tailings map 418 based on the characteristics at different locations in the field in the information map 259 and the prediction model 408, the predicted tailings map predicting the tailings characteristics at the different locations in the field.

[0129] Loss map generator 412 exemplarily generates a predicted loss map 420 based on the characteristics at different locations in the field in Infographic 259 and prediction model 408, the predicted loss map predicting grain loss at the different locations in the field.

[0130] The grain quality map generator 414 exemplarily generates a predicted grain quality map 422 based on the characteristics of different locations in the field in the information map 259 and the prediction model 408, the predicted grain quality map predicting the characteristics of grain quality at the different locations in the field.

[0131] The internal distribution map generator 416 exemplarily generates a predicted internal distribution map 422 based on the characteristics at different locations in the field in the information map 259 and the prediction model 408, the predicted internal distribution map predicting the material distribution at the different locations in the field.

[0132] The prediction map generator 212 outputs one or more of the functional prediction maps 418, 420, 422, and 424 of the predicted characteristics. Each of the functional prediction maps 418, 420, 422, and 424 is a functional prediction map of a respective characteristic at different locations in the field. Each of the functional prediction maps 418, 420, 422, and 444 can be provided to the control region generator 213, the control system 214, or both. The control region generator 213 generates control regions and merges the control regions into the functional prediction maps 418, 420, 422, and 424. Any or all of the functional prediction maps 418, 420, 422, or 424 and the corresponding functional prediction maps 418, 420, 422, or 424 with control regions can be provided to the control system 214, which generates control signals based on one or all of the functional prediction maps to control one or more of the controllable subsystems 216. Any or all of the functional prediction maps 418, 420, 422, or 424 (with or without control regions) can be presented to the operator 260 or another user.

[0133] Figure 5 is a flowchart of an example of the operations of the prediction model generator 210 and the prediction map generator 212 in generating the prediction machine model 408 and the prediction characteristic maps 418, 420, 422, and 424, respectively. At block 430, the prediction model generator 210 and the prediction map generator 212 receive the information map 259, which can be one or more of the information maps shown in FIGS. 2A-2C. At block 432, the processing system 352 receives one or more sensor signals from the field sensors 208. As described above, the field sensors 208 can be the tailings characteristic sensor 344, the loss sensor 346, the grain quality sensor 348, or the internal distribution sensor 350. Figure 4A

[0134] At block 434, the processing system 352 processes the one or more received sensor signals to generate data indicative of a characteristic. In some cases, as shown in block 436, the sensor data can be indicative of a tailings characteristic. In some cases, as shown in block 438, the sensor data can be indicative of grain loss. In some cases, as shown in block 440, the sensor data can be indicative of grain quality. In some cases, as shown in block 442, the sensor data can be indicative of an internal material distribution within the agricultural harvester 100.

[0135] ​At block 444, the predictive model generator 210 also obtains a geographic location 334 corresponding to the sensor data. For example, the predictive model generator 210 can obtain a geographic location from the geographic location sensor 204 and determine the precise geographic location at which the sensor data was captured or derived based on machine delays, machine speed, etc. Further, at block 444, a bearing of the agricultural harvester 100 on the field can be determined. The bearing of the agricultural harvester 100 can be obtained, for example, to represent the bearing of the agricultural harvester 100 relative to a slope on the field.

[0136] At block 446, the predictive model generator 210 generates one or more predictive models, such as machine models 408, that model relationships between one or more characteristics on the information map 259 and characteristics or related characteristics sensed by the field sensors 208.

[0137] At block 448, the predictive models, such as the predictive model 408, are provided to the predictive map generator 212 and the predictive map generator 212 generates a functional predictive map that maps the predicted characteristics based on the data referenced in the information map 259 and the predictive model 408. In some examples, the functional predictive map is a predicted tailings characteristic map 418. In some examples, the functional predictive map is a predicted loss map 420. In some examples, the functional predictive map is a predicted grain quality map 422. In some examples, the functional predictive map is a predicted internal distribution map 424.

[0138] The functional predictive map can be generated during the course of the agricultural operation. Thus, as the agricultural harvester moves through the field performing the agricultural operation, the functional predictive map is generated as the agricultural operation is performed.

[0139] At block 450, the predictive map generator 212 outputs the functional predictive map. At block 452, the predictive map generator 212 outputs the functional predictive map for presentation to and possible interaction with the operator 260. At block 454, the predictive map generator 212 can configure the functional predictive map for use by the control system 214. At block 456, the predictive map generator 212 can also provide the functional predictive map to the control zone generator 213 for generating control zones. At block 428, the predictive map generator 212 also otherwise configures the functional predictive map. The functional predictive map, with or without control zones, is provided to the control system 214. At block 460, the control system 214 generates control signals based on the functional predictive map to control the controllable subsystems 216.

[0140] The control system 214 can generate control signals to control one or more of the speed of the sieve 124 and the chaffer 122 and the size of the openings therein, the speed of the cleaning fan 120 and the rotor 112, the rotor pressure driving the rotor 112, and the gap between the rotor 112 and the concave 114 or other.

[0141] The control system 214 can generate control signals to control the cutting table or other machine actuators 248. The control system 214 can generate control signals to control the propulsion subsystem 250. The control system 214 can generate control signals to control the steering subsystem 252. The control system 214 can generate control signals to control the residue subsystem 138. The control system 214 can generate control signals to control the machine cleaning subsystem 254. The control system 214 can generate control signals to control the threshing machine 110. The control system 214 can generate control signals to control the material handling subsystem 125. The control system 214 can generate control signals to control the crop cleaning subsystem 118. The control system 214 can generate control signals to control the communication system 206. The control system 214 can generate control signals to control the operator interface mechanism 218. The control system 214 can generate control signals to control various other controllable subsystems 256.

[0142] In examples in which the control system 214 receives a function prediction map or a function prediction map augmented with control regions, the header / whip control 238 controls the header or other machine actuator 248 to control the height, pitch, or roll of the header 102. In examples in which the control system 214 receives a function prediction map or a function prediction map augmented with control regions, the feed rate controller 236 controls the propulsion subsystem 250 to control the travel speed of the agricultural harvester 100. In examples in which the control system 214 receives a function prediction map or a function prediction map augmented with control regions, the path planning controller 234 controls the steering subsystem 252 to steer the agricultural harvester 100. In another example in which the control system 214 receives a function prediction map or a function prediction map augmented with control regions, the residue system controller 244 controls the residue subsystem 138. In another example in which the control system 214 receives a function prediction map or a function prediction map augmented with control regions, 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 or a function prediction map augmented with control regions, 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 or a function prediction map augmented with control regions, the setting controller 232 controls the crop cleaning subsystem 118. In another example in which the control system 214 receives a function prediction map or a function prediction map augmented with control regions, the machine cleaning controller 245 controls the machine cleaning subsystem 254 on the agricultural harvester 100. In another example in which the control system 214 receives a function prediction map or a function prediction map augmented with control regions, the communication system controller 229 controls the communication system 206. In another example in which the control system 214 receives a function prediction map or a function prediction map augmented with control regions, the operator interface controller 231 controls the operator interface mechanism 218 on the agricultural harvester 100. In another example in which the control system 214 receives a function prediction map or a function prediction map augmented with control regions, the deck position controller 242 controls the machine / header actuator 248 to control the deck on the agricultural harvester 100. In another example in which the control system 214 receives a function prediction map or a function prediction map augmented with control regions, the conveyor belt controller 240 controls the machine / header actuator 248 to control the conveyor belt on the agricultural harvester 100. In another example in which the control system 214 receives a function prediction map or a function prediction map augmented with control regions, the other controller 246 controls other controllable subsystems 256 on the agricultural harvester 100.

[0143] In some examples, the control system 214 receives the function prediction map or the function prediction map with control regions added and generates control signals for one or more of the controllable subsystems 216 to control or compensate for the internal material distribution within the agricultural harvester 100. For example, the control system 214 can generate one or more control signals based on the received function prediction map (with or without control regions) to control the material handling subsystem 125 to control or compensate for the internal material distribution within the agricultural harvester 100. For example, the control system 214 can generate one or more control signals based on values in the function prediction map (with or without control regions) to control a setting or operating characteristic of a component of the material handling subsystem 125, such as to control the tailings accelerator 108, to control the threshing machine 110 (e.g., to control a speed of the threshing rotor 112, a concave gap (a spacing between the threshing rotor 112 and the concave 114), to control the separator 116, to control the unloading beater 126, to control the tailings elevator 128, to control the clean grain elevator 130, to control the unloading auger 134, or to control the spout 136. In another example, the control system 214 can generate one or more control signals based on the received function prediction map (with or without control regions) to control the cleaning subsystem 118 to control or compensate for the internal material distribution within the agricultural harvester. For example, the control system 214 can generate one or more control signals based on values in the function prediction map (with or without control regions) to control a setting or operating characteristic of a component of the cleaning subsystem 118, such as to control the cleaning fan 120 (e.g., to increase or decrease a speed of the cleaning fan 120), to control the chaffer 122 (e.g., to control a chaffer gap (to control a size of openings in the chaffer 122)), or to control the sieve 124 (e.g., to control a size of openings in the sieve 124). In another example, the control system 214 can generate one or more control signals based on the received function prediction map (with or without control regions) to control the residue subsystem 138 to control or compensate for the internal material distribution within the agricultural harvester 100. For example, the control system 214 can generate one or more control signals based on values in the function prediction map (with or without control regions) to control a setting or operating characteristic of a component of the residue subsystem 138, such as to control the chopper 140 or to control the spreader 142.

[0144] In some examples, the control system 214 can generate one or more control signals to control a setting (e.g., position, orientation, etc.) of an adjustable material engagement element disposed within a material flow path within the agricultural harvester to control or compensate for internal material distribution within the agricultural harvester 100. For example, the one or more control signals can control an actuator to actuate movement of the adjustable material engagement element to change a position or orientation of the adjustable material engagement element to direct at least a portion of the material flow right or left relative to a flow direction. In some examples, the direction can be laterally or fore-aft relative to the material flow direction from an area of greater material depth to an area of lesser material depth.

[0145] Thus, it can be seen that the present system employs one or more information maps that map characteristics to different locations in a field. The present system also uses one or more field sensors that sense field sensor data indicative of characteristics and generates a model that models a relationship between characteristics sensed using the field sensors or related characteristics and the characteristics 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 or presented to a local or remote operator or other user. For example, the control system can use the map to control one or more systems of the agricultural harvester.

[0146] Figure 6 A 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.

[0147] 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 region generator 213 can generate control regions corresponding to each individually controllable WMA, or to groups of WMAs that are controlled in coordination with one another.

[0148] The WMA selector 486 selects a WMA or group of WMAs for which a corresponding control region is to be generated. The control region generation system 488 then generates a control region for the selected WMA or group of WMAs. Different criteria can be used in identifying control regions 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 region. In another example, wear characteristics (e.g., how much a particular actuator or mechanism wears due to its motion) can be used as a criterion for identifying the boundaries of a control region. The control region criteria identifier component 494 identifies the particular criteria that will be used to define control regions for the selected WMA or group of WMAs. The control region boundary definition component 496 processes values on the function prediction map in the analysis to define the boundaries of control regions on the function prediction map in the analysis based on the values on the function prediction map in the analysis and based on the control region criteria for the selected WMA or group of WMAs.

[0149] The target setting identifier component 498 sets a value for a target setting that will be used to control the WMA or group of WMAs in different control regions. 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 region can be a target speed setting based on the speed values contained in the function prediction speed map 238 within the identified control region.

[0150] 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. Accordingly, 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 in order 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 in identifying the control zone and the target setting for the selected WMA in the control zone. For example, different target settings for controlling the speed of the machine can be generated based on, for example, a detected or predicted feed rate value, a detected or predicted fuel efficiency value, a detected or predicted grain loss value, 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. Accordingly, one of the competing target settings is selected to control the speed of the agricultural harvester 100.

[0151] Accordingly, 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 a selected WMA or group of WMAs on the function prediction map under analysis. Some criteria that can be used to identify or define a dynamic zone include, for example, crop type or crop variety based on a planting map or another source of crop type or crop variety, weed type, weed intensity, crop condition such as whether the crop is lodged, partially lodged, or standing, yield, biomass, vegetation index, or terrain. These are merely some examples of criteria that can be used to identify or define a dynamic zone. Just as each WMA or group of WMAs can have a corresponding control zone, different WMAs or groups of WMAs can have corresponding dynamic zones. The dynamic zone boundary definition component 524 identifies the boundaries of the dynamic zone on the function prediction map under analysis based on the dynamic zone criteria identified by the dynamic zone criteria identification component 522.

[0152] In some examples, dynamic zones can overlap one another. For example, a crop variety dynamic zone can overlap some or all of a crop condition 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 takes precedence over the dynamic zone with a lower position or importance in the priority hierarchy. The priority hierarchy of 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 variety dynamic zone, the lodged crop dynamic zone can be assigned greater importance in the priority hierarchy than the crop variety dynamic zone such that the lodged crop dynamic zone takes precedence.

[0153] 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 function prediction map under analysis and identifies the particular settings resolver for the selected WMA or group of WMAs.

[0154] 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:

[0155] 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 feed rate rather than other competing target setting values is used, otherwise the target setting value based on grain loss rather than other competing target setting values is used.

[0156] 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 computed 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.

[0157] Figure 7 is a flowchart illustrating one example of the operation of control region generator 213 in generating control regions and dynamic regions for a graph received by control region generator 213 for zone processing (e.g., a graph in analysis).

[0158] At block 530, control region generator 213 receives a graph in analysis for processing. In one example, as shown in block 532, the graph in analysis is a function prediction graph. For example, the graph in analysis can be one of function prediction graphs 436, 437, 438, or 440. Block 534 indicates that the graph in analysis can also be other graphs.

[0159] At block 536, the WMA selector 486 selects a WMA or group of WMAs for which to generate a control zone 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 an example in which the control zone criteria are or include wear characteristics of the selected WMA or group of WMAs. Block 542 indicates an example in which the control zone definition criteria are or include magnitudes and variations of input source data, such as magnitudes and variations of values on the map in the analysis or magnitudes and variations of inputs from various field sensors 208. Block 544 indicates an example in which the control zone definition criteria are or include physical machine characteristics, such as physical dimensions of the machine, speeds of different subsystem operations, or other physical machine characteristics. Block 546 indicates an example in which the control zone definition criteria are or include responsiveness of the selected WMA or group of WMAs in reaching set values of new commands. Block 548 indicates an example in which the control zone definition criteria are or include machine performance metrics. Block 550 indicates an example in which the control zone definition criteria are or include operator preferences. Block 552 indicates an example in which the control zone definition criteria are also or include other items. Block 549 indicates 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 the 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 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.

[0160] At block 554, the dynamic zone criteria identification component 522 obtains the dynamic zone definition criteria for the selected WMA or WMA group. Block 556 indicates an example in which the dynamic zone definition criteria are based on manual input from the operator 260 or another user. Block 558 shows an example in which the dynamic zone definition criteria are based on crop type or crop variety. Block 560 shows an example in which the dynamic zone definition criteria are based on weed characteristics (e.g., weed type or weed intensity or both). Block 561 shows an example in which the dynamic zone definition criteria are based on or include terrain. Block 562 shows an example in which the dynamic zone definition criteria are based on or include crop status. Block 564 indicates an example in which the dynamic zone definition criteria are also or include other criteria.

[0161] At block 566, the control zone boundary definition component 496 generates the boundaries of the control zones on the map under analysis based on the control zone criteria. The dynamic zone boundary definition component 524 generates the boundaries of the dynamic zones on the map under analysis based on the dynamic zone criteria. Block 568 indicates an example in which the zone boundaries are identified for both the control zones and the dynamic zones. Block 570 shows the target setting identifier component 498 identifying the target setting for each of the control zones. The control zones and dynamic zones can also be generated in other ways, and this is indicated by block 572.

[0162] 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 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.

[0163] At block 588, the WMA selector 486 determines whether there are more WMAs or WMA groups to process. If there are additional WMAs or WMA groups to process, the processing returns to block 436 in which the next WMA or WMA group for which to define control zones and dynamic zones is selected. When there are no additional WMAs or WMA groups left for which to generate control zones or dynamic zones, the processing moves to block 590 in which the control zone generator 213 outputs a map for each of the WMAs or WMA groups with the 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.

[0164] 8One example of the operation of the control system 214 in controlling the agricultural harvester 100 based on a map output by the control zone generator 213 is shown. Thus, at block 592, the control system 214 receives a map of the work site. In some cases, the map can be a function prediction map that can include control zones and dynamic zones (as indicated at block 594). In some cases, the received map can be a function prediction map that excludes control zones and dynamic zones. Block 596 indicates examples in which the received map of the work site can be a priori 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.

[0165] At block 612, the control system 214 receives sensor signals from the geo-location sensor 204. The sensor signals 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 signals. 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 shows examples in which one or more of the target settings for the selected WMA or group of WMAs are based on input from the control zones on the map of the work site. Block 632 shows examples in which one or more of the target settings are obtained from manual input by the operator 260 or another user. Block 634 shows examples in which the target settings are obtained from the field sensors 208. Block 636 shows examples in which one or more of the target settings are obtained from one or more sensors on other machines simultaneously working in the same field as the agricultural harvester 100 or from one or more sensors on machines that have worked in the same field in the past. Block 638 shows examples in which the target settings are also obtained from other sources. Figure 1

[0166] ​At block 640, the zone controller 247 accesses the setting resolver for the selected dynamic zone and controls the setting resolver to resolve the competitive target setting into a resolved target setting. 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 competitive target setting 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 competitive target setting 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 setting based on predicted quality metrics or historical quality metrics, based on threshold rules, or in the case of using a logic component.

[0167] At block 642, in the case that the zone controller 247 has identified a resolved target setting, the zone controller 247 provides the resolved target setting to other controllers in the control system 214, which generate control signals based on the resolved target setting and apply the control signals to the selected WMA or WMA group. For example, in the case that the selected WMA is a machine or header actuator 248, the zone controller 247 provides the resolved target setting to the setting controller 232 or the header / actual controller 238 or both to generate control signals based on the resolved target setting, 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 626-644 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.

[0168] 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.

[0169] Figure 9 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. Figure 9

[0170] ​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.

[0171] 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 indicative of 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 accomplish the verbal set change command.

[0172] The voice processing system 658 can be invoked in a variety of different ways. For example, in one example, the voice handling system 662 provides input from a microphone (as one of the operator interface mechanisms 218) continuously 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.

[0173] 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 any of 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 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.

[0174] In some examples, the speech processing system 658 can also generate output that navigates the operator 260 through the user experience 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 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 through 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.

[0175] 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 through the operator interface mechanisms. The haptic elements can also include a wide variety of other haptic elements.

[0176] Figure 10 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 10 One example of how the operator interface controller 231 can detect and process operator interaction with a touch-sensitive display screen is also shown.

[0177] 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 that identifies 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.

[0178] 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 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 predicted loss map such as the function prediction loss map 420, the displayed field can show different loss level categories 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.

[0179] Figure 11 is a diagram showing one example of a user interface display 720 that can be generated on a touch-sensitive display screen. In other implementations, 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 10 the user interface display 720 will be described.

[0180] In Figure 11In 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.

[0181] In Figure 11 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 portions 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 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 portions 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

[0182] The size of the next work unit 730 marked 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. In another example, the size of the next work unit 730 can vary based on the size of the agricultural harvester 100, including equipment on the agricultural harvester 100 (e.g., the header 102). For example, the width of the next work unit 730 can vary based on the width of the header 102. 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 11In the example shown, the plot being displayed is a predicted loss plot, such as a functional predicted loss plot 420. Therefore, multiple different loss level markers are displayed on the field display section 728. A set of loss level display markers 732 is shown in the already visited area 714. Another set of loss level display markers 732 is shown in the upcoming area 712, and a further set of loss level display markers 732 is shown in the next work unit 730. Figure 11 The loss level display markers 732 are shown to consist of different symbols indicating areas of similar loss levels. In the example shown in Figure 3, the ! symbol represents an area of ​​high loss level; the * symbol represents an area of ​​medium loss level; and the # symbol represents an area of ​​low loss level. Therefore, the field display section 728 displays different measured or predicted values ​​(or characteristics represented by said values) located in different areas within the field, and utilizes various display markers 732 to represent these measured or predicted values ​​(or characteristics indicated by said values). As shown, the field display section 728 includes display markers located at specific locations associated with specific locations on the field being displayed, in particular... Figure 11 The illustrative example shows a loss level display marker 732. In some cases, each location of the field may have an associated display marker. Therefore, in some cases, a display marker can be set at each location of the field display section 728 to identify the nature of the property mapped for each particular location of the field. Thus, this disclosure includes providing display markers, such as loss level display marker 732 (as shown in the illustrative example), at one or more locations on the field display section 728. Figure 11 (In the context of this example) to identify the nature, degree, etc., of the characteristic being displayed, thereby identifying the characteristic at the corresponding location in the field being displayed. As previously described, the display mark 732 can consist 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.

[0183] In other examples, the graph being displayed can be one or more of the graphs described herein, including infographics, prior infographics, functional prediction graphs (e.g., prediction graphs or prediction control area graphs), or combinations thereof. Therefore, the labels and features being displayed will be related to the information, data, features, and values ​​provided by the one or more graphs being displayed.

[0184] exist Figure 11 In the example, the user interface display 720 also has a control display section 738. The control display section 738 allows the operator to view information and interact with the user interface display 720 in various ways.

[0185] The actuators and display elements in part 738 can be displayed as, for example, individual items, fixed lists, scrollable lists, drop-down menus, or drop-down lists. Figure 11 In the example shown, display section 738 displays information corresponding to three different loss levels for the three symbols mentioned above. Display section 738 also includes a set of touch-sensitive actuators that operator 260 can interact with by touch. For example, operator 260 can touch a touch-sensitive actuator with a finger to activate the corresponding actuator.

[0186] like Figure 11 As shown, display portion 738 includes an interactive sign display portion, generally indicated by reference numeral 741. The interactive sign display portion 741 includes a sign column 739 that lists signs that have been set automatically or manually. Sign actuator 740 allows operator 260 to mark locations, such as the current location of the harvester, or other locations on the field specified by the operator, and then add information indicating the level of loss found at the current location. For example, when operator 260 actuates sign actuator 740 by touching it, touch gesture processing system 664 in operator interface controller 231 identifies the current location as a location where the harvester 100 has encountered a high level of loss. When operator 260 touches button 742, touch gesture processing system 664 identifies the current location as a location where the harvester 100 has encountered a medium level of loss. When operator 260 touches button 744, touch gesture processing system 664 identifies the current location as a location where the harvester 100 has encountered a low level of loss. When one of the actuation sign actuators 740, 742, or 744 is activated, the touch gesture processing system 664 can control the visual control signal generator 684 to add a symbol corresponding to the identified loss level on the field display portion 728 at the user-identified location. In this way, areas of field where predicted values ​​do not accurately represent actual values ​​can be marked for later analysis and can also be used for machine learning. In other examples, the operator can specify an area in front of or around the harvester 100 by actuating one of the actuation sign actuators 740, 742, or 744, allowing control of the harvester 100 based on values ​​specified by the operator 260.

[0187] Display portion 738 also includes an interactive marker display portion, generally indicated by reference numeral 743. The interactive marker display portion 743 includes a symbol column 746 that displays the value or characteristic of each category tracked on the field display portion 728 (in...). Figure 11corresponding symbol of one. The display portion 738 also includes an interactive specifier display portion, generally indicated by reference numeral 745. The interactor specifier display portion 745 includes a specifier column 748 that displays a specifier (which can be a text specifier or other specifier) that identifies a category of values (in this case, loss levels) that the symbols in the symbol column 746 correspond to. Without limitation, the symbols in the symbol column 746 and the specifiers in the specifier column 748 can include any display feature, such as different colors, shapes, patterns, intensities, text, icons, or other display features, and can be customizable through interaction by the operator of the agricultural harvester 100. Figure 11

[0188] The display portion 738 also includes an interactive value display portion, generally indicated by reference numeral 747. The interactive value display portion 747 includes a value display column 750 that displays a selected value. The selected value corresponds to the characteristic or value that is being tracked or displayed on the field display portion 728, or both. The selected value can be selected by the operator of the agricultural harvester 100. The selected value in the value display column 750 defines the value that range values, or other values (e.g., predicted values), will be categorized by. Thus, in the example shown in FIG. 7, the selected value is 1.5 bushels per acre, which is the value that predicted or measured loss levels will be categorized by. Figure 11 Figure 11

[0189] ​​​The display portion 738 also includes an interactive threshold display portion, generally indicated by reference numeral 749. The interactive threshold display portion 749 includes a threshold display column 752 that displays action thresholds. The action thresholds in column 752 can be thresholds that correspond to the selected values in the value display column 750. If the predicted or measured values of the tracked or displayed or tracked and displayed characteristics meet the corresponding action threshold in the threshold display column 752, the control system 214 takes the action identified in column 754. In some cases, the predicted or measured values can meet the corresponding action threshold by meeting or exceeding the corresponding action threshold. In one example, the operator 260 can select a threshold, for example, to change the threshold by touching a threshold in the threshold display column 752. Once selected, the operator 260 can change the threshold. The thresholds in column 752 can be configured such that the specified action is performed when the measured or predicted values of the characteristic exceed the threshold, equal the threshold, or are less than the threshold. In some cases, the threshold can represent a range of values, or a range of deviation from the selected values in the value display column 750, such that predicted or measured characteristic values that meet or fall within the range meet the threshold. For example, in the example of Figure 10 1.5 bushels per acre, predicted values that fall within 10% of 1.5 bushels per acre will meet the corresponding action threshold (within 10% of 1.5 bushels per acre), and an action such as reducing the cleanout fan speed will be taken by the control system 214. In other examples, the thresholds in column threshold display column 752 are separate from the selected values in the value display column 750, such that the values in the value display column 750 define the classification and display of the predicted or measured values, and the action thresholds define when an action is taken based on the measured or predicted values. For example, while a predicted or measured loss value of 1.0 bushels per acre can be designated as a “moderate loss level” for classification and display purposes, the action threshold can be 1.2 bushels per acre, such that an action is not taken until the loss value meets the threshold. In other examples, the thresholds in the threshold display column 752 can include a distance or a time. For example, in a distance example, the threshold can be a threshold distance from an area to which the measured or predicted values are georeferenced in the field that the agricultural harvester 100 must be located within before an action is taken. For example, a threshold distance value of 10 feet would mean that an action would be taken when the agricultural harvester is located 10 feet at or within 10 feet of an area to which the measured or predicted values are georeferenced in the field. In examples where the threshold is a time, the threshold can be a threshold time for the agricultural harvester 100 to reach an area to which the measured or predicted values are georeferenced in the field. For example, a threshold of 5 seconds would mean that an action would be taken when the agricultural harvester 100 is 5 seconds from an area to which the measured or predicted values are georeferenced in the field. In such examples, the current location and travel speed of the agricultural harvester can be considered.

[0190] The display portion 738 also includes an interactive action display portion, generally indicated by reference numeral 751. The interactive action display portion 751 includes an action display column 754 that displays action identifiers that indicate actions to be taken when a predicted value or a measured value satisfies an action threshold in the threshold display column 752. The operator 260 can touch an action identifier in the column 754 to change the action to be taken. The action can be taken when the threshold is satisfied. For example, at the bottom of the column 754, an increase cleaning fan speed action and a decrease cleaning fan speed action are identified as actions to be taken if the measured value or the predicted value satisfies a threshold in the column 752. In some examples, multiple actions can be taken when the threshold is satisfied. For example, the cleaning fan speed can be adjusted, the threshing rotor speed can be adjusted, and the concave gap can be adjusted in response to the threshold being satisfied.

[0191] The actions that can be set in the column 754 can be any of a variety of different types of actions. For example, the actions can include a disable action that, when executed, prevents the agricultural harvester 100 from further harvesting in the field. The actions can include a speed change action that, when executed, changes the speed at which the agricultural harvester 100 travels through the field. The actions can include a setting change action to change a setting of an internal actuator or another WMA or group of WMAs, or a setting change action to implement a change in threshing rotor speed, cleaning fan speed, position of the header (e.g., tilt, pitch, roll, etc.), and various other settings. These are merely examples, and a wide variety of other actions are contemplated herein.

[0192] The items 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, a display element can be controlled to modify the intensity, color, or pattern of the display element that is displayed. Additionally, the display element can be controlled to flash. As an example, the described changes to the visual appearance of the display element are provided. Thus, other aspects of the visual appearance of the display element can be changed. Thus, the display element can be modified in a desired manner in various situations in order to, for example, capture the attention of the operator 260. Moreover, while a particular number of items are shown on the user interface display 720, this need not be the case. In other examples, more or fewer items can be included on the user interface display 720, including more or fewer particular items.

[0193] Now returning to Figure 10The flowchart of FIG. 7 continues the description of the operation of the operator interface controller 231. At block 760, the operator interface controller 231 detects an input that sets a flag and controls the touch-sensitive user interface display 720 to display the flag on the field display portion 728. The detected input can be an operator input (as indicated by 762) or an input from another controller (as indicated by 764). At block 766, the operator interface controller 231 detects a field sensor input that indicates a measured characteristic of the field from one of the field sensors 208. At block 768, the visual control signal generator 684 generates a control signal 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 of the actuators for setting or modifying the values in the columns 739, 746, and 748 can be displayed. Thus, the user can set the flags and modify the characteristics of the flags. For example, the user can modify the loss levels and the loss level designators corresponding to the flags. Block 772 indicates that the action threshold values in the column 752 are displayed. Block 776 indicates that the actions in the column 754 are displayed, and block 778 indicates that the selected values in the column 750 are displayed. Block 780 indicates that a variety of other information and actuators can also be displayed on the user interface display 720.

[0194] At block 782, the operator input command processing system 654 detects and processes operator input corresponding to the interaction of the user interface display 720 by the operator 260. In the case where the user interface mechanism on which the user interface display 720 is displayed is a touch-sensitive display screen, the interaction input by the operator 260 with the touch-sensitive display screen can be a touch gesture 784. In some cases, the operator interaction input can be input using a click device 786 or other operator interaction input 788.

[0195] At block 790, the operator interface controller 231 receives a signal indicating an alarm condition. For example, block 792 indicates that the signal can be received by the controller input handler 668, which indicates that a detected or predicted value meets a threshold condition present in column 752. As explained previously, the threshold condition can include a value below, at, or above a threshold value. Block 794 shows that the action signal generator 660 can generate a visual alarm by using the visual control signal generator 684, an audio alarm by using the audio control signal generator 686, a haptic alarm by using the haptic control signal generator 688, or by using any combination of these to alert the operator 260 in response to receiving the alarm condition. 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 handle the alarm condition in other ways.

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

[0200] Operator interface controller: “Current loss levels are high.”

[0201] Table 2 shows an example in which the speech synthesis component 676 provides outputs 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 value).

[0202] Table 2

[0203] Operator interface controller: “Loss levels were higher over the past 10 minutes.”

[0204] Operator interface controller: "Next 1 acre of land, loss level is medium."

[0205] The example shown in Table 3 shows that some of the 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 an action signal to automatically mark a loss level area in the field being harvested.

[0206] Table 3

[0207] Human: "Johnny, mark the high loss level area."

[0208] Operator interface controller: "High loss level area marked."

[0209] 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 a loss level area.

[0210] Table 4

[0211] Human: "Johnny, start marking the high loss level area."

[0212] Operator interface controller: "Marking high loss level area."

[0213] Human: "Johnny, stop marking the high loss level area."

[0214] Operator interface controller: "High loss level area marking stopped."

[0215] The example shown in Table 5 shows that the action signal generator 160 can generate signals to mark a low loss level area in a different manner than shown in Tables 3 and 4.

[0216] Table 5

[0217] Human: "Johnny, mark the next 100 feet as a low loss level area."

[0218] Operator interface controller: "Next 100 feet marked as low loss level area."

[0219] Returning again Figure 12, block 906 shows that the operator interface controller 231 can also detect and handle cases where a message or other information is to be output in other ways. 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, where the geographic location of the harvesting machine 100 is updated, and the process continues as described above to update the user interface display 720.

[0220] 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. Saving the desired values is indicated by block 916. These values can be saved locally on the agricultural harvesting machine 100, or the values can be saved at a remote server location or sent to another remote system.

[0221] As can be seen, an information map is obtained by an agricultural harvesting machine, the information map showing values of agricultural properties at different geographic locations of a field being harvested. On-board sensors on the harvesting machine sense properties as the agricultural harvesting machine moves through the field. A prediction map is generated by the prediction map generator based on the values in the information map and the properties sensed by the on-board sensors, the prediction map including control values for different locations in the field. A control system controls controllable subsystems based on the control values in the prediction map.

[0222] The control values are values that actions can be based on. As described herein, the control values can include any value (or a property indicated by or derived from the value) that can be used to control the agricultural harvesting machine 100. The control values can be any value indicative of an agricultural property. 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 an information map, values provided by a 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 properties 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.

[0223] 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 part, and functionally are a part of that system or device, and are activated by, and facilitate the functionality of, the other components or items in that system.

[0224] 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 mechanisms can be text boxes, check boxes, icons, links, drop-down menus, search boxes, etc. The user-actuatable operator interface mechanisms can also be actuated in a variety of different ways. For example, the user-actuatable operator interface mechanisms 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 mechanisms are displayed is a touch-sensitive screen, the user-actuatable operator interface mechanisms can be actuated using touch gestures. Also, the user-actuatable operator interface mechanisms can be actuated using voice commands that utilize speech recognition functionality. Speech 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.

[0225] 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 that accesses the data store, all of the data stores can be located remotely from the system that utilizes 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.

[0226] 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.

[0227] 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.

[0228] Figure 2 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 12 The software or components shown in

[0229] In the example shown in Figure 2 some items are similar to those shown in Figure 12 and these items are similarly numbered. Figure 12 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 12 the agricultural harvester 600 accesses the system through the remote server location 502.

[0230] Figure 12 Another example of a remote server architecture is also depicted. Figure 2 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 the stored data can be accessed or forwarded to an operator, user, or system. For example, physical carriers can be used in addition to or instead of electronic carriers. 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 the fuel truck 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.

[0231] It will also be noted that Figure 13 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, or the like.

[0232] 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 use of ledgers for recording metadata, data, data transfers, data access, and data transformations. In some examples, the ledgers can be distributed and immutable (e.g., implemented as blockchains).

[0233] Figures 14-15 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, a 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. Figure 13 is an example of a handheld or mobile device.

[0234] Figure 2 An overall block diagram of components of a client device 16 is provided that can run some of the components shown in Figure 14 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 the communications link 13 provides a channel for automatically receiving information (e.g., by scanning). 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.

[0235] In other examples, the application can be received on a removable Secure Digital (SD) card that is connected to an interface 15. The interface 15 and the communications link 13 are in communication with a processor 17 (which can also implement the processor or server from other figures) along a bus 19 that is also connected to a memory 21 and input / output (I / O) components 23, as well as a clock 25 and a location system 27.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] Figure 14 One example is shown in which the device 16 is a tablet computer 600. In Figure 15 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.

[0241] Figure 14 Similar 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. In general, the device 16 can be any sort of computing device that is capable of interacting with a user and / or other devices.

[0242] Note that other forms of the device 16 are possible.

[0243] Figure 2 is one example of a computing environment in which elements of Figure 16 may be deployed. Reference is made to Figure 2An 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. With Figure 16 The described memory and programs can be deployed in Figure 16 corresponding portions of the

[0244] 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.

[0245] System memory 830 includes computer storage media in the form of volatile and / or non-volatile memory, or both, such as read-only memory (ROM) 831 and random access memory (RAM) 832. The basic input / output system 833 (BIOS) (which contains basic routines such as those that help transfer information between components within computer 810 during startup) is typically stored in ROM 831. RAM 832 typically contains data and / or program modules, or both, that are readily accessible to and / or currently being operated by processing unit 820. This is by way of example and not limitation. Figure 16 The operating system 834, application program 835, other program modules 836, and program data 837 are shown.

[0246] Computer 810 may also include other removable / non-removable volatile / non-volatile computer storage media. This is just one example. Figure 16 A hard disk drive 841 is shown that reads from or writes to a non-removable, non-volatile magnetic medium, an optical disk drive 855, and a non-volatile optical disk 856. The hard disk drive 841 is typically connected to the system bus 821 via a non-removable memory interface (such as interface 840), and the optical disk drive 855 is typically connected to the system bus 821 via a removable memory interface (such as interface 850).

[0247] Alternatively or additionally, the functions described herein may be performed at least in part by one or more hardware logic components. For example, but not limited to, illustrative types of hardware logic components that may 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), and the like.

[0248] The above discussion and Figure 16 The driver and its associated computer storage medium shown provide the computer 810 with storage for computer-readable instructions, data structures, program modules, and other data. For example, in Figure 16In the diagram, hard disk drive 841 is shown storing operating system 844, application program 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 program 835, other program modules 836, and program data 837.

[0249] 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 keyboards, 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.

[0250] Computer 810 operates in a networked environment using a logical connection (such as a Controller Area Network (CAN), Local Area Network (LAN), or Home Area Network (WAN)) between one or more remote computers (such as remote computer 880).

[0251] 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, ​ This demonstrates that remote application 885 can reside on remote computer 880.

[0252] 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.

[0253] Example 1 is an agricultural operating machine, comprising:

[0254] A communication system that receives an information map, the information map including values ​​of a first agricultural characteristic corresponding to different geographical locations in the field;

[0255] A geolocation sensor that detects the geolocation of agricultural machinery;

[0256] a field sensor that detects a value of a second agricultural characteristic indicative of a characteristic of the processed material corresponding to the geographic location;

[0257] a predictive model generator that generates a predictive agricultural model based on the value of the first agricultural characteristic in the information map at the geographic location and the value of the second agricultural characteristic sensed by the field sensor at the geographic location, the predictive agricultural model modeling a relationship between the first agricultural characteristic and the second agricultural characteristic; and

[0258] a predictive map generator that generates a functional predictive agricultural map of the field based on the value of the first agricultural characteristic in the information map and based on the predictive agricultural model, the functional predictive agricultural map mapping predicted values of the second agricultural characteristic to different geographic locations in the field.

[0259] Example 2 is the agricultural work machine of any or all of the preceding examples and further comprising:

[0260] a control system that generates a control signal based on the functional predictive agricultural map to control a controllable subsystem on the agricultural work machine.

[0261] Example 3 is the agricultural work machine of any or all of the preceding examples, wherein the field sensor comprises:

[0262] a tail material characteristic sensor that senses a tail material characteristic in the agricultural work machine as the second agricultural characteristic.

[0263] Example 4 is the agricultural work machine of any or all of the preceding examples, wherein the field sensor comprises:

[0264] a loss sensor that senses a characteristic indicative of crop loss from the agricultural work machine as the second agricultural characteristic.

[0265] Example 5 is the agricultural work machine of any or all of the preceding examples, wherein the field sensor comprises:

[0266] a grain quality sensor that senses a characteristic indicative of grain quality in the agricultural work machine as the second agricultural characteristic.

[0267] Example 6 is the agricultural work machine of any or all of the preceding examples, wherein the field sensor comprises:

[0268] an internal distribution sensor that senses a characteristic indicative of a distribution of harvested material in the agricultural work machine as the second agricultural characteristic.

[0269] Example 7 is the agricultural work machine of any or all of the preceding examples, wherein the predictive map generator comprises:

[0270] a tail characteristics map generator that generates, based on the values of the first agricultural characteristic in the information map and based on the predictive agricultural model, a predictive tail characteristics map as the functional predictive agricultural map, the predictive tail characteristics map mapping predicted values of a tail characteristic as predicted values of a second agricultural characteristic to different geographic locations in the field.

[0271] Example 8 is the agricultural work machine of any or all preceding examples, wherein the predictive map generator comprises:

[0272] a loss map generator that generates, based on the values of the first agricultural characteristic in the information map and based on the predictive agricultural model, a predictive loss map as the functional predictive agricultural map, the predictive loss map mapping predicted values of a crop loss characteristic to different geographic locations in the field.

[0273] Example 9 is the agricultural work machine of any or all preceding examples, wherein the predictive map generator comprises:

[0274] a grain quality map generator that generates, based on the values of the first agricultural characteristic in the information map and based on the predictive agricultural model, a predictive grain quality map as the functional predictive agricultural map, the predictive grain quality map mapping predicted values of a grain quality characteristic to different geographic locations in the field.

[0275] Example 10 is the agricultural work machine of any or all preceding examples, wherein the predictive map generator comprises:

[0276] an internal distribution map generator that generates, based on the values of the first agricultural characteristic in the information map and based on the predictive agricultural model, a predictive internal distribution map as the functional predictive agricultural map, the predictive internal distribution map mapping predicted values of an internal distribution characteristic indicative of a characteristic of a distribution of treated material in the agricultural work machine to different geographic locations in the field.

[0277] Example 11 is the agricultural work machine of any or all preceding examples, wherein the communication system receives as the information map a topography map comprising a topography characteristic as the first agricultural characteristic, wherein the predictive model generator generates the predictive agricultural model to model a relationship between the topography characteristic and a second agricultural characteristic.

[0278] Example 12 is the agricultural work machine of any or all preceding examples, wherein the communication system receives as the information map a seed genotype map comprising a seed genotype as the first agricultural characteristic, wherein the predictive model generator generates the predictive agricultural model to model a relationship between the seed genotype and a second agricultural characteristic.

[0279] Example 13 is the agricultural work machine of any or all preceding examples, wherein the communication system receives a vegetation index map as the information map, the vegetation index map including a vegetation index characteristic as the first agricultural characteristic, wherein the predictive model generator generates the predictive agricultural model to model a relationship between the vegetation index characteristic and the second agricultural characteristic.

[0280] Example 14 is the agricultural work machine of any or all preceding examples, wherein the communication system receives a yield map as the information map, the yield map including a predicted yield characteristic as the first agricultural characteristic, wherein the predictive model generator generates the predictive agricultural model to model a relationship between the predicted yield characteristic and the second agricultural characteristic.

[0281] Example 15 is the agricultural work machine of any or all preceding examples, wherein the communication system receives a biomass map as the information map, the biomass map including a biomass characteristic as the first agricultural characteristic, wherein the predictive model generator generates the predictive agricultural model to model a relationship between the biomass characteristic and the second agricultural characteristic.

[0282] Example 16 is the agricultural work machine of any or all preceding examples, wherein the communication system receives a weed map as the information map, the weed map including a weed characteristic as the first agricultural characteristic, wherein the predictive model generator generates the predictive agricultural model to model a relationship between the weed characteristic and the second agricultural characteristic.

[0283] Example 17 is a computer-implemented method of generating a functional predictive agricultural map, comprising:

[0284] receiving, at an agricultural work machine, an information map indicating values of a first agricultural characteristic corresponding to different geographic locations in a field;

[0285] detecting a geographic location of the agricultural work machine;

[0286] detecting, with an in-field sensor, a second agricultural characteristic indicative of a characteristic of a processed material corresponding to the geographic location;

[0287] generating a predictive agricultural model modeling a relationship between the first agricultural characteristic and the second agricultural characteristic; and

[0288] controlling a predictive map generator to generate, based on the values of the first agricultural characteristic in the information map and the predictive agricultural model, a functional predictive agricultural map of the field that maps predicted values of the second agricultural characteristic to different locations in the field.

[0289] Example 18 is the computer-implemented method of any or all preceding examples, and further comprising:

[0290] The functional predictive agricultural map is configured for a control system that generates control signals to control controllable subsystems on the agricultural work machine based on the functional predictive agricultural map.

[0291] Example 19 is an agricultural work machine comprising:

[0292] a communication system that receives an information map comprising values of a first agricultural property corresponding to different geographic locations in a field;

[0293] a geographic location sensor that detects a geographic location of the agricultural work machine;

[0294] a field sensor that detects values of a second agricultural property corresponding to the geographic location that are indicative of a property of a material being processed;

[0295] a predictive model generator that generates a predictive agricultural model based on the values of the first agricultural property in the information map at the geographic location and the values of the second agricultural property sensed by the field sensor at the geographic location, the predictive agricultural model modeling a relationship between the first agricultural property and the second agricultural property; and

[0296] a predictive map generator that generates a functional predictive agricultural map of the field based on the values of the first agricultural property in the information map and based on the predictive agricultural model, the functional predictive agricultural map mapping predicted values of the second agricultural property to different geographic locations in the field, the predictive map generator configuring the functional predictive agricultural map for a control system that generates control signals to control controllable subsystems on the agricultural work machine based on the functional predictive agricultural map.

[0297] Example 20 is the agricultural work machine of any or all preceding examples, wherein the field sensor comprises one or more of:

[0298] a tail material property sensor that senses a tail material property in the agricultural work machine as the second agricultural property;

[0299] a loss sensor that senses a property indicative of crop loss from the agricultural work machine as the second agricultural property;

[0300] a grain quality sensor that senses a property indicative of grain quality in the agricultural work machine as the second agricultural property; and

[0301] an internal distribution sensor that senses a property indicative of a distribution of harvested material in the agricultural work machine as the second agricultural property.

[0302] 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 system comprising: A communication system (206) receives an information map (258) comprising values ​​of a first agricultural characteristic corresponding to different geographical locations in the field; A geolocation sensor (204) detects the geolocation of agricultural machinery; A field sensor (208) detects a value corresponding to the geographical location of a second agricultural characteristic that indicates the properties of the material being processed; A predictive model generator (210) generates a predictive agricultural model based on the value of the first agricultural characteristic in the information map (258) at the geographic location and the value of the second agricultural characteristic sensed by the field sensor (208) at the geographic location, the predictive agricultural model modeling the relationship between the first agricultural characteristic and the second agricultural characteristic; and A prediction map generator (212) generates a functional predictive agricultural map of the field based on the value of the first agricultural characteristic in the information map (258) and based on the predictive agricultural model. The functional predictive agricultural map maps the predicted value of the second agricultural characteristic to different geographical locations in the field.

2. The agricultural system according to claim 1, further comprising: A control system that generates control signals based on the functional predictive agricultural map to control controllable subsystems on the agricultural machinery.

3. The agricultural system according to claim 1, wherein, The field sensors include: A tailings characteristic sensor senses the characteristics of tailings in the agricultural machinery, and these characteristics are used as the second agricultural characteristic.

4. The agricultural system according to claim 1, wherein, The field sensors include: A loss sensor that senses a characteristic, as a second agricultural characteristic, indicating crop loss from the agricultural machinery.

5. The agricultural system according to claim 1, wherein, The field sensors include: A grain quality sensor that senses the characteristics of grain quality in the agricultural machinery, the characteristics of which are used as a second agricultural characteristic.

6. The agricultural system according to claim 1, wherein, The field sensors include: An internal distribution sensor senses a characteristic that is a second agricultural characteristic, indicating the distribution of harvested material in the agricultural machinery.

7. The agricultural system according to claim 1, wherein, The prediction map generator includes: A tailings characteristic map generator generates a predicted tailings characteristic map as the functional predicted agriculture map based on the value of the first agricultural characteristic in the information map and based on the predicted agriculture model. The predicted tailings characteristic map maps the predicted value of the tailings characteristic as the predicted value of the second agricultural characteristic to the different geographical locations in the field.

8. The agricultural system according to claim 1, wherein, The prediction map generator includes: A loss map generator generates a predicted loss map as the functional predicted agriculture map based on the value of the first agricultural characteristic in the information map and based on the predicted agriculture model, the predicted loss map mapping the predicted value of the crop loss characteristic to the different geographical locations in the field.

9. A computer-implemented method for generating functional predictive agricultural maps, comprising: Receive information map (258), the information map indicating the value of the first agricultural characteristic corresponding to different geographical locations in the field; Detect the geographical location of agricultural machinery (100); A second agricultural characteristic, which indicates the properties of the material being processed, is detected using a field sensor (208) corresponding to the geographical location; Generate a predictive agricultural model that models the relationship between the first agricultural characteristic and the second agricultural characteristic; and The control prediction map generator generates a functional predictive agriculture map of the field based on the value of the first agricultural characteristic in the information map and the predictive agriculture model, the functional predictive agriculture map mapping the predicted value of the second agricultural characteristic to the different locations of the field.

10. An agricultural system comprising: A communication system (206) receives an information map (258) comprising values ​​of a first agricultural characteristic corresponding to different geographical locations in the field; A geolocation sensor (204) detects the geolocation of agricultural machinery; A field sensor (208) detects a value corresponding to the geographical location of a second agricultural characteristic that indicates the properties of the material being processed; A predictive model generator (210) generates a predictive agricultural model based on the value of a first agricultural characteristic in the information map (258) at the geographic location and the value of a second agricultural characteristic sensed by the field sensor (208) at the geographic location, the predictive agricultural model modeling the relationship between the first agricultural characteristic and the second agricultural characteristic; and A prediction map generator (212) generates a functional prediction agricultural map of the field based on the value of the first agricultural characteristic in the information map (258) and the prediction agricultural model. The functional prediction agricultural map maps the predicted value of the second agricultural characteristic to the different geographical locations in the field. The prediction map generator configures the functional prediction agricultural map for a control system (214). The control system (214) generates control signals based on the functional prediction agricultural map to control the controllable subsystem (216) on the agricultural machine (100).

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