Predictive graph generation and control systems

Abstract

One or more infographics are obtained from agricultural machinery. These infographics map the values ​​of one or more agricultural characteristics at different geographic locations in the field. Field sensors on the agricultural machinery sense these agricultural characteristics as the machinery moves through the field. A predictive map generator generates predictive maps of the agricultural characteristics at different locations in the field, based on the relationships between the values ​​in the infographics and the agricultural characteristics sensed by the field sensors. These predictive maps can be output and used for automated machine control.

Description

Technical Field

[0001] This manual covers agricultural machinery, forestry machinery, construction machinery, and lawn management machinery. Background Technology

[0002] There are various types of agricultural machinery. Some agricultural machinery includes harvesters, such as combine harvesters, sugarcane harvesters, cotton harvesters, self-propelled forage harvesters, and reapers. Some harvesters can also be equipped with different types of headers to harvest different types of crops.

[0003] Various conditions in the field can have a variety of adverse effects on harvesting operations. Therefore, when such conditions are encountered during harvesting, the operator may attempt to modify the harvester controls.

[0004] The above discussion is provided for general background information only and is not intended to help determine the scope of the subject matter for which protection is sought. Summary of the Invention

[0005] One or more infographics are obtained from agricultural machinery. These infographics map the values ​​of one or more agricultural characteristics at different geographical locations in the field. Field sensors on the agricultural machinery sense these agricultural characteristics as the machinery moves through the field. A predictive map generator generates predictive maps of the agricultural characteristics at different locations in the field, based on the relationships between the values ​​in the infographics and the agricultural characteristics sensed by the field sensors. These predictive maps can be output and used for automated machine control.

[0006] This overview is provided to introduce, in a simplified form, some concepts that will be further described in the detailed description below. This overview is not intended to identify key or essential features of the claimed subject matter, nor is it intended to help determine the scope of the claimed subject matter. The claimed subject matter is not limited to examples addressing any or all of the shortcomings mentioned in the background art. Attached Figure Description

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

[0008] Figure 2 The following are block diagrams illustrating some parts of an agricultural harvester in more detail, based on some examples of this disclosure.

[0009] Figures 3A to 3B (Referred to herein as Figure 3) shows a flowchart illustrating an example of the operation of an agricultural harvester when generating a diagram.

[0010] Figure 4A This is a block diagram illustrating an example of a predictive model generator and a predictive metric graph generator.

[0011] Figure 4B This is a block diagram showing some examples of field sensors.

[0012] Figure 5 This is a flowchart illustrating an example of how an agricultural harvester receives a priori information map, detects agricultural characteristics, and generates a functional predictive map for controlling the agricultural harvester during harvesting operations.

[0013] Figure 6 This is a block diagram illustrating an example of a control region generator.

[0014] Figure 7 It is a diagram. Figure 6 The flowchart shows an example of the operation of the control region generator.

[0015] Figure 8 The diagram illustrates an example of how a control system operates when selecting target settings to control an agricultural harvester.

[0016] Figure 9 This is a block diagram illustrating an example of an operator interface controller.

[0017] Figure 10 This is a flowchart illustrating an example of an operator interface controller.

[0018] Figure 11 This is a schematic diagram showing an example of an operator interface display.

[0019] Figure 12 This is a block diagram illustrating an example of an agricultural harvester communicating with a remote server environment.

[0020] Figures 13 to 15 An example of a mobile device that can be used in agricultural harvesters is shown.

[0021] Figure 16 This is a block diagram illustrating an example of a computing environment that can be used for agricultural harvesters. Detailed Implementation

[0022] To facilitate an understanding of the principles of this disclosure, reference will now be made to the examples illustrated in the accompanying drawings, and these examples will be described using specific language. However, it should be understood that this disclosure is not intended to limit its scope. Any changes and modifications to the described apparatus, systems, and methods, as well as any further application of the principles of this disclosure, are entirely contemplated, as would normally occur to those skilled in the art to which this disclosure pertains. In particular, it is entirely conceivable that features, components, steps, or combinations thereof described for one example may be combined with features, components, steps, or combinations thereof described for other examples of this disclosure.

[0023] This description relates to using field data acquired concurrently with agricultural operations, combined with prior data from previous operations, to generate predictive maps that can be displayed or used by automated control systems. Previous agricultural operations may include, but are not limited to, application operations such as sowing, application of dry chemicals, wet chemicals, dry fertilizers, wet fertilizers, manure, and irrigation; removal operations such as previous harvesting, stacking, and baling; and ground-joining operations such as tillage and drainage tile installation. As mentioned above, previous operations of different machines influence the operation of the harvester. Data collected during previous agricultural operations can be geolocated to different locations on the field.

[0024] For example, applications such as seeding density are associated with densely planted areas of crop. Densely planted areas can negatively impact harvester operation because the subsystem requires more power to handle the large amount of material. Densely planted areas may also be associated with other agricultural characteristics such as higher yields and higher biomass. Overly dense crop areas may also be associated with lower yields.

[0025] Or, for example, the application of fertilizers, herbicides, and pesticides can also affect many characteristics of the field. These operations may reduce weeds, reduce pests, increase yields, etc. For example, the application of fungicides may affect crop stalk moisture or grain moisture. Or, for example, the application of herbicides may reduce the biomass that harvesters will encounter and process.

[0026] Or, for example, ground-level operations can affect soil properties, which in turn affect other crops or other agricultural characteristics. For instance, tillage operations may affect power use.

[0027] Or, for example, removal operations such as pumping remove material and potential nutrients from the field, which can subsequently affect crop composition or reduce yield.

[0028] Therefore, this discussion is directed to an example in which the system receives one or more maps generated during a priori operation and also uses field sensors to detect variables indicating agricultural characteristics during harvesting. The system generates a model that models the relationship between values ​​from the priori operation maps and field data from field position sensors. This model is used to generate a functional predictive agriculture map that predicts expected agricultural characteristics. The functional predictive agriculture characteristic map generated during harvesting can be presented to an operator or other user or used for the automatic control of an agricultural harvester during harvesting, or both.

[0029] Figure 1This is a partial schematic diagram of a self-propelled agricultural harvester 100. In the example shown, the agricultural harvester 100 is a combine harvester. Furthermore, although combine harvesters are provided as examples throughout this disclosure, it should be understood that this description is also applicable to other types of harvesters, such as cotton harvesters, sugarcane harvesters, self-propelled hay harvesters, slashers, or other agricultural operating machines. Therefore, this disclosure is intended to cover the various types of harvesters described, and is therefore not limited to combine harvesters. Moreover, this disclosure relates to other types of operating machines, such as agricultural seeders and sprayers in which the generation of predictive maps can be applied, construction equipment, forestry equipment, and lawn management equipment. Therefore, this disclosure is intended to cover these various types of harvesters and other operating machines, and is therefore not limited to combine harvesters.

[0030] like Figure 1 As shown, the agricultural harvester 100 schematically includes an operator's cab 101, which may 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 generally indicated by 104. The agricultural harvester 100 also includes a feed chamber 106, a feed accelerator 108, and a thresher generally indicated by 110. The feed chamber 106 and the feed accelerator 108 form part of a material handling subsystem 125. The header 102 is pivotally coupled to the frame 103 of the agricultural harvester 100 along a pivot axis 105. One or more actuators 107 drive the header 102 to move about the axis 105 in a direction generally indicated by arrow 109. Thus, the vertical position (header height) of the header 102 above the ground 111 over which the header 102 travels can be controlled by actuating the actuators 107. Figure 1 As not shown, the agricultural harvester 100 may also include one or more actuators that operate to apply a tilt angle, a lateral tilt angle, or both to the header 102 or multiple portions thereof. Tilt refers to the angle at which the cutter 104 engages with the crop. For example, the tilt angle is increased by controlling the header 102 to point the distal edge 113 of the cutter 104 more towards 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 lateral tilt angle refers to the orientation of the header 102 about the longitudinal axis of the agricultural harvester 100.

[0031] The threshing machine 110 schematically includes a threshing rotor 112 and a set of recesses 114. Additionally, the agricultural harvester 100 includes a separator 116. The agricultural harvester 100 also includes a cleaning subsystem or cleaning device (collectively referred to as cleaning subsystem 118), which 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 an unloading screw conveyor 134 and a spout 136. The clean grain elevator moves clean grain into a clean grain trough 132. The agricultural harvester 100 also includes a residue subsystem 138, which may include a shredder 140 and a spreader 142. The agricultural harvester 100 also includes a propulsion subsystem comprising an engine that drives ground engagement components 144 (e.g., wheels or tracks). In some examples, combine harvesters within the scope of this disclosure may have more than one of any of the subsystems mentioned above. In some examples, the agricultural harvester 100 may have a left cleaning subsystem and a right cleaning subsystem, a separator, etc., which... Figure 1 Not shown in the image.

[0032] In operation, and as an overview, the combine harvester 100 schematically moves across the field in the direction indicated by arrow 147. As the combine harvester 100 moves, the header 102 (and associated reel 164) engages the crop to be harvested and collects the crop toward the cutter 104. The operator of the combine harvester 100 can be a local human operator, a remote human operator, or an automated system. The operator of the combine harvester 100 can determine one or more of the height setting, tilt angle setting, or side tilt angle setting of the header 102. For example, the operator inputs one or more settings (described in more detail below) to the control system of the control actuator 107. The control system can also receive settings from the operator for establishing the tilt angle and side tilt angle of the header 102 and implement the input settings by controlling associated actuators (not shown) that operate to change the tilt angle and side tilt angle of the header 102. Actuator 107 maintains the cutter head 102 at a height above ground 111 based on a height setting, and maintains it at desired tilt and yaw angles where applicable. Each of the height, tilt, and yaw settings can be implemented independently of the others. The control system responds to cutter head errors (e.g., the difference between the height setting and the measured height of the cutter head 104 above ground 111, and in some examples, tilt and yaw angle errors) with a responsiveness determined based on a selected sensitivity level. If the sensitivity level is set at a higher sensitivity level, the control system responds to smaller cutter head position errors and attempts to reduce the detected errors faster than if the sensitivity level were lower.

[0033] 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 toward the feed accelerator 108 via a conveyor in the feed chamber 106, 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 recess 114. The threshed crop material is moved by the separator rotor in the separator 116, where a portion of the residue is moved toward the residue subsystem 138 by the discharge beater 126. This portion of residue conveyed to the residue subsystem 138 is shredded by the residue shredder 140 and spread on the field by the spreader 142. In other configurations, the residue is released from the agricultural harvester 100 into a stockpile. In other examples, the residue subsystem 138 may include a weed seed remover (not shown), such as a seed bagger or other seed collector, or a seed crusher or other seed destroyer.

[0034] The grain falls into the cleaning subsystem 118. A husk sieve 122 separates some of the larger pieces of material from the grain, and a screen 124 separates some of the finer pieces of material from the clean grain. The clean grain falls onto a screw conveyor that moves the grain to the inlet of a clean grain elevator 130, which moves the clean grain upwards, thereby storing it in a clean grain trough 132. Residue is removed from the cleaning subsystem 118 by an airflow generated by a cleaning fan 120. The cleaning fan 120 directs air upwards along an airflow path through the screen and the husk sieve. The airflow carries the residue in the agricultural harvester 100 backwards toward the residue treatment subsystem 138.

[0035] Tail feeder 128 returns the tail feed to thresher 110, where it is re-threshed. Alternatively, the tail feed may be conveyed by tail feeder or another conveyor to a separate re-threshing mechanism, where it is also re-threshed.

[0036] Figure 1 Also 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 front 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 the cleaning subsystem 118.

[0037] Ground speed sensor 146 senses the travel speed of the agricultural harvester 100 on the ground. 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 can be used to sense the travel speed, such as a global positioning system (GPS), dead reckoning system, long range navigation (LORAN) system, or various other systems or sensors that provide an indication of travel speed.

[0038] 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.

[0039] 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.

[0040] The agricultural harvester 100 may also include other sensors and measuring mechanisms. For example, the agricultural harvester 100 may include one or more of the following sensors: a header height sensor that senses the height of the header 102 above the ground 111; a stability sensor that senses vibration or jumping (and amplitude) of the agricultural harvester 100; a residue setting sensor configured to sense whether the agricultural harvester 100 is configured to chop residue, create a pile, etc.; a cleaning device fan speed sensor for sensing the speed of the fan 120; a recess gap sensor that senses the gap between the rotor 112 and the recess 114; a threshing rotor speed sensor that senses the rotor speed of the rotor 112; a husk sieve gap sensor that senses the size of the opening in the husk sieve 122; a screen gap sensor that senses the size of the opening in the screen 124; and a material other than grain sensor. A grain (MOG) humidity sensor senses the humidity level of the MOG passing through the harvester 100; one or more machine setting sensors are configured to sense various configurable settings of the harvester 100; a machine orientation sensor senses the orientation of the harvester 100; and a crop property sensor senses various different types of crop properties (such as crop type, crop humidity, and other crop properties). The crop property sensor can also be configured to sense characteristics of the cut crop material as it is processed by the harvester 100. For example, in some cases, the crop property sensor may sense: grain quality (such as broken grain, MOG level); grain composition (such as starch and protein); and the grain feeding rate as the grain travels through the feed chamber 106, the clean grain elevator 130, or other locations within the harvester 100. The crop property sensor may also sense the feeding rate of biomass through the feed chamber 106, the separator 116, or other locations within the harvester 100. The crop property sensor can also sense the feeding rate via the elevator 130 or other parts of the agricultural harvester 100 as the grain mass flow rate, or provide other output signals indicating other sensed variables.

[0041] Examples of sensors used to detect or sense dynamic characteristics include, but are not limited to, voltage sensors, current sensors, torque sensors, hydraulic sensors, hydraulic flow sensors, force sensors, bearing load sensors, and rotation sensors. Dynamic characteristics can be measured at different intervals. For example, power usage can be sensed at the machine level, the subsystem level, or through individual components of a subsystem.

[0042] Before describing how the agricultural harvester 100 generates a functional predictive map and uses that map for control, a brief description of some items on the agricultural harvester 100 and their operation will be provided first. Figure 2 The description in Figure 3 illustrates the following: A general type of prior information map is received, and information from the prior information map is combined with georeferenced sensor signals generated by field sensors, where the sensor signals indicate characteristics of the field, such as the dynamic characteristics of an agricultural harvester. These field characteristics may include, but are not limited to, field properties such as slope, weed intensity, weed type, soil moisture, and surface quality; crop properties (such as crop height, crop moisture, crop density, and crop condition); grain properties (such as grain moisture, grain size, and grain test weight); and machine performance characteristics (such as loss level, work quality, fuel consumption, and power utilization). The relationship between the characteristic values ​​obtained from the field sensor signals and the prior information map values ​​is identified, and this relationship is used to generate a new functional predictive map. The functional predictive map predicts values ​​at different geographic locations in the field, and one or more of these values ​​can be used to control one or more subsystems of a machine, such as an agricultural harvester. In some cases, the functional predictive map can be presented to a user, such as an operator of an agricultural operating machine, which may be an agricultural harvester. Predictive function maps can be presented to users visually (e.g., via a display), tactilely, or audibly. Users can interact with predictive function maps to perform editing operations and other user interface actions. In some cases, predictive function maps can be used to control agricultural machinery (such as agricultural harvesters), presented to operators or other users, or presented to operators or users for operator or user interaction, or one or more of these functions.

[0043] In reference Figure 2 After describing the general method in Figure 3, refer to Figure 4 and... Figure 5 A more specific method for generating a predictive feature map is described, which can be presented to an operator or user, or used to control an agricultural harvester 100, or both. Again, although this discussion is directed to agricultural harvesters, and particularly combine harvesters, the scope of this disclosure extends to other types of agricultural harvesters or other agricultural operating machines.

[0044] Figure 2 This is a block diagram showing some parts of an example agricultural harvester 100. Figure 2 An agricultural harvester 100 is illustrated schematically including one or more processors or servers 201, a data storage device 202, a geographic location sensor 204, a communication system 206, and one or more field sensors 208 that sense one or more agricultural characteristics of the field during harvesting operations. Agricultural characteristics can include any characteristic capable of influencing the harvesting operation. Some examples of agricultural characteristics include characteristics of the harvester, the field, the plants on the field, and the weather. Other types of agricultural characteristics are also included. The field sensors 208 generate values ​​corresponding to the sensed characteristics. The agricultural harvester 100 also includes a predictive model or relation generator (collectively referred to as "predictive model generator 210"), a predictive graph generator 212, a control area generator 213, a control system 214, one or more controllable subsystems 216, and an operator interface mechanism 218. The agricultural harvester 100 may also include a variety of other agricultural harvester functions 220. Field sensors 208 include, for example, onboard sensors 222, remote sensors 224, and other sensors 226 that sense field characteristics during agricultural operations. A predictive model generator 210 schematically includes a priori information variable-to-field variable model generator 228, and the predictive model generator 210 may 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 controller 240, a platform position controller 242, a residue system controller 244, a machine cleaning controller 245, an area controller 247, and the system 214 may include other items 246. The controllable subsystem 216 includes a machine and header actuator 248, a propulsion subsystem 250, a steering subsystem 252, a residue subsystem 138, a machine cleaning subsystem 254, and the subsystem 216 may include a variety of other subsystems 256.

[0045] Figure 2 The agricultural harvester 100 is also shown to receive a priori information map 258. As described below, the priori information map 258 includes, for example, a vegetation index map or a vegetation map from a priori operations. However, the priori information map 258 may also encompass other types of data obtained prior to the harvesting operation or maps from prior operations. Figure 2The diagram also illustrates that operator 260 can operate agricultural harvester 100. Operator 260 interacts with operator interface mechanism 218. In some examples, operator interface mechanism 218 may include joysticks, control levers, steering wheels, linkages, pedals, buttons, dials, keypads, user-actuable elements (such as icons, buttons, etc.) on a user interface display device, microphones and speakers (where speech recognition and speech synthesis are provided), and various other types of control devices. Where a touch-sensitive display system is provided, operator 260 may interact with operator interface mechanism 218 using touch gestures. The examples described above are provided as illustrative examples and are not intended to limit the scope of this disclosure. Therefore, other types of operator interface mechanisms 218 may be used, and other types of operator interface mechanisms are within the scope of this disclosure.

[0046] The prior information map 258 can be downloaded to the agricultural harvester 100 and stored in the data storage device 202 using the communication system 206 or other means. In some examples, the communication system 206 may be a cellular communication system, a system for communication over a wide area network or a local area network, a system for communication over a near-field communication network, or a communication system configured to communicate over any one or a combination of various other networks. The communication system 206 may also include systems that facilitate the downloading or transfer of information to or from a secure digital (SD) card or a universal serial bus (USB) card, or both.

[0047] The geolocation sensor 204 schematically senses or detects the geolocation or orientation of the agricultural harvester 100. The geolocation sensor 204 may include, but is not limited to, a GNSS receiver that receives signals from a Global Navigation Satellite System (GNSS) satellite transmitter. The geolocation sensor 204 may also include a real-time kinematic (RTK) component configured to improve the accuracy of the position data derived from the GNSS signals. The geolocation sensor 204 may include any of a dead reckoning system, a cellular triangulation system, or a variety of other geolocation sensors.

[0048] The field sensor 208 can be referenced above. Figure 1Any of the sensors described. Field sensor 208 includes onboard sensor 222 mounted on the agricultural harvester 100. Such sensors may include, for example, sensing sensors (e.g., forward-looking monocular or stereo camera systems and image processing systems), and image sensors inside the agricultural harvester 100 (such as one or more grain cleaning cameras mounted to identify weed seeds leaving the agricultural harvester 100 through the residue subsystem or from the cleaning subsystem). Field sensor 208 also includes remote field sensor 224 for capturing field information. Field data includes data acquired from sensors mounted on the harvester, or data acquired by any of the sensors that detect data during harvesting operations.

[0049] Predictive model generator 210 generates a model indicating the relationship between values ​​sensed by field sensors 208 and metrics mapped to the field by prior information map 258. For example, if prior information map 258 maps historical yield values ​​to different locations in the field, and field sensors 208 sense values ​​indicating header power usage, then prior information variable to field variable model generator 228 generates a predictive model that models the relationship between historical yield values ​​and header power usage values. The predictive model can also be generated based on historical yield values ​​from prior information map 258 and multiple field data values ​​generated by field sensors 208. Then, predictive map generator 212 uses the predictive model generated by predictive model generator 210 to generate a functional predictive dynamic map based on prior information map 258, which predicts values ​​of dynamic characteristics such as subsystem power usage sensed by field sensors 208 at different locations in the field.

[0050] In some examples, the type of values ​​in functional predictive graph 263 may be the same as the field data type sensed by field sensor 208. In some cases, the type of values ​​in functional predictive graph 263 may have a different unit than the data sensed by field sensor 208. In some examples, the type of values ​​in functional predictive graph 263 may be different from the data type sensed by field sensor 208, but related to the data type sensed by field sensor 208. For example, in some examples, the data type sensed by field sensor 208 may indicate the type of values ​​in functional predictive graph 263. In some examples, the type of data in functional predictive graph 263 may be different from the data type in prior information graph 258. In some cases, the type of data in functional predictive graph 263 may have a different unit than the data in prior information graph 258. In some examples, the type of data in functional predictive graph 263 may be different from the data type in prior information graph 258, but related to the data type in prior information graph 258. For example, in some examples, the data type in prior information graph 258 may indicate the type of data in functional predictive graph 263. In some examples, the type of data in the functional predictive graph 263 is different from one or both of the field data type sensed by the field sensor 208 and the data type in the prior information graph 258. In some examples, the type of data in the functional predictive graph 263 is the same as one or both of the field data type sensed by the field sensor 208 and the data type in the prior information graph 258. In some examples, the type of data in the functional predictive graph 263 is the same as one of the field data type sensed by the field sensor 208 or the data type in the prior information graph 258, but different from the other.

[0051] Continuing with the previous example, where prior infographic 258 is a historical yield map and field sensor 208 senses values ​​indicating header power usage, predictive map generator 212 can use the historical yield values ​​from prior infographic 258 and the model generated by predictive model generator 210 to generate a functional predictive map 263 predicting header power usage at different locations in the field. Predictive map generator 212 therefore outputs predictive map 264.

[0052] like Figure 2As shown, predictive map 264 predicts the values ​​of sensed characteristics (sensed by field sensors 208) or characteristics related to sensed characteristics at various locations on the field, based on prior information values ​​at each location in prior information map 258 and predictive models. For example, if predictive model generator 210 has generated a predictive model indicating the relationship between historical yield values ​​and header power usage, then, given historical yield values ​​at different locations on the field, predictive map generator 212 generates predictive map 264 predicting the values ​​of header power usage at those different locations on the field. The historical values ​​at these locations obtained from the historical yield map, and the relationship between historical yield values ​​and header power usage obtained from the predictive model, are used to generate predictive map 264.

[0053] The following will describe some changes in the data types mapped in the prior information graph 258, the data types sensed by the field sensor 208, and the data types predicted on the predictive graph 264.

[0054] In some examples, the data type in the prior infographic 258 differs from the data type sensed by the field sensor 208, but the data type in the predictive infographic 264 is the same as that sensed by the field sensor 208. For example, the prior infographic 258 could be a vegetation index map, and the variable sensed by the field sensor 208 could be yield. The predictive infographic 264 could then be a predictive yield map that maps predicted yield values ​​to different geographic locations in the field. In another example, the prior infographic 258 could be a vegetation index map, and the variable sensed by the field sensor 208 could be crop height. The predictive infographic 264 could then be a predictive crop height map that maps predicted crop height values ​​to different geographic locations in the field.

[0055] Furthermore, in some examples, the data type in the prior information map 258 differs from the data type sensed by the field sensor 208, and the data type in the predictive map 264 differs from both the data type in the prior information map 258 and the data type sensed by the field sensor 208. For example, the prior information map 258 could be a vegetation index map, and the variable sensed by the field sensor 208 could be crop height. The predictive map 264 could then be a predictive biomass map that maps predicted biomass values ​​to different geographic locations in the field. In another example, the prior information map 258 could be a vegetation index map, and the variable sensed by the field sensor 208 could be yield. The predictive map 264 could then be a predictive speed map that maps predicted harvester speed values ​​to different geographic locations in the field.

[0056] In some examples, the prior information map 258 is derived from previous passage through the field during a priori operations, and its data type differs from the data type sensed by the field sensor 208, but the data type in the predictive map 264 is the same as that sensed by the field sensor 208. For example, the prior information map 258 could be a seed population map generated during planting, and the variable sensed by the field sensor 208 could be stem size. The predictive map 264 could then be a predictive stem size map that maps predicted stem size values ​​to different geographic locations in the field. In another example, the prior information map 258 could be a seed mix map, and the variable sensed by the field sensor 208 could be crop state, such as upright or lodged crop. The predictive map 264 could then be a predictive crop state map that maps predicted crop state values ​​to different geographic locations in the field.

[0057] In some examples, the prior information map 258 is derived from a field previously traversed during a prior operation, and its data type is the same as that sensed by the field sensor 208, and the data type in the predictive map 264 is also the same as that sensed by the field sensor 208. For example, the prior information map 258 could be a yield map generated during the previous year, and the variable sensed by the field sensor 208 could be yield. The predictive map 264 could then be a predictive yield map that maps predicted yield values ​​to different geographic locations within the field. In such an example, the predictive model generator 210 could use the relative yield differences from the geographic reference prior information map 258 from the previous year to generate a predictive model that models the relationship between the relative yield differences on the prior information map 258 and the yield values ​​sensed by the field sensor 208 during the current harvest operation. The predictive model is then used by the predictive map generator 210 to generate the predictive yield map.

[0058] In another example, the prior information map 258 could be a power usage map of the threshing / separation subsystem generated during prior operations, and the variables sensed by the field sensor 208 could be the power usage of the threshing / separation subsystem. The predictive map 264 could then be a predictive power usage map of the threshing / separation subsystem that maps predicted power usage values ​​to different geographic locations in the field.

[0059] In some examples, predictive graph 264 may be provided to control region generator 213. Control region generator 213 groups the multiple adjacent portions of a region into one or more control regions based on data values ​​associated with multiple adjacent portions of a region in predictive graph 264. A control region may include two or more consecutive portions of a region (such as a field), for which the control parameters corresponding to the control region used to control the controllable subsystem are constant. For example, the response time of changing the settings of controllable subsystem 216 may not be satisfactory in responding to changes in values ​​contained in a graph such as predictive graph 264. In this case, control region generator 213 analyzes the graph and identifies control regions with defined dimensions to accommodate the response time of controllable subsystem 216. In another example, the size of the control region may be determined to reduce wear caused by excessive actuator movement due to continuous adjustment. In some examples, different groups of control regions may be available for each controllable subsystem 216 or group of controllable subsystems 216. Control regions may be added to predictive graph 264 to obtain predictive control region graph 265. Predictive control area map 265 can therefore be similar to predictive map 264, except that predictive control area map 265 includes control area information that defines the control area. Therefore, as described herein, functional predictive map 263 may or may not include control areas. Both predictive map 264 and predictive control area map 265 are functional predictive maps 263. In one example, functional predictive map 263 does not include control areas, as in predictive map 264. In another example, functional predictive map 263 does include control areas, as in predictive control area map 265. In some examples, if an intercropping production system is implemented, multiple crops may coexist in the field. In this case, predictive map generator 212 and control area generator 213 are able to identify the location and characteristics of two or more crops and then generate predictive map 264 and predictive control area map 265 accordingly.

[0060] It should also be understood that the control region generator 213 can cluster values ​​to generate control regions, and these control regions can be added to the predictive control region graph 265 or a separate graph to show only the generated control regions. In some examples, the control regions can be used to control or calibrate the agricultural harvester 100 or both. In other examples, the control regions can be presented to the operator 260 for controlling or calibrating the agricultural harvester 100, and in still other examples, the control regions can be presented to the operator 260 or another user or stored for later use.

[0061] Predictive map 264 or predictive control area map 265, or both, is provided to control system 214, which generates control signals based on predictive map 264 or predictive control area map 265, or both. In some examples, communication system controller 229 controls communication system 206 to communicate predictive map 264 or predictive control area map 265, or control signals based on predictive map 264 or predictive control area map 265, to other agricultural harvesters harvesting in the same field. In some examples, communication system controller 229 controls communication system 206 to send predictive map 264, predictive control area map 265, or both, to other remote systems.

[0062] Operator interface controller 231 is operable to generate control signals to control operator interface mechanism 218. Operator interface controller 231 is also operable to present operator 260 with predictive graph 264 or predictive control area graph 265, or other information derived from or based on predictive graph 264, predictive control area graph 265, or both. Operator 260 can be a local operator or a remote operator. As an example, controller 231 generates control signals to control the display mechanism to display one or both of predictive graph 264 and predictive control area graph 265 to operator 260. Controller 231 can generate operator-actuable mechanisms that are shown and can be actuated by the operator to interact with the displayed graphs. The operator can edit the graphs by, for example, correcting characteristics displayed on the graphs based on the operator's observation. Setting controller 232 can generate control signals based on predictive graph 264, predictive control area graph 265, or both to control various settings on agricultural harvester 100. For example, controller 232 can generate control signals to control the machine and header actuator 248. In response to the generated control signals, the machine and header actuator 248 operate to control, for example, screen and chaff screen settings, recess clearance, rotor settings, cleaning fan speed settings, header height, header function, reel speed, reel position, belt conveyor function (where the harvester 100 is coupled to the belt conveyor header), corn header function, internal distribution control, and other actuators 248 affecting other functions of the harvester 100. Path planning controller 234 schematically generates control signals to control steering subsystem 252 to turn the harvester 100 according to a desired path. Path planning controller 234 can control the path planning system to generate a route for the harvester 100 and can control propulsion subsystem 250 and steering subsystem 252 to turn the harvester 100 along that route. The feed rate controller 236 can control various subsystems (such as the propulsion subsystem 250 and the machine actuator 248) to control the feed rate based on a predictive map 264 or a predictive control area map 265, or both. For example, when the harvester 100 approaches a region where the predicted yield value is higher than a selected threshold, the feed rate controller 236 can reduce the speed of the harvester 100 to maintain the grain feed rate. The header and reel controller 238 can generate control signals to control the header or reel or other header functions. The belt conveyor controller 240 can generate control signals based on the predictive map 264, the predictive control area map 265, or both, to control the belt conveyor belt or other belt conveyor functions.The tabletop position controller 242 can generate control signals based on predictive map 264 or predictive control area map 265, or both, to control the position of the tabletop included on the cutter, and the residue system controller 244 can generate control signals based on predictive map 264 or predictive control area map 265, or both, to control the residue subsystem 138. The machine cleaning controller 245 can generate control signals to control the machine cleaning subsystem 254. Other controllers included on the agricultural harvester 100 can also control other subsystems based on predictive map 264 or predictive control area map 265, or both.

[0063] Figure 3A and Figure 3B (Hereinafter referred to as Figure 3) shows a flowchart illustrating an example of the operation of an agricultural harvester 100 in generating a predictive map 264 and a predictive control region map 265 based on prior information map 258.

[0064] At 280, the agricultural harvester 100 receives a priori information map 258. Examples of priori information map 258 or receiving priori information map 258 are discussed with respect to boxes 281, 282, 284, and 286. As discussed above, as shown in box 282, priori information map 258 maps the values ​​of variables corresponding to a first characteristic to different locations in the field. As shown in box 281, receiving priori information map 258 may include selecting one or more of a plurality of possible priori information maps available. For example, one priori information map may be a vegetation index map generated from aerial imagery. Another priori information map may be a map generated during a previous pass through the field, which may be performed by a different machine (such as a sprayer or other machine) performing the previous operation in the field. The process of selecting one or more priori information maps can be manual, semi-automatic, or automatic. Priori information map 258 is based on data collected prior to the current harvesting operation. This is indicated by box 284. For example, the data may be collected based on aerial imagery acquired during the previous year or early in the current growing season or at other times. As shown in box 285, the prior information map can be a predictive map that predicts characteristics based on the prior information map and its relationship with field sensors. The process of generating the predictive map is as follows: Figure 5The data is presented in the diagram. This process can also be performed using other sensors and other prior maps to generate, for example, predictive yield maps or predictive biomass maps. These predictive maps can be used as prior maps in other predictive processes, as shown in box 285. The data can be based on data detected in a manner different from using aerial imagery. For example, data from prior information map 258 can be transmitted to the agricultural harvester 100 using communication system 206 and stored in data storage device 202. Data from prior information map 258 can also be otherwise provided to the agricultural harvester 100 using communication system 206, and this is represented by box 286 in the flowchart of Figure 3. In some examples, prior information map 258 can be received by communication system 206.

[0065] At the start of the harvesting operation, field sensor 208 generates sensor signals indicating one or more field data values ​​that indicate agricultural characteristics, as shown in box 288. Examples of field sensor 288 are discussed with respect to boxes 222, 290, and 226. As explained above, field sensor 208 includes airborne sensor 222, remote field sensor 224 (such as a UAV-based sensor that collects field data on each flight (as shown in box 290)), or other types of field sensors specified by field sensor 226. In some examples, position, heading, or speed data from geolocation sensor 204 is georeferenced against data from airborne sensors.

[0066] Predictive model generator 210 controls prior information variable to field variable model generator 228 to generate a model that models the relationship between the mapped values ​​contained in prior information graph 258 and the field values ​​sensed by field sensor 208, as shown in box 292. The characteristics or data types represented by the mapped values ​​in prior information graph 258 and the field values ​​sensed by field sensor 208 can be the same or different characteristics or data types.

[0067] The relation or model generated by the predictive model generator 210 is provided to the predictive graph generator 212. The predictive graph generator 212 uses the predictive model and prior information graph 258 to generate a predictive graph 264 that predicts the values ​​of characteristics at different geographical locations in the harvesting field sensed by the field sensor 208, or the values ​​of different characteristics related to the characteristics sensed by the field sensor 208, as shown in box 294.

[0068] It should be noted that in some examples, the prior information map 258 may include two or more different maps or two or more different layers of a single map. Each layer may represent a data type different from that of another layer, or the layers may have the same data type acquired at different times. Each map in the two or more different maps, or each layer in the two or more different layers of a map, maps different types of variables to geographic locations in the field. In such an example, the predictive model generator 210 generates a predictive model that models the relationship between the field data and each of the different variables mapped by the two or more different maps or two or more different layers. Similarly, the field sensor 208 may include two or more sensors, each sensing a different type of variable. Therefore, the predictive model generator 210 generates a predictive model that models the relationship between each type of variable mapped by the prior information map 258 and each type of variable sensed by the field sensor 208. Predictive map generator 212 can use each of the graphs or layers in the predictive model and prior information graph 258 to generate a functional predictive map 263 that predicts the value of each sensed property (or property associated with the sensed property) at different locations in the harvesting field as sensed by field sensor 208.

[0069] Predictive graph generator 212 configures predictive graph 264 such that predictive graph 264 can be operated (or consumed) by control system 214. Predictive graph generator 212 can provide predictive graph 264 to control system 214 or to control region generator 213 or both. Some examples of different ways in which predictive graph 264 can be configured or output are described with respect to boxes 296, 295, 299 and 297. For example, predictive graph generator 212 configures predictive graph 264 such that predictive graph 264 includes values ​​that can be read by control system 214 and used as the basis for generating control signals for one or more of the different controllable subsystems of agricultural harvester 100, as shown in box 296.

[0070] Control area generator 213 can divide predictive map 264 into control areas based on values ​​on predictive map 264. Values ​​that are consecutively geolocated within each other's thresholds can be grouped into control areas. The thresholds can be default thresholds, or they can be set based on operator input, input from the automation system, or other criteria. The size of the areas can be based on the responsiveness of control system 214, controllable subsystem 216, wear considerations, or other criteria, as shown in box 295. Predictive map generator 212 configures predictive map 264 for presentation to operators or other users. Control area generator 213 can configure predictive control area map 265 for presentation to operators or other users. This is indicated by box 299. When presented to an operator or other user, the presentation of predictive map 264 or predictive control area map 265, or both, may include one or more of the following: a geographic location-related predicted value on predictive map 264, a geographic location-related control area on predictive control area map 265, and setting values ​​or control parameters used based on the predicted value on map 264 or the area on predictive control area map 265. In another example, the presentation may include more abstract or more detailed information. The presentation may also include a confidence level indicating that the predicted value on predictive map 264 or the area on predictive control area map 265 conforms to the accuracy of measurements that can be measured by sensors on harvester 100 as harvester 100 moves through the field. Additionally, where information is presented to more than one location, an authentication and authorization system may be provided to implement the authentication and authorization process. For example, there may be a hierarchy of individuals authorized to view and modify maps and other presented information. As an example, an onboard display device may display the map locally on the machine in near real-time, or the map may be generated at one or more remote locations, or both. In some examples, each physical display device at each location can be associated with a person or user permission level. User permission levels can be used to determine which display markers are visible on the physical display devices and which values ​​the corresponding person can change. For example, the local operator of harvester 100 may not be able to see the information corresponding to predictive diagram 264 or make any changes to the machine's operation. However, a supervisor, such as a manager at a remote location, may be able to see predictive diagram 264 on the display but be prevented from making any changes. A manager at a separate remote location may be able to see all elements on predictive diagram 264 and also be able to change predictive diagram 264. In some cases, predictive diagram 264, accessible and modifiable by a remotely located manager, can be used for machine control. This is an example of an authorization hierarchy that can be implemented. Predictive diagram 264 or predictive control area diagram 265, or both, can also be configured in other ways, as shown in box 297.

[0071] At box 298, the control system receives input from the geolocation sensor 204 and other field sensors 208. Specifically, at box 300, the control system 214 detects input from the geolocation sensor 204 identifying the geolocation of the harvester 100. Box 302 indicates that the control system 214 receives sensor input indicating the trajectory or heading of the harvester 100, and box 304 indicates that the control system 214 receives the speed of the harvester 100. Box 306 indicates that the control system 214 receives other information from various field sensors 208.

[0072] At block 308, control system 214 generates a control signal to control controllable subsystem 216 based on predictive map 264 or predictive control area map 265, or both, and inputs from geographic location sensor 204 and any other field sensors 208. At block 310, control system 214 applies the control signal to the controllable subsystem. It should be understood that the specific control signal generated and the specific controllable subsystem 216 controlled can vary based on one or more different factors. For example, the generated control signal and the controllable subsystem 216 controlled can be based on the type of predictive map 264 or predictive control area map 265, or both, being used. Similarly, the generated control signal, the controllable subsystem 216 controlled, and the timing of the control signal can be based on various delays in the crop flow through agricultural harvester 100 and the responsiveness of controllable subsystem 216.

[0073] At box 312, it is determined whether the harvesting operation has been completed. If the harvesting is not completed, the process proceeds to box 314, where field sensor data from geolocation sensor 204 and field sensor 208 (and possibly other sensors) are read.

[0074] In some examples, at box 316, the agricultural harvester 100 can also detect learning trigger criteria to perform machine learning on one or more of the predictive graph 264, predictive control region graph 265, model generated by predictive model generator 210, region generated by control region generator 213, control algorithm implemented by controller in control system 214, and other learning trigger criteria.

[0075] The learning trigger criterion can include any of a variety of different criteria. Some examples of detecting the trigger criterion are discussed with regard to boxes 318, 320, 321, 322, and 324. For example, in some examples, triggering learning can include recreating the relationships used to generate a predictive model when a threshold amount of field sensor data is received from field sensor 208. In such an example, an amount of field sensor data received from field sensor 208 exceeding a threshold triggers or prompts predictive model generator 210 to generate a new predictive model used by predictive graph generator 212. Thus, as the agricultural harvester 100 continues its harvesting operation, receiving a threshold amount of field sensor data from field sensor 208 triggers the creation of a new relationship represented by the predictive model generated by predictive model generator 210. Furthermore, the new predictive model can be used to regenerate a new predictive graph 264, a predictive control region graph 265, or both. Box 318 represents detecting a threshold amount of field sensor data used to trigger the creation of a new predictive model.

[0076] In other examples, the learning trigger criterion may be based on the degree of change in field sensor data from field sensor 208, such as the degree of change over time or compared to previous values. For example, if the change within the field sensor data (or the relationship between the field sensor data and the information in prior information graph 258) is within a selected range, less than a defined amount, or below a threshold, then a new predictive model is not generated by predictive model generator 210. As a result, predictive graph generator 212 does not generate a new predictive graph 264, predictive control region graph 265, or both. However, for example, if the change within the field sensor data is outside the selected range, greater than a defined amount, or above a threshold, then predictive model generator 210 uses all or part of the newly received field sensor data used by predictive graph generator 212 to generate a new predictive graph 264 to generate a new predictive model. At box 320, changes in field sensor data (such as the magnitude of data exceeding the selected range or the magnitude of changes in the relationship between field sensor data and information in prior information graph 258) can be used as triggers to generate new predictive models and predictive graphs. Continuing with the example described above, thresholds, ranges, and defined quantities can be set to default values, set by an operator or user through user interface interaction, set by the automation system, or otherwise.

[0077] Other learning trigger criteria can also be used. For example, if the predictive model generator 210 switches to a different prior infographic (different from the initially selected prior infographic 258), the switch to a different prior infographic can trigger relearning by the predictive model generator 210, the predictive map generator 212, the control region generator 213, the control system 214, or others. In another example, the transition of the agricultural harvester 100 to different terrain or to a different control region can also be used as a learning trigger criterion.

[0078] In some cases, operator 260 can also edit predictive graph 264 or predictive control area graph 265, or both. Editing can change the values ​​on predictive graph 264, change the size, shape, position, or presence of control areas on predictive control area graph 265, or both. Box 321 shows that the edited information can be used as a learning trigger criterion.

[0079] In some cases, operator 260 may also observe that the automatic control of the controllable subsystem is not as the operator expects. In this case, operator 260 may provide manual adjustments to the controllable subsystem, reflecting operator 260's expectation that the controllable subsystem will operate in a manner different from that commanded by control system 214. Therefore, manual changes to the settings by operator 260 may result in one or more of the following: based on operator 260's adjustments (as shown in box 322), predictive model generator 210 relearns the model, predictive graph generator 212 regenerates graph 264, control region generator 213 regenerates one or more control regions on predictive control region graph 265, and control system 214 relearns the control algorithm or performs machine learning on one or more of the controller components 232 to 246 in control system 214. Box 324 indicates the use of other triggering learning criteria.

[0080] In other examples, relearning can be performed periodically or intermittently, for example based on a chosen time interval, such as a discrete time interval or a variable time interval, as shown in box 326.

[0081] If relearning is triggered (whether based on a learning trigger criterion or on the elapsed time interval, as shown in box 326), one or more of the predictive model generator 210, predictive graph generator 212, control region generator 213, and control system 214 perform machine learning to generate new predictive models, new predictive graphs, new control regions, and new control algorithms, respectively, based on the learning trigger criterion. The new predictive models, new predictive graphs, and new control algorithms are generated using any additional data collected since the last learning operation. The execution of relearning is indicated by box 328.

[0082] If the harvesting operation is complete, the operation moves from box 312 to box 330, where one or more of the predictive map 264, predictive control region map 265, and predictive models generated by predictive model generator 210 are stored. The predictive map 264, predictive control region map 265, and predictive models can be stored locally on data storage device 202 or transmitted to a remote system using communication system 206 for later use.

[0083] It will be noted that while some examples in this paper describe the predictive model generator 210 and the predictive graph generator 212 receiving prior information graphs when generating predictive models and functional predictive graphs, respectively, in other examples, the predictive model generator 210 and the predictive graph generator 212 may receive other types of graphs, including predictive graphs, such as functional predictive graphs generated during harvesting operations, when generating predictive models and functional predictive graphs, respectively.

[0084] Figure 4 is Figure 1 Figure 4 shows a block diagram of a portion of an agricultural harvester 100. In particular, Figure 4 illustrates examples of the predictive model generator 210 and the predictive map generator 212 in more detail. Figure 4 also illustrates the information flow between the different components shown. The predictive model generator 210 receives one or more prior operation maps 332 as information maps. The prior operation map 332 includes geographic reference values ​​from previous agricultural operations on the field. Some examples or prior operations may include, but are not limited to, the application of materials such as sowing, dry chemicals, wet chemicals, dry fertilizer, wet fertilizer, manure, irrigation, etc.; the removal of materials such as previous harvesting, stacking, and baling; ground tillage, application of drainage pipes, etc. In the case of application, the map may include the type of material applied, the amount of material applied, and the area where the material was applied.

[0085] The predictive map generator 210 also receives a geographic location 334 or a geographic location indication from the geographic location sensor 204. The field sensor 208 illustratively includes an agricultural sensor 336 and a processing system 338. The sensor 336 senses one or more agricultural characteristics. In some cases, the agricultural sensor 336 may be located on the agricultural harvester 100. The processing system 338 processes the sensor data generated from the agricultural sensor 336 to generate processed data, some examples of which are described below.

[0086] Figure 4B This is a block diagram showing some examples of the real-time (field) sensor 208. Figure 4B Some of the sensors shown, or different combinations thereof, may simultaneously have sensor 236 and processing system 338. Figure 4BSome of the possible field sensors 208 shown are illustrated and described relative to the previous figures and are similarly numbered. Figure 4B The field sensors 208 shown may include an operator input sensor 980, a machine sensor 982, a harvested material property sensor 984, a field and soil property sensor 985, an environmental property sensor 987, and may include a variety of other sensors 226. The operator input sensor 980 may be a sensor that senses operator input via an operator interface mechanism 218. Therefore, the operator input sensor 980 can sense user movements of linkages, joysticks, steering wheels, buttons, dials, or pedals. The operator input sensor 980 can also sense user interactions with other operator input mechanisms, such as interactions with a touchscreen, a microphone utilizing voice recognition, or any of the various other operator input mechanisms.

[0087] Machine sensor 982 can sense various characteristics of the agricultural harvester 100. For example, as discussed above, machine sensor 982 may include machine speed sensor 146, separator loss sensor 148, clean grain camera 150, forward-view image capture mechanism 151, loss sensor 152, or geolocation sensor 204, examples of which are described above. Machine sensor 982 may also include machine setting sensor 991 that senses machine settings. (See above references) Figure 1Examples of machine setups are described. A front-end equipment (e.g., header) position sensor 993 can sense the position of the header 102, reel 164, cutter 104, or other front-end equipment relative to the frame of the harvester 100. For example, sensor 993 can sense the height of the header 102 above the ground. Machine sensor 982 may also include a front-end equipment (e.g., header) orientation sensor 995. Sensor 995 can sense the orientation of the header 102 relative to the harvester 100 or relative to the ground. Machine sensor 982 may include a stability sensor 997. Stability sensor 997 senses vibrational or bouncing motions (and amplitude) of the harvester 100. Machine sensor 982 may also include a residue setup sensor 999 configured to sense whether the harvester 100 is configured to shred residue, create a stockpile, or otherwise process residue. Machine sensor 982 may include a cleaning device fan speed sensor 951 that senses the speed of the cleaning fan 120. Machine sensor 982 may include a recess gap sensor 953 for sensing the gap between the rotor 112 and the recess 114 on the harvester 100. Machine sensor 982 may include a husk sieve gap sensor 955 for sensing the size of the openings in the husk sieve 122. Machine sensor 982 may include a threshing rotor speed sensor 957 for sensing the rotor speed of the rotor 112. Machine sensor 982 may include a rotor pressure sensor 959 for sensing the pressure used to drive the rotor 112. Machine sensor 982 may include a sieve gap sensor 961 for sensing the size of the openings in the sieve 124. Machine sensor 982 may include a MOG humidity sensor 963 for sensing the humidity level of the MOG passing through the harvester 100. Machine sensor 982 may include a machine orientation sensor 965 for sensing the orientation of the harvester 100. Machine sensor 982 may include a material feed rate sensor 967 for sensing the material feed rate as the material travels through the feed chamber 106, the clean grain elevator 130, or other locations within the harvester 100. Machine sensor 982 may include a biomass sensor 969 that senses biomass traveling through feed chamber 106, separator 116, or other locations within harvester 100. Machine sensor 982 may include a fuel consumption sensor 971 that senses the rate of fuel consumption of harvester 100 over time. Machine sensor 982 may include a power usage sensor 973 that senses power utilization in harvester 100 (such as which subsystems are using power), or the rate at which subsystems are using power, or the distribution of power among subsystems in harvester 100. Machine sensor 982 may include a tire pressure sensor 977 that senses the inflation pressure in the tires 144 of harvester 100. Machine sensor 982 may include a variety of other machine performance sensors or machine characteristic sensors (as shown in box 975).The machine performance sensor and machine characteristic sensor 975 can sense the machine performance or characteristics of the agricultural harvester 100.

[0088] While crop material is being processed by the agricultural harvester 100, the post-harvest material property sensor 984 can sense the characteristics of the cut crop material. Crop properties may include things such as crop type, crop moisture content, grain quality (e.g., broken grain), MOG level, grain composition (e.g., starch and protein), MOG moisture content, and other crop material properties. Other sensors can sense straw “toughness,” corn and ear adhesion, and other characteristics that can be beneficially used to control processing for better grain capture, reduced grain damage, lower power consumption, reduced grain loss, and so on.

[0089] The 985 field and soil property sensor can sense the characteristics of fields and soils. Field and soil properties may include soil moisture, soil compaction, presence and location of waterlogging, soil type, and other soil and field characteristics.

[0090] The environmental characteristic sensor 987 can sense one or more environmental characteristics. Environmental characteristics may include things such as wind direction and speed, precipitation, fog, dust level or other obfuscation or other environmental features.

[0091] This discussion continues with an example as described below, in which agricultural sensor 336 is the above... Figure 4B One or more of the sensors listed herein. It should be understood that these are merely examples, and other examples of agricultural sensors 336 are also considered herein. Figure 4A As shown, the exemplary predictive model generator 210 includes a priori operation-to-sensed feature model generator 342. In other examples, the predictive model generator 210 may include... Figure 4A The components shown in the examples are additional, fewer, or different from the components. Therefore, in some examples, the predictive model generator 210 may also include other items 348, which may include other types of predictive model generators to generate other types of models.

[0092] The prior operation to sensed characteristic model generator 342 identifies the relationship between the characteristic at the geographic location corresponding to the location where the agricultural characteristic is sensed by the agricultural sensor 336 and the prior operation value from the prior operation map 332 corresponding to the same location in the field corresponding to the agricultural characteristic. Based on this relationship established by the model generator 342, the model generator 342 generates a predictive model 350. The predictive map generator 212 uses the predictive model 350 to predict the agricultural characteristic at the same location in the field based on the georeferenced prior operation values ​​contained in the prior operation map 332 at different locations in the field.

[0093] In view of the above, the predictive model generator 210 is operable to generate multiple predictive models, such as one or more of the predictive models generated by model generators 342 and 348. In another example, two or more of the above-described predictive models can be combined into a single predictive model that predicts one or more agricultural characteristics based on different values ​​at different locations in the field represented in one or more prior operation diagrams 332. Any one of these predictive models or a combination thereof is generated by... Figure 4A The model 350 is represented by the same symbol.

[0094] A predictive model 350 is provided to a predictive graph generator 212. In the example of Figure 4, the predictive graph generator 212 includes a characteristic graph generator 356. In other examples, the predictive graph generator 212 may include additional, fewer, or different graph generators. Thus, in some examples, the predictive graph generator 212 may include additional items 358, which may include other types of graph generators to generate predictive graphs of other types of agricultural characteristics.

[0095] Characteristic map generator 356 receives predictive model 350, which predicts agricultural characteristics based on values ​​in one or more of the prior operation maps 332. For example, characteristic map generator 356 generates a map of estimated yield based on predictive model 350, which defines the relationship between historical yields of harvesting operations in the past year and yields in the current year.

[0096] Predictive map generator 212 outputs one or more predictive maps 360 predicting one or more agricultural characteristics. Each of the predictive maps 360 predicts a corresponding agricultural characteristic at a different location in the field. Each of the generated predictive maps 360 can be provided to control region generator 213, control system 214, or both. Control region generator 213 can generate control regions and incorporate these control regions into the functional predictive maps 360. One or more functional predictive maps can be provided to control system 214, which generates control signals based on one or more functional predictive maps to control one or more controllable subsystems 216.

[0097] Figure 5 This is a flowchart illustrating an example of the operation of the predictive model generator 210 and the predictive graph generator 212 in generating the predictive model 350 and the predictive graph 360. At block 362, the predictive model generator 210 and the predictive graph generator 212 receive one or more prior operation graphs. At block 364, the processing system 338 receives one or more sensor signals from the sensor 336.

[0098] At box 372, processing system 338 processes one or more received sensor signals to generate data indicating one or more agricultural characteristics.

[0099] At box 382, ​​the predictive model generator 210 also obtains the geographic location corresponding to the sensor data. For example, the predictive model generator 210 can obtain the geographic location from the geographic location sensor 204 and determine the precise geographic location at the point where the sensor data 340 was captured or derived based on machine latency, machine speed, sensor calibration, etc.

[0100] At box 384, predictive model generator 210 generates one or more predictive models, such as predictive model 350, that model the relationship between values ​​obtained from a prior information map, such as prior information map 258, and agricultural or related characteristics sensed by field sensor 208. For example, predictive model generator 210 may generate a predictive model that models the relationship between previous tillage operation characteristics (e.g., tillage depth) and sensed characteristics including power use indicated by sensor data obtained from field sensor 208. Or, for example, predictive model generator 210 may generate a predictive model that models the relationship between weed characteristics sensed during spraying operations and sensed characteristics including biomass indicated by sensor data obtained from field sensor 208. Or, for example, predictive model generator 210 may generate a predictive model that models the relationship between fungicide application characteristics sensed during spraying operations and sensed characteristics including humidity indicated by sensor data obtained from field sensor 208. Alternatively, for example, the predictive model generator 210 can generate a predictive model that models the relationship between nutrient application characteristics sensed during spraying or dry chemical application operations and yield or crop composition, including sensor data obtained from field sensors 208. Alternatively, for example, the predictive model generator 210 can generate a predictive model that models the relationship between baling characteristics sensed during removal operations and sensed characteristics including yield or crop health, including sensor data obtained from field sensors 208.

[0101] At box 386, a predictive model, such as predictive model 350, is provided to predictive graph generator 212, which generates predictive graph 360 based on prior operational graph 332 and predictive model 350. This predictive graph maps predictive characteristics. For example, in some examples, predictive graph 360 predicts power use / demand for various subsystems. Predictive graph 360 can be generated during agricultural operations. Thus, predictive graph 360 is generated while agricultural operations are in progress, as an agricultural harvester moves through the field where agricultural operations are being performed.

[0102] At block 394, the predictive graph generator 212 outputs a predictive graph 360. At block 391, the predictive graph generator 212 outputs a predictive graph to be presented to operator 260 and to facilitate possible interaction by operator 260. At block 393, the predictive graph generator 212 can configure the graph for use by control system 214. At block 395, the predictive graph generator 212 can also provide graph 360 to control region generator 213 to generate a control region. At block 397, the predictive graph generator 212 also configures graph 360 in other ways. The predictive graph 360 (with or without a control region) is provided to control system 214. At block 396, control system 214 generates control signals based on predictive graph 360 to control controllable subsystem 216.

[0103] In an example where the control system 214 receives a functional predictive map or a functional predictive map with added control areas, the path planning controller 234 controls the steering subsystem 252 to turn the harvester 100. In another example where the control system 214 receives a functional predictive map or a functional predictive map with added control areas, the residue system controller 244 controls the residue subsystem 138. In another example where the control system 214 receives a functional predictive map or a functional predictive map with added control areas, the setting controller 232 controls the threshing settings of the threshing machine 110. In another example where the control system 214 receives a functional predictive map or a functional predictive map with added control areas, the setting controller 232 or another controller 246 controls the material handling subsystem 125. In another example where the control system 214 receives a functional predictive map or a functional predictive map with added control areas, the setting controller 232 controls the crop cleaning subsystem 118. In another example where the control system 214 receives a function predictive diagram or a function predictive diagram with added control areas, the machine cleaning controller 245 controls the machine cleaning subsystem 254 on the harvester 100. In another example where the control system 214 receives a function predictive diagram or a function predictive diagram with added control areas, the communication system controller 229 controls the communication system 206. In another example where the control system 214 receives a function predictive diagram or a function predictive diagram with added control areas, the operator interface controller 231 controls the operator interface mechanism 218 on the harvester 100. In another example where the control system 214 receives a function predictive diagram or a function predictive diagram with added control areas, the platform position controller 242 controls the machine / header actuator 248 to control the platform on the harvester 100. In another example where the control system 214 receives a function predictive diagram or a function predictive diagram with added control areas, the conveyor belt controller 240 controls the machine / header actuator 248 to control the conveyor belt on the harvester 100. In another example where the control system 214 receives a functional predictive map or a functional predictive map with added control areas, other controllers 246 control other controllable subsystems 256 on the agricultural harvester 100.

[0104] Therefore, it can be seen that this system employs a priori information graph, which maps agricultural characteristics derived from previous operations (e.g., tillage operations, spraying operations, previous harvesting operations, etc.). The system also uses one or more field sensors to sense data from field sensors indicating agricultural characteristics and generates a model that models the relationship between the characteristics or related characteristics sensed by the field sensors and the characteristics mapped in the priori information graph. Thus, this system uses the model, field data, and priori information graph to generate a functional predictive graph, and the generated functional predictive graph can be configured for use by the control system, presented to local or remote operators or other users, or some combination thereof. For example, the control system can use the graph to control one or more systems of a combine harvester.

[0105] Figure 6 A block diagram illustrating an example of a control region generator 213 is shown. The control region generator 213 includes a work machine actuator (WMA) selector 486, a control region generation system 488, and a regime region generation system 490. The control region generator 213 may also include other items 492. The control region generation system 488 includes a control region standard identifier component 494, a control region boundary definition component 496, a target setting identifier component 498, and other items 520. The regime region generation system 490 includes a dynamic region standard identifier component 522, a dynamic region boundary definition component 524, a set resolver identifier component 526, and other items 528. Before describing the overall operation of the control region generator 213 in more detail, a brief description of some of the items in the control region generator 213 and their corresponding operations will first be provided.

[0106] The agricultural harvester 100 or other operating machine may have various types of controllable actuators that perform different functions. The controllable actuators on the agricultural harvester 100 or other operating machine are collectively referred to as operating machine actuators (WMAs). Each WMA can be controlled independently based on values ​​on a function predictive map, or WMAs can be controlled in groups based on one or more values ​​on the function predictive map. Therefore, the control region generator 213 can generate control regions corresponding to each individual controllable WMA, or corresponding to groups of WMAs with coordinated control.

[0107] WMA selector 486 selects the WMA or WMA group for which a corresponding control region is to be generated. Control region generation system 488 then generates a control region for the selected WMA or WMA group. For each WMA or WMA group, different criteria can be used to identify the control region. For example, for a WMA, the WMA response time can be used as a criterion for defining the boundary of the control region. In another example, wear characteristics (e.g., the degree of wear of a particular actuator or mechanism due to its movement) can be used as a criterion for identifying the boundary of the control region. Control region criterion identifier component 494 identifies the specific criterion that will be used to define the control region for the selected WMA or WMA group. Control region boundary definition component 496 processes the values ​​on the function predictive graph in the analysis to define the boundary of the control region on the function predictive graph based on the values ​​on the function predictive graph in the analysis and based on the control region criteria of the selected WMA or WMA group.

[0108] The target setting identifier component 498 sets the value of the target setting, which will be used to control the WMA or WMA group in different control regions. For example, if the selected WMA is the propulsion system 250 and the functional predictive map in the analysis is the functional predictive speed map 438, then the target setting in each control region can be a target speed setting based on the speed values ​​contained in the functional predictive speed map 238 in the identified control region.

[0109] In some examples, when controlling the harvester 100 based on its current or future position, multiple target settings are possible for the WMA at a given position. In this case, the target settings may have different values ​​and may compete with each other. Therefore, it is necessary to resolve the target settings so that only a single target setting is used to control the WMA. For example, in the case where the WMA is an actuator controlled in the propulsion system 250 to control the speed of the harvester 100, there may be multiple different competing sets of criteria, which are considered by the control region generation system 488 when identifying the control region and the target settings of the WMA selected in the control region. For example, different target settings for controlling machine speed may be generated based on, for example, detected or predicted feed rate values, detected or predicted fuel efficiency values, detected or predicted grain loss values, or combinations of these values. However, at any given time, the harvester 100 cannot travel on the ground at multiple speeds simultaneously. Instead, at any given time, the harvester 100 travels at a single speed. Therefore, one of the competing target settings is selected to control the speed of the agricultural harvester 100.

[0110] Therefore, in some examples, the dynamic region generation system 490 generates dynamic regions to resolve multiple different competing objective settings. The dynamic region criterion identification component 522 identifies the criteria used to establish dynamic regions on the selected WMA or WMA group on the functional predictive map in the analysis. Some criteria that can be used to identify or define dynamic regions include, for example, crop type or crop species based on a planting map or another source of crop type or crop species, weed type, weed intensity, soil type, or crop state (such as whether the crop is lodged, partially lodged, or upright). Just as each WMA or WMA group may have a corresponding control region, different WMAs or WMA groups may have corresponding dynamic regions. The dynamic region boundary definition component 524 identifies the boundaries of the dynamic regions on the functional predictive map in the analysis based on the dynamic region criteria identified by the dynamic region criterion identification component 522.

[0111] In some examples, dynamic regions may overlap. For instance, a crop type dynamic region may partially or completely overlap with a crop state dynamic region. In such examples, different dynamic regions can be assigned priority levels such that, in the case of two or more overlapping dynamic regions, the dynamic region assigned a higher priority level or importance takes precedence over the dynamic region with a lower priority level or importance. The priority levels of dynamic regions can be set manually or automatically using rule-based systems, model-based systems, or other systems. As an example, in the case of an overlap between a lodged crop dynamic region and a crop type dynamic region, the lodged crop dynamic region can be assigned greater importance in the priority level than the crop type dynamic region, thus giving priority to the lodged crop dynamic region.

[0112] Furthermore, for a given WMA or WMA group, each dynamic region may have a unique setting resolver. The setting resolver identifier component 526 identifies a specific setting resolver for each dynamic region identified on the functional predictive graph in the analysis, and identifies a specific setting resolver for the selected WMA or WMA group.

[0113] Once a setting resolver is identified for a specific dynamic region, it can be used to resolve competing target settings, in which more than one target setting is identified based on the control region. Different types of setting resolvers can take different forms. For example, a setting resolver for each dynamic region may include a human-selected resolver, in which the competing target setting is presented to an operator or other user for resolution. In another example, the setting resolver may include a neural network or other artificial intelligence or machine learning system. In this case, the setting resolver can resolve competing target settings based on a predicted or historical quality metric corresponding to each of the different target settings. As an example, an increased vehicle speed setting may reduce harvesting time and corresponding time-based labor and equipment costs, but may increase grain loss. A decreased vehicle speed setting may increase harvesting time and corresponding time-based labor and equipment costs, but may reduce grain loss. When grain loss or harvesting time is selected as a quality metric, given two competing vehicle speed setting values, the predicted or historical value of the selected quality metric can be used to resolve the speed setting. In some cases, setting the parser can be a set of threshold rules that can be used to replace or supplement dynamic regions. Examples of threshold rules can be expressed as follows:

[0114] If the predicted biomass value within 20 feet of the header of the agricultural harvester 100 is greater than x kg (where x is the selected or predetermined value), the target setting value selected based on the feed rate rather than other competing target settings is used; otherwise, the target setting value based on grain loss rather than other competing target settings is used.

[0115] A setting parser can be a logical component that executes logical rules when identifying a target setting. For example, a setting parser can parse a target setting while attempting to minimize harvest time, minimize total harvest cost, or maximize harvested grain, or other variables calculated as a function of different candidate target settings. Harvesting time can be minimized when the amount harvested is reduced to or below a selected threshold. Total harvest cost can be minimized when it is reduced to or below a selected threshold. Harvested grain can be maximized when the amount harvested is increased to or above a selected threshold.

[0116] Figure 7 This is a flowchart illustrating an example of the operation of the control region generator 213 when it receives a graph for region processing (e.g., a graph in analysis) and generates control regions and dynamic regions.

[0117] At box 530, control region generator 213 receives the graph in the analysis for processing. In one example, as shown in box 532, the graph in the analysis is a functional predictive graph. For example, the graph in the analysis could be one of functional predictive graphs 436, 437, 438, or 440. Box 534 indicates that the graph in the analysis could also be other graphs.

[0118] At box 536, WMA selector 486 selects the WMA or WMA group for which a control region will be generated on the graph in the analysis. At box 538, control region criterion identification component 494 obtains the control region definition criteria for the selected WMA or WMA group. Box 540 indicates an example where the control region criteria are or include the wear characteristics of the selected WMA or WMA group. Box 542 indicates an example where the control region definition criteria are or include the amplitude and variation of input source data, such as the amplitude and variation of values ​​on the graph in the analysis or the amplitude and variation of inputs from various field sensors 208. Box 544 indicates an example where the control region definition criteria are or include physical machine characteristics, such as the physical dimensions of the machine, the speed of operation of different subsystems, or other physical machine characteristics. Box 546 indicates an example where the control region definition criteria are or include the responsiveness of the selected WMA or WMA group when a setpoint for a new command is reached. Box 548 indicates an example where the control region definition criteria are or include machine performance metrics. Box 550 indicates an example where the control area definition criterion is or includes operator preferences. Box 552 indicates an example where the control area definition criterion is also or includes other items. Box 549 indicates an example where the control area definition criterion is time-based, meaning that the harvester 100 will not cross the boundary of the control area until a selected amount of time has elapsed since the harvester 100 entered the specific control area. In some cases, the selected amount of time may be a minimum amount of time. Therefore, in some cases, the control area definition criterion can prevent the harvester 100 from crossing the boundary of the control area until at least the selected amount of time has elapsed. Box 551 indicates an example where the control area definition criterion is based on a selected size value. For example, a control area definition criterion based on a selected size value can exclude the definition of control areas smaller than the selected size. In some cases, the selected size may be a minimum size.

[0119] At box 554, the dynamic zone standard identification component 522 obtains the dynamic zone definition standard of the selected WMA or WMA group. Box 556 indicates an example where the dynamic zone definition standard is based on manual input from operator 260 or another user. Box 558 shows an example where the dynamic zone definition standard is based on crop type or crop variety. Box 560 shows an example where the dynamic zone definition standard is based on weed type or weed intensity or both. Box 562 shows an example where the dynamic zone definition standard is based on or includes crop status. Box 564 indicates an example where the dynamic zone definition standard is also or includes other standards.

[0120] At box 566, the control region boundary definition component 496 generates the boundary of the control region on the graph in the analysis based on control region criteria. The dynamic region boundary definition component 524 generates the boundary of the dynamic region on the graph in the analysis based on dynamic region criteria. Box 568 indicates an example in which the boundaries of the control region and the dynamic region are identified. Box 570 shows that the target setting identifier component 498 identifies the target settings for each in the control region. The control region and the dynamic region can also be generated in other ways, and this is indicated by box 572.

[0121] At box 574, the set parser identifier component 526 identifies the set parser for the selected WMA in each dynamic region defined by the dynamic region boundary definition component 524. As discussed above, the dynamic region parser can be a human parser 576, an artificial intelligence or machine learning system parser 578, a parser 580 based on the predicted quality or historical quality of each competing objective setting, a rule-based parser 582, a performance criterion-based parser 584, or another parser 586.

[0122] At box 588, WMA selector 486 determines if there are more WMAs or WMA groups to process. If there are additional WMAs or WMA groups to process, processing returns to box 436, where the next WMA or WMA group for which a control region and dynamic region are defined is selected. When no additional WMAs or WMA groups remain for which a control region or dynamic region is to be generated, processing moves to box 590, where control region generator 213 outputs a graph for each WMA or WMA group, containing a control region, target settings, dynamic region, and settings resolver. As discussed above, the output graph can be presented to operator 260 or another user; the output graph can be provided to control system 214; or the output graph can be output in other ways.

[0123] Figure 8An example of the operation of control system 214 in controlling agricultural harvester 100 based on a map output by control region generator 213 is shown. Thus, at box 592, control system 214 receives a map of the work site. In some cases, this map may be a functional predictive map that can include control regions and dynamic regions (as shown in box 594). In some cases, the received map may be a functional predictive map that excludes control regions and dynamic regions. Box 596 indicates an example where the received work site map may be a priori information map with control regions and dynamic regions identified thereon. Box 598 indicates an example where the received map may include multiple different maps or multiple different layers. Box 610 indicates an example where the received map may also take other forms.

[0124] At box 612, control system 214 receives sensor signals from geolocation sensor 204. Sensor signals from geolocation sensor 204 may include data indicating the geolocation 614 of harvester 100, the speed 616 of harvester 100, the heading 618 of harvester 100, or other information 620. At box 622, area controller 247 selects a dynamic area, and at box 624, area controller 247 selects a control area on a map based on the geolocation sensor signals. At box 626, area controller 247 selects a WMA or WMA group to be controlled. At box 628, area controller 247 obtains one or more target settings for the selected WMA or WMA group. The target settings obtained for the selected WMA or WMA group can come from a variety of different sources. For example, box 630 shows an example where one or more of the target settings for the selected WMA or WMA group are based on inputs from a control area on a map from the work site. Box 632 illustrates an example where one or more target settings are obtained from manual input by operator 260 or another user. Box 634 illustrates an example where target settings are obtained from field sensor 208. Box 636 illustrates an example where one or more target settings are obtained from one or more sensors on other machines operating simultaneously with agricultural harvester 100 in the same field, or from one or more sensors on machines that have previously operated in the same field. Box 638 illustrates an example where target settings are also obtained from other sources.

[0125] At box 640, the zone controller 247 accesses the settings resolver for the selected dynamic zone and controls the settings resolver to parse the competing target settings into a resolved target setting. As discussed above, in some cases, the settings resolver may be a human resolver, in which case the zone controller 247 controls the operator interface mechanism 218 to present the competing target settings to the operator 260 or another user for resolution. In some cases, the settings resolver may be a neural network or other artificial intelligence or machine learning system, and the zone controller 247 submits the competing target settings to the neural network, artificial intelligence, or machine learning system for selection. In some cases, the settings resolver may be based on predicted or historical quality metrics, threshold rules, or logic components. In any of these later examples, the zone controller 247 executes the settings resolver to obtain the resolved target setting based on predicted or historical quality metrics, threshold rules, or, when using logic components.

[0126] At box 642, if the area controller 247 has identified the resolved target setting, it provides the resolved target setting to other controllers in the control system 214. These controllers generate control signals based on the resolved target setting and apply the control signals to the selected WMA or WMA group. For example, if the selected WMA is a machine or header actuator 248, the area 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 box 644, if an additional WMA or additional WMA group is to be controlled at the current geographic location of the agricultural harvester 100 (as detected at box 612), the process returns to box 626, where the next WMA or WMA group is selected. The process represented by boxes 626 to 644 continues until all WMAs or WMA groups to be controlled at the current geographic location of the harvester 100 have been resolved. If no additional WMAs or WMA groups remain to be controlled at the current geographic location of the harvester 100, the process proceeds to box 646, where the area controller 247 determines whether an additional control area to be considered exists in the selected dynamic area. If an additional control area to be considered exists, the process returns to box 624, where the next control area is selected. If no additional control area needs to be considered, the process proceeds to box 648, where it is determined whether there is an additional dynamic area to be considered. The area controller 247 determines whether there is an additional dynamic area to be considered. If there is an additional dynamic area to be considered, the process returns to box 622, where the next dynamic area is selected.

[0127] At box 650, the area controller 247 determines whether the operation being performed by the harvester 100 has been completed. If not, the area controller 247 determines whether control area criteria have been met to continue processing, as shown in box 652. For example, as mentioned above, control area definition criteria may include criteria defining when the harvester 100 can cross the control area boundary. For example, whether the harvester 100 can cross the control area boundary may be defined by a selected time period, meaning that the harvester 100 is prevented from crossing the boundary until the selected amount of time has elapsed. In this case, at box 652, the area controller 247 determines whether the selected time period has elapsed. Additionally, the area controller 247 can perform processing continuously. Therefore, the area controller 247 does not wait for any specific time period before continuing to determine whether the operation of the harvester 100 has been completed. At box 652, the area controller 247 determines that it is time to continue processing, and then processing continues at box 612, where the area controller 247 again receives input from the geolocation sensor 204. It should also be understood that the area controller 247 can use a multiple-input multiple-output controller to control the WMA and WMA group simultaneously, rather than controlling the WMA and WMA group sequentially.

[0128] Figure 9 This is a block diagram illustrating an 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 systems 656, a voice processing system 658, and a motion signal generator 660. The operator input command processing system 654 includes a voice processing 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 voice processing system 658 includes a trigger detector 672, a recognition unit 674, a synthesis unit 676, a natural language understanding system 678, a dialogue management system 680, and other items 682. The motion signal generator 660 includes a visual control signal generator 684, an audio control signal generator 686, a tactile control signal generator 688, and other items 690. Figure 9 Before processing various operator interface actions, the example operator interface controller 231 shown in the figure first provides a brief description of some of the items in the operator interface controller 231 and their associated operations.

[0129] The operator input command processing system 654 detects operator input on the operator interface mechanism 218 and processes these command inputs. The voice processing system 662 detects voice input and processes interaction with the voice processing system 658 to process voice command inputs. The touch gesture processing system 664 detects touch gestures on touch-sensitive elements in the operator interface mechanism 218 and processes these command inputs.

[0130] Other controller interaction system 656 handles interactions with other controllers in control system 214. Controller input processing system 668 detects and processes inputs from other controllers in control system 214, and controller output generator 670 generates outputs and provides these outputs to other controllers in control system 214. Voice processing system 658 recognizes voice input, determines the meaning of these inputs, and provides outputs indicating the meaning of the spoken input. For example, voice processing system 658 can recognize voice input from operator 260 as a setting change command, where operator 260 is instructing control system 214 to change the settings of controllable subsystem 216. In such an example, voice processing system 658 recognizes the content of the spoken command, identifies the meaning of the command as a setting change command, and provides the meaning of the input back to voice processing system 662. Voice processing system 662 then interacts with controller output generator 670 to provide command outputs to the appropriate controllers in control system 214 to complete the spoken setting change command.

[0131] The voice processing system 658 can be invoked in various ways. For example, in one example, the voice processing system 662 continuously provides input from a microphone (as part of the operator interface mechanism 218) to the voice processing system 658. The microphone detects speech from the operator 260, and the voice processing system 662 provides the detected speech to the voice processing system 658. A trigger detector 672 detects a trigger indicating that the voice processing system 658 has been invoked. In some cases, when the voice processing system 658 receives continuous voice input from the voice processing system 662, the voice recognition unit 674 performs continuous speech recognition on all speech uttered 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 the recognition of a selected spoken word (referred to as a wake-up word). In such an example, when the recognition unit 674 recognizes the wake-up word, the recognition unit 674 provides an indication to the trigger detector 672 that the wake-up word has been recognized. Trigger detector 672 detects that voice processing system 658 has been invoked or triggered by a wake word. In another example, voice processing system 658 may be invoked by operator 260 actuating an actuator on the user interface mechanism, such as by touching an actuator on a touch-sensitive display, by pressing a button, or by providing another trigger input. In such an example, trigger detector 672 can detect that voice processing system 658 has been invoked when a trigger input via the user interface mechanism is detected. Trigger detector 672 may also detect that voice processing system 658 has been invoked in other ways.

[0132] Once the speech processing system 658 is invoked, speech input from operator 260 is provided to the speech recognition unit 674. The speech recognition unit 674 recognizes linguistic elements in the speech input, such as words, phrases, or other linguistic units. The natural language understanding system 678 identifies the meaning of the recognized speech. This meaning can be any of the following: natural language output, command output identifying a command reflected in the recognized speech, value output identifying a value in the recognized speech, or a variety of other outputs reflecting an understanding of the recognized speech. For example, more generally, the natural language understanding system 678 and the speech processing system 568 can understand the meaning of speech recognized in the environment of the agricultural harvester 100.

[0133] In some examples, the voice processing system 658 can also generate output for navigating operator 260 through user experience based on voice input. For example, the dialogue management system 680 can generate and manage dialogues with users to identify what the user wants to do. The dialog box can disambiguate user commands, identify one or more specific values ​​required to execute user commands, or obtain other information from or provide other information to the user, or both. The synthesis component 676 can generate speech synthesis, which can be presented to the user through an audio operator interface mechanism such as a speaker. Therefore, dialogues managed by the dialogue management system 680 can be exclusively verbal dialogues or a combination of visual and verbal dialogues.

[0134] Motion signal generator 660 generates motion signals to control operator interface mechanism 218 based on outputs from one or more of the operator input command processing system 654, other controller interaction system 656, and voice processing system 658. Visual control signal generator 684 generates control signals to control visual items in operator interface mechanism 218. Visual items may be lights, displays, warning indicators, or other visual items. Audio control signal generator 686 generates outputs to control audio elements of operator interface mechanism 218. Audio elements include speakers, audible alarm mechanisms, horns, or other audible elements. Tactile control signal generator 688 generates control signals that are output to control tactile elements of operator interface mechanism 218. Tactile elements include vibratory elements that can be used to make vibrations, such as an operator's seat, steering wheel, pedals, or joystick used by the operator. Tactile elements may include tactile feedback or force feedback elements that provide tactile or force feedback to the operator through the operator interface mechanism. Tactile elements may also include a wide variety of other tactile elements.

[0135] Figure 10 This is a flowchart illustrating an example of the operation of the operator interface controller 231 when generating an operator interface display on an operator interface mechanism 218 that may include a touch-sensitive display screen. Figure 10 An example of how the operator interface controller 231 can detect and process operator interactions with the touch-sensitive display is also shown.

[0136] At box 692, operator interface controller 231 receives a graph. Box 694 indicates that the graph is an example of a functional predictive graph, while box 696 indicates that the graph is an example of another type of graph. At box 698, operator interface controller 231 receives input from geolocation sensor 204 identifying the geolocation of harvester 100. As shown in box 700, the input from geolocation sensor 204 may include the heading and position of harvester 100. Box 702 indicates that the input from geolocation sensor 204 includes an example of the speed of harvester 100, and box 704 indicates that the input from geolocation sensor 204 includes an example of other items.

[0137] At box 706, the vision 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 part of the field represented by the received map. Box 708 indicates that the displayed field may include a current position marker showing the current position of the harvester 100 relative to the field. Box 710 indicates an example where the displayed field includes a next work unit marker identifying the next work unit (or area on the field) in which the harvester 100 will operate. Box 712 indicates an example where the displayed field includes an upcoming area display showing areas not yet processed by the harvester 100, while box 714 indicates an example where the displayed field includes a previously visited display showing areas of the field after the harvester 100 has processed. Box 716 indicates an example where the displayed field shows various characteristics of the field having a geographic reference location on the map. For example, if the received map is a yield map, the displayed fields can show the different yield characteristics present in georeferenced fields within the displayed fields. Mapped characteristics can be shown in previously visited areas (as shown in box 714), upcoming areas (as shown in box 712), and the next work unit (as shown in box 710). Box 718 indicates that the fields displayed therein also include examples of other items.

[0138] Figure 11 This illustration shows an example of a user interface display 720 that can be generated on a touch-sensitive display screen. In other embodiments, the user interface display 720 can be generated on other types of displays. The touch-sensitive display screen can be installed in the operator's cab of an agricultural harvester 100, on a mobile device, or elsewhere. (Continuing the description...) Figure 10 Before the flowchart shown, the user interface display 720 will be described.

[0139] exist Figure 11In the example shown, the user interface display 720 illustrates a touch-sensitive display including display features for operating a microphone 722 and a speaker 724. Therefore, the touch-sensitive display can be communicatively coupled to the microphone 722 and the speaker 724. Box 726 indicates that the touch-sensitive display may include various user interface control actuators, such as buttons, keypads, soft keypads, links, icons, switches, etc. Operator 260 can actuate the user interface control actuators to perform various functions.

[0140] exist Figure 11 In the example shown, the user interface display 720 includes a field display portion 728 that displays at least a portion of the field in which the harvester 100 is operating. The field display portion 728 is shown having a current position marker 708 corresponding to the current position of the 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 multiple portions of the field display portion 728, or pan or scroll the field display portion 728 to display different portions of the field. The next work unit 730 is shown as the field area directly in front of the current position marker 708 of the harvester 100. The current position marker 708 can also be configured to identify the direction of travel of the harvester 100, the speed of travel of the harvester 100, or both. Figure 13 In the image, the shape of the current position marker 708 provides an indication of the orientation of the agricultural harvester 100 in the field, which can be used as an indication of the direction of travel of the agricultural harvester 100.

[0141] The size of the next work unit 730 marked on the field display section 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 travel speed of the harvester 100. Therefore, the area of ​​the next work unit 730 may be larger when the harvester 100 is traveling faster compared to when the harvester 100 is traveling slower. In another example, the size of the next work unit 730 can vary based on the size of the harvester 100, including equipment on the harvester 100 (e.g., 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 section 728 is also shown displaying previously visited areas 714 and upcoming areas 712. The previously visited area 714 represents areas that have already been harvested, while the upcoming area 712 represents areas that still need to be harvested. The field display section 728 is also shown displaying different characteristics of the field. Figure 11In the example shown, the plot being displayed is a predictive loss plot, such as a functional predictive loss plot 420. Therefore, multiple different yield markers are displayed on the field display section 728. A set of yield display markers 732 is shown in the already visited area 714. Another set of yield display markers 732 is shown in the upcoming area 712, and another set of yield display markers 732 is shown in the next work unit 730. Figure 11 The yield display markers 732 are shown to consist of different symbols that indicate areas of similar yield. In the example shown in Figure 3, the ! symbol indicates a high-yield area; the * symbol indicates a medium-yield area; and the # symbol indicates a low-yield area. 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 uses 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 yield display mark 732. In some cases, each location of the field may have an associated display mark. Thus, in some cases, a display mark can be set at each location of the field display section 728 to identify the nature of the characteristics mapped for each particular location of the field. Therefore, this disclosure includes providing display marks, such as yield display mark 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, the display mark 732 is used 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. In some cases, each location in the field can have a display mark associated with that location. Therefore, in some cases, display marks can be provided at each location of the field display section 728 to identify the nature of the characteristic mapped for each particular location in the field. Thus, this disclosure includes providing display marks at one or more locations on the field display section 728, for example, loss level display mark 732 (as in...). 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.

[0142] In other examples, the graph being displayed can be one or more of the graphs described herein, including infographics, prior infographics, functional predictive graphs (e.g., predictive graphs or predictive 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 one or more of the graphs being displayed.

[0143] 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.

[0144] The actuators and display markers 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 for three different production categories corresponding to 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.

[0145] like Figure 11As 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 select a location, such as the current location of the harvester, or another location on the field specified by the operator, and then add information indicating the yield found at said 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 location as a high-yield location for the harvester 100. When operator 260 touches button 742, touch gesture processing system 664 identifies the location as a medium-yield location for the harvester 100. When operator 260 touches button 744, touch gesture processing system 664 identifies the location as a low-yield location for the harvester 100. 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 yield 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.

[0146] 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 11 In the case of production output, the symbol corresponding to the output is shown. The display portion 738 also includes an interactor specifier display portion, generally indicated by reference numeral 745. The interactor specifier display portion 745 includes a specifier column 748 that displays identifier values ​​or characteristics (in...). Figure 11 In the case of yield, the category of the designator (which may be a text designator or other designator). Without limitation, the symbols in the symbol column 746 and the designators in the designator column 748 may include any display features, such as different colors, shapes, patterns, intensities, text, icons or other display features, and may be customized through the interaction of the operator of the agricultural harvester 100.

[0147] Display section 738 also includes an interactive value display section, generally indicated by reference numeral 747. The interactive value display section 747 includes a value display column 750 displaying a selected value. The selected value corresponds to a characteristic or value, or both, being tracked or displayed on the field display section 728. The selected value can be selected by the operator of the agricultural harvester 100. The selected value in the value display column 750 defines a range of values, or other values ​​(e.g., predicted values), by which they will be categorized. Therefore, in Figure 11 In the examples, predicted or measured yields of 185 bushels / acre or higher are classified as "high yield," while predicted or measured yields of 100 bushels / acre or lower are classified as "low yield." In some examples, the selected values ​​can include a range, such that predicted or measured values ​​within the selected range are classified under the appropriate specifier. Figure 11 As shown, "medium yield" includes a range of 126 bushels / acre to 154 bushels / acre, such that predicted or measured yields falling within the 10% range of 140 bushels / acre are classified as "medium yield". The selected values ​​in column 750 can be adjusted by the operator of the agricultural harvester 100. In one example, operator 260 can select a specific portion of the field display section 728 for which values ​​in column 750 will be displayed. Therefore, the values ​​in column 750 can correspond to values ​​in display sections 712, 714, or 730.

[0148] Display section 738 also includes an interactive threshold display section, generally indicated by reference numeral 749. The interactive threshold display section 749 includes a threshold display column 752 that displays action thresholds. The action threshold in column 752 can be a threshold corresponding to a selected value in value display column 750. If the predicted or measured value of a tracked or displayed characteristic meets the corresponding action threshold in threshold display column 752, control system 214 takes the action identified in column 754. In some cases, the predicted or measured value can satisfy the corresponding action threshold by meeting or exceeding it. In one example, operator 260 can select a threshold, for example, to change the threshold by touching a threshold in threshold display column 752. Once selected, operator 260 can change the threshold. The threshold in column 752 can be configured such that a specified action is performed when the measured or predicted value of the characteristic exceeds, equals, or is less than the threshold. In some cases, a threshold can represent a range of values, or a range of deviations from the values ​​selected in column 750, so that predicted or measured characteristic values ​​that meet or fall within that range satisfy the threshold. For example, in Figure 11In one example, a predicted value falling within 10% of 1.5 bushels per acre would satisfy the corresponding action threshold (within 10% of 1.5 bushels per acre), and actions such as reducing the cleaning fan speed would be performed by the control system 214. In other examples, the threshold in column threshold display column 752 is separated from the selected value in value display column 750, such that the value in value display column 750 defines the classification and display of the predicted or measured value, while the action threshold defines when to take action based on the measured or predicted value. For example, while a predicted or measured loss value of 140 bushels per acre could be designated as “medium yield” for classification and display purposes, the action threshold could be 10%, such that action is not taken until the loss value meets the threshold. In other examples, the threshold in threshold display column 752 could include distance or time. For example, in the distance example, the threshold could be a threshold distance from a georeferenced area of ​​the field where the measured or predicted value is located, where the agricultural harvester 100 must be within that area before taking action. For example, a 10-foot threshold distance would mean that action will be taken when the harvester is 10 feet or within 10 feet of a georeferenced area in the field where the measured or predicted value is located. In an example where the threshold is time, the threshold could be the threshold time it takes for the harvester 100 to reach the georeferenced area in the field where the measured or predicted value is located. For example, a 5-second threshold would mean that action will be taken when the harvester 100 is 5 seconds away from the georeferenced area in the field where the measured or predicted value is located. In such examples, the harvester's current position and speed of travel can be considered.

[0149] Display section 738 also includes an interactive action display section, generally indicated by reference numeral 751. The interactive action display section 751 includes an action display column 754 displaying action identifiers that indicate the action to be taken when a predicted or measured value meets an action threshold in a threshold display column 752. Operator 260 can touch the action identifiers in column 754 to change the action to be taken. An action can be taken when the threshold is met. For example, at the bottom of column 754, actions to increase the cleaning fan speed and decrease the cleaning fan speed are identified as actions to be taken if the measured or predicted value meets the threshold in column 752. In some examples, multiple actions can be taken when the threshold is met. For example, in response to the threshold being met, the cleaning fan speed can be adjusted, the threshing rotor can be adjusted, and the recess gap can be adjusted.

[0150] The actions that can be set in column 754 can be any of a variety of different types of actions. For example, these actions can include prohibition actions that, when executed, prevent the combine harvester 100 from harvesting further in the area. These actions can include speed-changing actions that, when executed, change the travel speed of the combine harvester 100 through the field. These actions can include setting-changing actions for changing the settings of internal actuators or another WMA or WMA group, or setting-changing actions for implementing changes to the threshing rotor speed, cleaning fan speed, header position (e.g., pitch, height, tilt, etc.), and various other settings. These are merely examples, and a wide variety of other actions are considered herein.

[0151] The items displayed 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, display markers can be controlled to modify the intensity, color, or pattern of the displayed markers. Additionally, the flashing of the display markers can be controlled. As an example, a description of changes to the visual appearance of the display markers is provided. Therefore, other aspects of the visual appearance of the display markers can be changed. Thus, the display markers can be modified in a desired manner in various situations to, for example, capture the attention of the operator 260. Furthermore, while a specific number of items are displayed on the user interface display 720, this is not necessary. In other examples, more or fewer items may be included on the user interface display 720, and these items may include more or fewer specific items.

[0152] Now back Figure 10The flowchart continues to describe the operation of the operator interface controller 231. At box 760, the operator interface controller 231 detects input for setting a flag and controls the touch-sensitive user interface display 720 to display the flag on the field display section 728. The detected input can be operator input (as shown in 762) or input from another controller (as shown in 764). At box 766, the operator interface controller 231 detects a field sensor input indicating a measured characteristic of the field from one of the field sensors 208. At box 768, the vision control signal generator 684 generates control signals to control the user interface display 720 to display actuators for modifying the user interface display 720 and for modifying machine control. For example, box 770 indicates that one or more actuators for setting or modifying values ​​in columns 739, 746, and 748 can be displayed. Therefore, the user can set flags and modify the characteristics of these flags. For example, the user can modify the yield and the yield specification corresponding to the flag. Box 772 indicates that the action thresholds in column 752 are displayed. Box 776 indicates that the action in column 754 is displayed, and box 778 indicates that the selected value in column 750 is displayed. Box 780 indicates that various other information and actuators can also be displayed on the user interface display 720.

[0153] At box 782, the operator input command processing system 654 detects and processes operator input corresponding to the interaction performed by operator 260 with user interface display 720. If the user interface mechanism displayed on user interface display 720 is a touch-sensitive display, the interaction input performed by operator 260 with the touch-sensitive display can be a touch gesture 784. In some cases, operator interaction input can be input using a clicking device 786 or other operator interaction input 788.

[0154] At box 790, operator interface controller 231 receives a signal indicating an alarm condition. For example, box 792 indicates that the signal may be received by controller input processing system 668, indicating that the value detected in column 750 satisfies a threshold condition present in column 752. As previously explained, threshold conditions may include values ​​below, at, or above a threshold. Box 794 shows that action signal generator 660 may alert operator 260 in response to receiving an alarm condition by generating a visual alarm using visual control signal generator 684, an audio alarm using audio control signal generator 686, a tactile alarm using tactile control signal generator 688, or any combination thereof. Similarly, as shown in box 796, controller output generator 670 may generate outputs to other controllers in control system 214, causing these controllers to perform the corresponding actions identified in column 754. Box 798 shows that operator interface controller 231 may also detect and process alarm conditions in other ways.

[0155] Box 900 illustrates that the voice processing system 662 can detect and process input that invokes the voice processing system 658. Box 902 illustrates that performing voice processing may include using the dialogue management system 680 to converse with the operator 260. Box 904 illustrates that voice processing may include providing a signal to the controller output generator 670 to automatically perform control operations based on the voice input.

[0156] Table 1 below shows an example of a dialogue between the operator interface controller 231 and the operator 260. In Table 1, the operator 260 invokes the voice processing system 658 using a trigger word or wake-up word detected by the trigger detector 672. In the example shown in Table 1, the wake-up word is "Johnny".

[0157] Table 1

[0158] Operator: "Johnny, tell me about the current power usage."

[0159] Operator interface controller: "Machine-level power usage is 90%".

[0160] Operator: "Johnny, what should I do with the current power usage?"

[0161] Operator interface controller: "If the machine speed is increased by 1 MPH, power usage can be increased to 95%."

[0162] Table 2 illustrates an example in which the speech synthesis unit 676 provides output to the audio control signal generator 686 to provide auditory updates intermittently or periodically. The interval between updates can be based on time (such as every five minutes), or on coverage or distance (such as every five acres), or on anomalies (such as when a measured value is greater than a threshold).

[0163] Table 2

[0164] Operator interface controller: "Power usage has averaged 80% over the past 10 minutes."

[0165] Operator interface controller: "For the next 1 acre of land, the projected average power usage is 82%."

[0166] Operator interface controller: "Attention: Power usage is below 80%. Machine speed is increasing."

[0167] The examples shown in Table 3 illustrate some actuators or user input mechanisms on the touch-sensitive display 720 that can be supplemented by voice dialogue. The examples in Table 3 also show that the motion signal generator 660 can generate motion signals to automatically mark clumps of weeds in a field being harvested.

[0168] Table 3

[0169] Human: "Johnny, mark the weed block."

[0170] Operator interface controller: "Tall weed blocks have been marked".

[0171] The example shown in Table 4 illustrates that the motion signal generator 660 can communicate with the operator 260 to start and stop marking weed blocks.

[0172] Table 4

[0173] Human: "Johnny, start marking the weed patch."

[0174] Operator interface controller: "Marking weed blocks".

[0175] Human: "Johnny, stop marking the weed patch."

[0176] Operator interface controller: "Weed block marker stop".

[0177] The examples shown in Table 5 illustrate that the motion signal generator 160 can generate signals for marking weed blocks in a manner different from that shown in Tables 3 and 4.

[0178] Table 5

[0179] Human: "Johnny, mark the next 100 feet as weed block."

[0180] Operator interface controller: "The next 100 feet is marked as a weed block."

[0181] Return again Figure 10 Box 906 illustrates that the operator interface controller 231 can also detect and process situations for outputting messages or other information in other ways. For example, the other controller interaction system 656 can detect input from other controllers indicating alarms or output messages that should be presented to the operator 260. Box 908 illustrates that the output can be an audio message. Box 910 illustrates that the output can be a visual message, and Box 912 illustrates that the output can be a tactile message. Until the operator interface controller 231 determines that the current harvesting operation is complete (as shown in Box 914), processing returns to Box 698, where the geographical location of the harvester 100 is updated, and processing continues as described above to update the user interface display 720.

[0182] Once the operation is complete, any desired values ​​displayed or already 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 predictive model generator 210, predictive graph generator 212, control region generator 213, control algorithm, or other components. The desired values ​​are saved as indicated by box 916. These values ​​can be saved locally on the agricultural harvester 100, or they can be saved at a remote server location or sent to another remote system.

[0183] A control value is a value upon which an action can be based. As described herein, a control value can include any value (or a characteristic indicated by or derived from that value) that can be used to control the agricultural harvester 100. A control value can be any value indicating an agricultural characteristic. A control value can be a predicted value, a measured value, or a detected value. A control value can include any value provided by a graph (such as any of the graphs described herein), for example, a control value can be a value provided by an infographic, a value provided by a priori infographic, or a value provided by a predictive graph, such as a functional predictive graph. A control value can also include any characteristic indicated by or derived from a value detected by any of the sensors described herein. In other examples, control values ​​can be provided by the operator of the agricultural machine, such as commands entered by the operator of the agricultural machine.

[0184] The current discussion has already mentioned processors and servers. In some examples, processors and servers include computer processors with associated memory and timing circuitry (not shown separately). Processors and servers are functional parts of the system or device to which they belong, and are activated and facilitated by other components or items in these systems.

[0185] Furthermore, numerous user interface displays have been discussed. Displays can take various forms and can have various user-actuable operator interface structures set upon them. For example, user-actuable operator interface mechanisms can be text boxes, checkboxes, icons, links, drop-down menus, search boxes, etc. User-actuable operator interface mechanisms can also be actuated in various ways. For example, user-actuable operator interface mechanisms can be actuated using operator interface mechanisms such as click devices (e.g., trackballs or mice, hardware buttons, switches, joysticks or keyboards, thumb switches or thumb pads, etc.), virtual keyboards, or other virtual actuators). Furthermore, if the screen displaying the user-actuable operator interface mechanism is a touch-sensitive screen, touch gestures can be used to actuate the user-actuable operator interface mechanism. Moreover, user-actuable operator interface mechanisms can be actuated using voice commands utilizing speech recognition capabilities. Speech recognition can be implemented using speech detection devices (such as microphones) and software for recognizing the detected speech and executing commands based on the received speech.

[0186] Many data storage devices are also discussed. It should be noted that data storage can be divided into multiple data storage devices. In some examples, one or more data storage devices may be local to the system accessing the data storage devices; one or more data storage devices may all be located remotely from the system utilizing the data storage devices; or one or more data storage devices may be local while others are remote. This disclosure considers all of these configurations.

[0187] Furthermore, the accompanying diagram shows multiple boxes, with each function belonging to a specific box. It should be noted that fewer boxes can be used to illustrate that functions attributed to multiple different boxes are performed by fewer components. Moreover, more boxes can be used to show that the function can be distributed across more components. In different examples, some functions can be added, and some functions can be removed.

[0188] It should be noted that the foregoing discussion has described various different systems, components, logics, and interactions. It should be understood that any or all of such systems, components, logics, and interactions can be implemented by hardware items such as processors, memory, or other processing units, including but not limited to artificial intelligence units (such as neural networks, some of which are described below) that perform functions associated with those systems, components, logics, or interactions. Furthermore, any or all of the systems, components, logics, and interactions can be implemented by software loaded into memory and subsequently executed by a processor or server or other computing unit, as described below. Any or all of the systems, components, logics, 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, logics, and interactions described above. Other structures may also be used.

[0189] Figure 12 This is a block diagram of the agricultural harvester 600, which can be similar to... Figure 2 The agricultural harvester 100 is shown in the diagram. The agricultural harvester 600 communicates with components in a remote server architecture 500. In some examples, the remote server architecture 500 can provide computing, software, data access, and storage services without requiring end users to know the physical location or configuration of the system delivering 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 applications over a wide area network and can be accessed via a web browser or any other computing component. Figure 2 The software or components shown herein, along with their associated data, can be stored on servers at remote locations. Computing resources in a remote server environment can be consolidated at a remote data center location, or they can be distributed across multiple remote data centers. Remote server infrastructure can deliver services through a shared data center, even if the service appears as a single access point to the user. Therefore, the components and functions described herein can be provided from remote servers at remote locations using a remote server architecture. Alternatively, components and functions can be provided from servers, or they can be installed directly or otherwise on client devices.

[0190] exist Figure 12 In the examples shown, some items are similar to Figure 2 The items shown are numbered similarly. Figure 12 Specifically, a predictive model generator 210 or a predictive graph generator 212, or both, can be located at a server location 502, away from the agricultural harvester 600. Therefore, in Figure 12In the example shown, the agricultural harvester 600 accesses the system via a remote server location 502.

[0191] Figure 12 Another example of a remote server architecture is also described. Figure 12 It shows Figure 2 Some components can be located at remote server location 502, while others can be located elsewhere. As an example, data storage device 202 can be placed at a location separate from location 502 and accessed via a remote server at location 502. Regardless of their location, these components can be directly accessed by the combine harvester 600 via a network (such as a wide area network or local area network); these components can be hosted on a remote site, provided as a service, or accessed by a connection service residing at a remote location. Furthermore, data can be stored anywhere, and the stored data can be accessed or forwarded to or by an operator, user, or system. For example, a physical carrier can be used in addition to a physical carrier. In some examples, where wireless telecommunications service coverage is poor or absent, another machine (such as a fuel truck or other mobile machine or vehicle) can have an automatic, semi-automatic, or manual information collection system. When the combine harvester 600 approaches the machine (such as a fuel truck) containing the information collection system before refueling, the information collection system collects information from the combine harvester 600 using any type of temporary dedicated wireless connection. Then, when the machine containing the received information reaches a location where wireless telecommunications service coverage or other wireless coverage is available, the collected information can be forwarded to another network. For example, when a fuel truck travels to a location to refuel other machines or at a main fuel storage location, the fuel truck can enter an area with wireless communication coverage. All these architectures are considered in this paper. Furthermore, 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.

[0192] It will also be noted that Figure 2 The components or parts thereof can be located on a variety of different devices. One or more of these devices may include an airborne computer, electronic control unit, display unit, server, desktop computer, laptop computer, tablet computer, or other mobile devices such as PDAs, cellular phones, smartphones, multimedia players, personal digital assistants, etc.

[0193] In some examples, the remote server architecture 500 may include network security measures. Without limitation, these measures include encryption of data on storage devices, encryption of data sent between network nodes, authentication of personnel or processes accessing data, and the use of a ledger for recording metadata, data, data transfer, data access, and data transformation. In some examples, the ledger may be distributed and immutable (e.g., implemented as a blockchain).

[0194] Figure 13 This is a simplified block diagram illustrating a schematic example of a handheld or mobile computing device 16 that can be used as a part of this system (or a user's or customer's handheld device) to be deployed therein. For example, a mobile device could be deployed in the operator's cab of an agricultural harvester 100 for use in generating, processing, or displaying the diagrams discussed above. Figures 14 to 15 Examples are handheld or mobile devices.

[0195] Figure 13 A general block diagram of the components of client device 16, which can operate... Figure 2 The device 16 includes some of the components shown, interacts with them, or both. In device 16, a communication link 13 is provided that allows the handheld device to communicate with other computing devices, and in some examples, the communication link 13 provides a channel for automatically receiving information (e.g., by scanning). Examples of the communication link 13 include those that allow communication via 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.

[0196] In other examples, the application can receive data on a removable Secure Digital (SD) card connected to interface 15. Interface 15 and communication link 13 communicate along bus 19 with processor 17 (which may also be implemented from a processor or server in other figures), which is also connected to memory 21 and input / output (I / O) components 23, as well as clock 25 and position system 27.

[0197] In one example, I / O components 23 are provided to facilitate input and output operations. Various examples of I / O components 23 in device 16 may include input components (such as buttons, touch sensors, optical sensors, microphones, touchscreens, proximity sensors, accelerometers, orientation sensors) and output components (such as display devices, speaker and / or printer ports). Other I / O components 23 may also be used.

[0198] Clock 25 schematically includes a real-time clock component that outputs the time and date. Schematically, it may also provide timing functions for processor 17.

[0199] Location system 27 schematically includes components that output the current geographic location of device 16. This may include, for example, a Global Positioning System (GPS) receiver, a LoRAN system, a dead reckoning system, a cellular triangulation system, or other positioning systems. Location system 27 may also include, for example, mapping or navigation software that generates desired maps, navigation routes, and other geographic functions.

[0200] Memory 21 stores operating system 29, network settings 31, applications 33, application configuration settings 35, data storage device 37, communication driver 39, and communication configuration settings 41. Memory 21 may include all types of tangible, volatile, and non-volatile computer-readable storage devices. Memory 21 may also include computer storage media (described below). Memory 21 stores computer-readable instructions that, when executed by processor 17, cause the processor to perform computer-implemented steps or functions. Processor 17 may also be activated by other components to facilitate their functionality.

[0201] Figure 14 The illustration shows an example where device 16 is a tablet computer 600. Figure 14 In the diagram, computer 601 is shown as having a user interface display screen 602. Screen 602 can be a touchscreen or a pen-enabled interface that receives input from a pen or stylus. Tablet 600 can also utilize an on-screen virtual keyboard. Of course, computer 601 can also be attached to a keyboard or other user input device, for example, via a suitable attachment structure (such as a wireless link or USB port). Computer 601 can also schematically receive voice input.

[0202] Figure 15 Similar to Figure 14 In addition to being a smartphone 71, the smartphone 71 has a touch-sensitive display 73 that shows icons, blocks, or other user input mechanisms 75. Users can use these mechanisms 75 to run applications, make calls, perform data transfer operations, etc. Generally, smartphones 71 are built on a mobile operating system and offer more advanced computing power and connectivity than feature phones.

[0203] Note that other forms of device 16 are possible.

[0204] Figure 16 It is one of the deployable ones Figure 2 An example of a computing environment for the components. Reference Figure 16An example system for implementing some embodiments includes a computing device in the form of a computer 810 programmed to operate as discussed above. Components of the computer 810 may include, but are not limited to, a processing unit 820 (which may include a processor or server from the previous figures), 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 may be any of several types of bus architectures, including a memory bus or memory controller, a peripheral bus, and a local bus using any of various bus architectures. Regarding... Figure 2 The described memory and program can be deployed in Figure 16 In the corresponding part.

[0205] Computer 810 typically includes a variety of computer-readable media. Computer-readable media can be any available medium accessible to computer 810, and includes volatile and non-volatile media, removable and non-removable media. By way of example and not limitation, computer-readable media can include computer storage media and communication media. Computer storage media are distinct from and do not include modulated data signals or carriers. Computer-readable media include hardware storage media, including volatile and non-volatile, removable and non-removable media implemented in any way or by any technique for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical disc storage devices, magnetic tape, magnetic tape, disk storage devices or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to computer 810. Communication media can implement computer-readable instructions, data structures, program modules, or other data in a transmission mechanism, and includes any information delivery medium. The term "modulated data signal" refers to a signal that has one or more of its characteristics set or changed in a manner that encodes information in the signal.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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.

[0211] Computer 810 operates in a networked environment using a logical connection (such as a controller local area network (CAN), local area network (LAN), or wide area network (WAN)) between one or more remote computers (such as remote computer 880).

[0212] When used in a LAN networking environment, computer 810 connects to LAN 871 via a network interface or adapter 870. When used in a WAN networking environment, computer 810 typically includes a modem 872 or other devices for establishing communication over a WAN 873 (such as the Internet). In a networking environment, program modules can be stored in remote memory storage devices. For example, Figure 16 This demonstrates that remote application 885 can reside on remote computer 880.

[0213] 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.

[0214] Example 1 is an agricultural operating machine, comprising:

[0215] A communication system that receives a priori information map, the priori information map including values ​​of a first agricultural characteristic from previous agricultural operations corresponding to different geographical locations in the field;

[0216] A geolocation sensor that detects the geolocation of agricultural machinery;

[0217] A field sensor that detects a value of a second agricultural characteristic corresponding to the geographical location;

[0218] A predictive model generator generates a predictive agricultural model based on the value of a first agricultural characteristic at the geographical location in a priori information map and the value of a second agricultural characteristic sensed by a field sensor at the geographical location, the predictive agricultural model modeling the relationship between the first and second agricultural characteristics; and

[0219] A predictive map generator generates a functional predictive agricultural map of a field, which maps the predicted value of a second agricultural characteristic to different geographical locations in the field based on the value of a first agricultural characteristic in a prior information map and a predictive agricultural model.

[0220] Example 2 is any or all of the agricultural operating machines of the foregoing examples, wherein a predictive map generator configures a functional predictive agricultural map for use by a control system, which generates control signals based on the functional predictive agricultural map to control controllable subsystems on the agricultural operating machine.

[0221] Example 3 is any or all of the agricultural operating machines of the foregoing examples, wherein the prior information map includes a prior operation map that maps the value of the application operation characteristic, which is a first agricultural characteristic, to different geographical locations in the field.

[0222] Example 4 is any or all of the agricultural operation machines of the foregoing examples, wherein a predictive model generator is configured to determine the relationship between the second agricultural characteristic and the application operation characteristic based on the value of the second agricultural characteristic detected at the geographic location and the value of the application operation characteristic in the prior information map at the geographic location, and the predictive agricultural model is configured to receive the input application operation characteristic value as the model input and generate a predicted second agricultural characteristic value as the model output based on the determined relationship.

[0223] Example 5 is any or all of the agricultural machinery described in the preceding examples, wherein the application operation characteristics include one of the following: water application characteristics, fertilizer application characteristics, herbicide application characteristics, insecticide application characteristics, and fungicide application characteristics.

[0224] Example 6 is any or all of the agricultural operation machines of the foregoing examples, wherein the prior information map includes a prior operation map that maps the value of the removal operation characteristic as a first agricultural characteristic to different geographical locations in the field.

[0225] Example 7 is any or all of the agricultural operation machines of the foregoing examples, wherein a predictive model generator is configured to determine the relationship between the second agricultural characteristic and the removal operation characteristic based on the value of the second agricultural characteristic detected at the geographic location and the value of the removal operation characteristic in the prior information map at the geographic location, and the predictive agricultural model is configured to receive the input removal operation characteristic value as the model input and generate a predicted second agricultural characteristic value as the model output based on the determined relationship.

[0226] Example 8 is any or all of the agricultural operating machines of the foregoing examples, wherein the removal operation feature includes one of the harvesting operation feature, the stacking operation feature, and the baling operation feature.

[0227] Example 9 is any or all of the agricultural operating machines of the foregoing examples, wherein the prior information map includes a prior operation map that maps the values ​​of the ground-attached operation characteristic, which is a first agricultural characteristic, to different geographical locations in the field.

[0228] Example 10 is any or all of the agricultural operation machines of the foregoing examples, wherein a predictive model generator is configured to determine a relationship between the second agricultural characteristic and the ground-joining operation characteristic based on a second agricultural characteristic value detected at a geographic location and a value of the ground-joining operation characteristic in a priori information map at the geographic location, and a predictive agricultural model is configured to receive an input ground-joining operation characteristic value as a model input and generate a predicted second agricultural characteristic value as a model output based on the determined relationship.

[0229] Example 11 is any or all of the agricultural operating machines of the foregoing examples, and also includes an operator interface mechanism that displays a graphical representation of a functional predictive agricultural map.

[0230] Example 12 is a computer-implemented method for generating functional predictive agricultural maps, comprising:

[0231] Receive a priori information map at the agricultural operation machinery, the priori information map indicating the value of a first agricultural characteristic from previous agricultural operations corresponding to different geographical locations in the field;

[0232] Detecting the geographical location of agricultural machinery;

[0233] The values ​​of the second agricultural characteristic corresponding to the geographical location are detected using on-site sensors;

[0234] Generate predictive agricultural models that model the relationship between the first and second agricultural characteristics; and

[0235] A predictive map generator is controlled to generate a functional predictive agricultural map of a field based on the value of a first agricultural characteristic in a priori information map and a predictive agricultural model. The functional predictive agricultural map maps the predicted value of a second agricultural characteristic to different locations in the field.

[0236] Example 13 is a computer-implemented method of any or all of the foregoing examples, and also includes:

[0237] A functional predictive agriculture map is configured for the control system, which generates control signals based on the functional predictive agriculture map to control controllable subsystems on agricultural machinery.

[0238] Example 14 is a computer-implemented method of any or all of the foregoing examples, wherein receiving the prior information graph includes:

[0239] Receive a priori information map generated by prior ground joining operations performed in the field.

[0240] Example 15 is a computer-implemented method of any or all of the foregoing examples, wherein receiving the prior information graph includes:

[0241] Receive a priori information map generated by prior application operations performed in the field.

[0242] Example 16 is a computer-implemented method of any or all of the foregoing examples, wherein receiving the prior information graph includes:

[0243] Receive a priori information map generated by prior removal operations performed in the field.

[0244] Example 17 is a computer-implemented method of any or all of the foregoing examples, wherein receiving the prior information graph includes:

[0245] Receive the prior information map generated by the prior acceptance and harvesting operation performed in the field.

[0246] Example 18 is a computer-implemented method of any or all of the foregoing examples, further comprising:

[0247] Control the operator interface mechanism to present predictive agricultural maps.

[0248] Example 19 is an agricultural operating machine, comprising:

[0249] A communication system that receives a priori information map indicating agricultural characteristic values ​​corresponding to different geographical locations in the field from previous agricultural operations;

[0250] A geolocation sensor that detects the geolocation of agricultural machinery;

[0251] A field sensor that detects a value of a second agricultural characteristic corresponding to the geographical location;

[0252] A predictive model generator generates a predictive model based on the values ​​of a first agricultural characteristic corresponding to the geographical location in a priori information map and the values ​​of a second agricultural characteristic sensed by the field sensor corresponding to the geographical location. The predictive model models the relationship between the first and second agricultural characteristics.

[0253] A predictive map generator generates a functional predictive map of a field, which maps a first agricultural characteristic value from a prior information map to different locations in the field based on a predictive model.

[0254] Example 20 is any or all of the agricultural operating machines of the foregoing examples, wherein the prior information map indicates one or more agricultural characteristics from the application operation, removal operation, and ground engagement operation.

[0255] Although the subject matter has been described in language specific to structural features or methodological actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are disclosed as examples of the claims.

Claims

1. An agricultural system comprising: a geographic position sensor (204) that detects a geographic position of an agricultural work machine; a field sensor (208) that detects a value of a second agricultural property corresponding to the geographic position as the agricultural work machine performs an operation in a field; one or more processors; a data store that stores computer-executable instructions that, when executed by the one or more processors, configure the one or more processors to: receive a prior information map (258) that includes values of a first agricultural property corresponding to different geographic positions in the field from previous agricultural operations; generate, as the agricultural work machine performs the operation in the field, a predictive agricultural model that models a relationship between the first agricultural property and the second agricultural property based on values of the first agricultural property in the prior information map (258) at the geographic position corresponding to the detected value of the second agricultural property and the detected value of the second agricultural property corresponding to the geographic position; generate, as the agricultural work machine performs the operation in the field, a functional predictive agricultural map of the field that maps predicted values of the second agricultural property to the different geographic positions in the field based on values of the first agricultural property in the prior information map (258) and based on the predictive agricultural model; and generate, as the agricultural work machine performs the operation in the field, control signals to control a controllable subsystem on the agricultural work machine based on the functional predictive agricultural map. the prior information map includes a prior operation map that maps values of an application operation property that is a value of the first agricultural property to the different geographic positions in the field.

2. The agricultural system of claim 1, wherein, the predictive agricultural model models a relationship between the second agricultural property and the application operation property based on the detected value of the second agricultural property corresponding to the geographic position and the value of the application operation property in the prior information map at the geographic position corresponding to the detected value of the second agricultural property, the predictive agricultural model configured to receive an input application operation property value as a model input and generate a predicted value of the second agricultural property as a model output based on the determined relationship.

3. The agricultural system of claim 2, wherein, the application operation property includes one of a water application property, a fertilizer application property, a herbicide application property, an insecticide application property, and a fungicide application property.

4. The agricultural system of claim 3, wherein, the prior information map includes a prior operation map that maps values of a removal operation property that is a value of the first agricultural property to the different geographic positions in the field.

5. The agricultural system of claim 1, wherein, ​ 6. The agricultural system of claim 5, wherein, The predictive agricultural model models a relationship between the second agricultural property and the removal operation property based on the detected value of the second agricultural property corresponding to the geographic location and a value of the removal operation property in the prior information map at the geographic location corresponding to the detected value of the second agricultural property, the predictive agricultural model configured to receive an input removal operation property value as a model input and generate a predicted value of the second agricultural property as a model output based on the determined relationship.

7. The agricultural system of claim 6, wherein, The removal operation property includes one of a harvesting operation property, a stacking operation property, and a baling operation property.

8. A computer-implemented method of generating a functional predictive agricultural map, comprising: receiving, at an agricultural work machine (100), a prior information map (258) indicating values of a first agricultural property corresponding to different geographic locations in a field from previous agricultural operations; detecting a geographic location of the agricultural work machine (100); detecting, with an in-field sensor (208), a value of a second agricultural property corresponding to the geographic location as the agricultural work machine performs an operation in the field; generating, as the agricultural work machine performs the operation in the field, a predictive agricultural model modeling a relationship between the first agricultural property and the second agricultural property; controlling, as the agricultural work machine operates in the field, a predictive map generator (212) to generate a functional predictive agricultural map of the field based on the values of the first agricultural property in the prior information map (258) and the predictive agricultural model, the functional predictive agricultural map mapping predicted values of the second agricultural property to different locations in the field; and controlling, as the agricultural work machine performs the operation in the field, a controllable subsystem of the agricultural work machine based on the functional predictive agricultural map.

9. An agricultural system, comprising: a geographic location sensor (204) that detects a geographic location of an agricultural work machine (100); an in-field sensor (208) that detects a value of a second agricultural property corresponding to the geographic location as the agricultural work machine operates in a field; one or more processors; a data store storing computer-executable instructions that, when executed by the one or more processors, configure the one or more processors to: receive a prior information map (258) indicating values of an agricultural property corresponding to different geographic locations in the field from previous agricultural operations; generate, as the agricultural work machine performs the operation in the field, a predictive model modeling a relationship between a first agricultural property and the second agricultural property based on values of the first agricultural property in the prior information map (258) corresponding to the geographic location corresponding to the detected value of the second agricultural property and the detected value of the second agricultural property corresponding to the geographic location; and control, as the agricultural work machine performs the operation in the field, a controllable subsystem of the agricultural work machine based on the functional predictive agricultural map. ​ While the agricultural work machine performs the operation in the field, a functional predictive map of the field is generated based on values of a first agricultural property in the a priori information map (258) and based on the predictive model, the functional predictive map mapping predictive second agricultural property values to different geographical locations in the field.

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