Machine control using prediction maps

Abstract

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

Description

TECHNICAL FIELD

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

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

[0003] As the harvester travels and performs the harvesting operation, the agricultural harvester disperses by-products from the harvesting operation (referred to as material other than grain (MOG)) across the field.

[0004] The above discussion is provided as background information only and is not intended to act as an aid in determining the scope of the subject matter claimed. SUMMARY

[0005] One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural property values at different geographic locations of a field. As the agricultural work machine moves through the field, an on-site sensor on the agricultural work machine senses an agricultural property. A prediction map generator generates a prediction map that predicts the agricultural property at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural property sensed by the on-site sensor. The prediction map can be output and used for automated machine control. The summary is provided to introduce a selection of concepts in a simplified form that are further described below in the DETAILED DESCRIPTION. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used in determining the scope of the claimed subject matter. The claimed subject matter is not limited to implementations that solve any or all disadvantages noted in the background. BRIEF DESCRIPTION OF DRAWINGS

[0006] Figure 1 is a partially diagrammatic, partially schematic view of an example of an agricultural harvester.

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

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

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

[0010] Figure 5is a flowchart illustrating one example of operations of a control zone generator.

[0011] Figure 6 is a block diagram illustrating one example of a control zone generator.

[0012] Figure 7 is a flowchart illustrating one example of operations of the control zone generator illustrated in Figure 6

[0013] Figure 8 is a flowchart illustrating one example of operations of the control zone generator illustrated in

[0014] Figure 9 is a block diagram illustrating one example of an operator interface controller.

[0015] Figure 10 is a flowchart illustrating one example of operations of the operator interface controller.

[0016] Figure 11 is a diagrammatical illustration of one example of an operator interface display.

[0017] Figure 12 is a block diagram illustrating one example of an agricultural harvester in communication with a remote server environment.

[0018] Figures 13-15 is shown an example of a mobile device that can be used with an agricultural harvester.

[0019] Figure 16 is a block diagram illustrating one example of a computing environment that can be used with an agricultural harvester. DETAILED DESCRIPTION

[0020] To facilitate an understanding of the principles of the present disclosure, reference will now be made to the examples illustrated in the drawings, and specific language will be used to describe them. It will, nevertheless, be understood that no limitation of the scope of the disclosure is intended. Alterations and further modifications of the described devices, systems, methods, and any further applications of the principles of the present disclosure are fully contemplated as are likely to occur to one skilled in the art to which the present disclosure pertains. In particular, it is fully contemplated that the features, components, and / or steps described with respect to one example can be combined with the features, components, and / or steps described with respect to other examples of the present disclosure.

[0021] ​This specification relates to the generation of predictive maps, such as predictive residue maps, using field data acquired concurrently with agricultural operations in combination with predicted or prior data. In some examples, the predictive maps can be used to control agricultural machinery (e.g., agricultural harvesters) to distribute residues evenly across the field.

[0022] Evenly distributed residue can increase overall field yield. Uneven residue cover can lead to a variety of problems. For example, nutrients in the residue will concentrate under high residue patches. Or, for example, pests such as insects, slugs, and rodents will inhabit larger residue mounds. Or, for example, weed seeds and grains lost through combine harvesters will concentrate in residue patches. Or, for example, herbicide effectiveness will be affected because residue patches prevent herbicides from reaching the soil. Or, for example, piles or clumps of residue can reduce seeder performance because the furrow opener cannot cut through too much residue, and seeds cannot be planted in the soil. Or, for example, uneven residue cover can also lead to uneven soil temperature and moisture conditions. The soil beneath areas with more residue will be a few degrees lower than bare soil and will be more moist, resulting in differences in crop development.

[0023] The performance of residue spreaders on agricultural harvesters can be adversely affected by a variety of different criteria. For example, areas with variations in vegetation (such as the density of weeds or crop plants) can negatively impact residue spreading operations. Increased vegetation can increase the quality of residue spread by agricultural harvesters.

[0024] Alternatively, for example, terrain characteristics influence the orientation (e.g., pitching and rolling) of a combine harvester as it travels across the terrain. This orientation affects how the harvester spreads residue across the field. For instance, when a combine harvester rolls to the left or right, the uphill side will have a shorter residue spread distance.

[0025] Alternatively, areas with variations in vegetation moisture (e.g., moisture in weeds and crop plants) may adversely affect residue dispersal operations. For example, materials with higher moisture content may disperse over a narrower width due to increased friction within the residue system or due to increased material mass. Or, in some cases, materials with higher moisture content may disperse further due to increased inertia as the material resists air resistance or the effects of wind.

[0026] Vegetation index maps illustratively map vegetation index values (which can be indicative of vegetation growth) at different geographic locations throughout a field of interest. One example of a vegetation index includes the normalized difference vegetation index (NDVI). There are many other vegetation indices within the scope of the present disclosure. In some examples, a vegetation index can be derived from sensor readings of one or more electromagnetic radiation bands reflected by the plants. Without limitation, these electromagnetic radiation bands can be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum.

[0027] Vegetation index maps can be used to identify the presence and location of vegetation. In some examples, these maps enable identification and georegistration of weeds in the presence of bare soil, crop residue, or other plants, including crops or other weeds. For example, at the end of a growing season, when crops are mature, the crop plants can exhibit a reduced level of live and growing vegetation. However, weeds typically continue to be in a growing state after the crops have matured. Thus, if a vegetation index map is generated at a relatively late stage of the growing season, the vegetation index map can indicate the location of weeds in the field.

[0028] Topography maps illustratively map the height or elevation of the ground at different geographic locations in a field of interest. Since ground slope indicates a change in height or elevation, having two or more height values allows for the calculation of slope across an area with known height values. Greater granularity of slope can be achieved with more areas with known height values. As an agricultural harvester travels across the topography in a known direction, pitch and roll of the agricultural harvester can be determined based on the slope of the ground (i.e., areas of changing height or elevation). The topographical characteristics mentioned below can include, but are not limited to, height or elevation, slope (e.g., including machine orientation with respect to slope), and ground contour (e.g., roughness).

[0029] Crop moisture maps illustratively map the vegetation moisture at different geographic locations in a field of interest. In one example, crop moisture can be sensed by an unmanned aerial vehicle (UAV) equipped with moisture sensors prior to a harvesting operation. As the UAV travels through the field, crop moisture readings are geolocated to create a crop moisture map. This is merely an example, and vegetation moisture maps can also be created in other ways. For example, vegetation moisture throughout a field can be predicted based on weather conditions such as precipitation, temperature, or wind, field surface characteristics such as topography or soil moisture, or a combination thereof. In some examples, a vegetation moisture map can be generated by subtracting the difference between potential evapotranspiration and moisture to determine any deficit. In this approach, weather characteristics such as precipitation and temperature, as well as previously calculated vegetation moisture indices, can be used as inputs.

[0030] Therefore, this discussion focuses on a system that receives an infographic based on prior or previous operational predictions or generation, and which also uses field sensors to detect one or more variables indicative of agricultural characteristics, such as residue characteristics during harvesting operations. The system generates a model that models the relationship between values ​​on the infographic and output values ​​from the field sensors. This model is used to generate a functional prediction map that predicts, for example, residue characteristics at different locations in the field. The functional prediction map generated during harvesting operations can be presented to the operator or other users, and / or used to automatically control the agricultural harvester during harvesting operations. The functional prediction map can be used to control a residue handling subsystem or other components of the agricultural harvester.

[0031] Figure 1 This 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 will be understood that this specification also applies 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. In addition, this disclosure relates to other types of operating machines, such as agricultural seeders and sprayers to which predictive mapping can be applied, construction equipment, forestry equipment, and turf 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.

[0032] like Figure 1 As shown, the agricultural harvester 100 exemplary 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. In the illustrated example, the cutter 104 is included on the header 102. The agricultural harvester 100 also includes a feeder housing 106, a feed accelerator 108, and a thresher generally indicated by 110. The feeder housing 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. Therefore, the vertical position (cutting height) of the cutting table 102 above the ground 111 (where the cutting table 102 travels) can be controlled by actuating the actuator 107. Although Figure 1As not shown, the agricultural harvester 100 can also include one or more actuators that operate to apply a tilt angle, a roll angle, or both, to the header 102 or portions of the header 102. Tilt refers to the angle at which the cutterbar 104 engages the crop. For example, the tilt angle is increased by controlling the header 102 to cause the distal edge 113 of the cutterbar 104 to point more toward the ground. The tilt angle is decreased by controlling the header 102 to cause the distal edge 113 of the cutterbar 104 to point more away from the ground. Roll refers to the orientation of the header 102 about the fore-aft longitudinal axis of the agricultural harvester 100.

[0033] The threshing machine 110 illustratively includes a threshing cylinder 112 and a set of concaves 114. In addition, the agricultural harvester 100 also includes a separator 116. The agricultural harvester 100 also includes a grain cleaning subsystem or grain cleaning house 118 (collectively, the grain cleaning subsystem 118) that includes a grain cleaning fan 120, a chaffer 122, and a sieve 124. The material handling subsystem 125 also includes an unloading beater 126, a residue elevator 128, a clean grain elevator 130, and an unloading auger 134 and spout 136. The clean grain elevator moves clean grain into a clean grain tank 132. The agricultural harvester 100 also includes a residue subsystem 138 that can include a chopper 140 and a spreader 142. The agricultural harvester 100 also includes a propulsion subsystem that includes an engine that drives ground engaging assemblies 144 (e.g., wheels or tracks). In some examples, a combine within the scope of the present disclosure can have more than one of any of the subsystems described above. In some examples, the agricultural harvester 100 can have Figure 1 Left and right grain cleaning subsystems, separators, etc., not shown in the middle.

[0034] In operation, as outlined, the agricultural harvester 100 is illustratively moved through a field in the direction indicated by arrow 147. As the agricultural harvester 100 moves, the header 102 (and associated reel 164) engages the crop to be harvested and collects the crop toward the cutter 104. The operator of the agricultural harvester 100 can be a local human operator, a remote human operator, or an automated system. The operator commands are in accordance with the operator's commands. The operator of the agricultural harvester 100 can determine one or more of a height setting, a tilt angle setting, or a roll angle setting of the header 102. For example, the operator inputs one or more settings to a control system (described in greater detail below) that controls the actuators 107. The control system can also receive settings from the operator for establishing the tilt angle and roll angle of the header 102 and implement the input settings by controlling the associated actuators (not shown) that change the tilt angle and roll angle of the header 102. The actuators 107 maintain the header 102 at a height above the ground 111 based on the height setting and, where applicable, at a desired tilt angle and roll angle. Each of the height setting, roll setting, and tilt setting can be implemented independent of the other settings. The control system responds to header errors (e.g., a difference between the height setting and a measured height of the header 104 above the ground 111, and in some cases, a tilt angle error and a roll angle error) with a responsiveness determined based on a selected sensitivity level. If the sensitivity level is set at a greater sensitivity level, the control system responds to smaller header position errors and attempts to reduce detected errors more quickly than when the sensitivity is at a lower sensitivity level.

[0035] Returning to the description of the operation of the agricultural harvester 100, after the crop is cut by the cutter 104, the cut crop material moves through the feeder housing 106 by a conveyor toward the feed accelerator 108, which accelerates the crop material into the threshing machine 110. The crop is threshed by rotating the cylinder 112 against the concave 114. The threshed crop is moved by the separator cylinder in the separator 116, with a portion of the residue moved toward the residue subsystem 138 by the discharge beater 126. The portion of the residue that is conveyed to the residue subsystem 138 is chopped by the residue chopper 140 and spread on the field by the spreader 142. In other configurations, the residue is discharged from the agricultural harvester 100 in a pile. In other examples, the residue subsystem 138 can include a grass seed rejector (not shown), such as a seed bagger or other seed collector or a seed pulverizer or other seed breaker.

[0036] The grain falls into the grain cleaning subsystem 118. The chaffer 122 separates some larger material from the grain, and the sieve 124 separates some fine material from the clean grain. The clean grain falls onto a screw conveyor that moves the clean grain to an inlet end of a clean grain elevator 130, and the clean grain elevator 130 moves the clean grain upward, depositing the clean grain in a clean grain bin 132. An airflow generated by the grain cleaning fan 120 removes the residue from the grain cleaning subsystem 118. The grain cleaning fan 120 directs air up through the sieve and the chaffer along an airflow path. The airflow transports the residue back in the agricultural harvester 100 toward a residue handling subsystem 138.

[0037] The residue elevator 128 returns the residue to the threshing machine 110, where the residue is re-threshed. Alternatively, the residue can also be delivered by the residue elevator or another transport device to a separate re-threshing mechanism, where the residue is also re-threshed.

[0038] Figure 1 Also shown, in one example, the agricultural harvester 100 includes a machine speed sensor 146, one or more separator loss sensors 148, a clean grain camera 150, a forward / rear / rear view image capture mechanism 151 (which can be in the form of a stereo camera or a monocular camera), and one or more loss sensors 152 disposed in the grain cleaning subsystem 118.

[0039] The machine speed sensor 146 senses a speed of travel of the agricultural harvester 100 over the ground. The machine speed sensor 146 can sense the speed of travel of the agricultural harvester 100 by sensing a rotational speed of a ground-engaging component (e.g., a wheel or track), a drive shaft, an axle, or other component. In some cases, a positioning system such as a global positioning system (GPS), a dead reckoning system, a LORAN system, or a variety of different other systems or sensors that provide an indication of the speed of travel can be used.

[0040] The loss sensors 152 illustratively provide output signals indicative of an amount of grain loss occurring in both the right and left sides of the grain cleaning subsystem 118. In some examples, the sensors 152 are impact sensors that count grain impacts per unit of time or per unit of travel distance to provide an indication of grain loss occurring at the grain cleaning subsystem 118. The impact sensors of the right and left sides of the grain cleaning subsystem 118 can provide separate signals or a combined or aggregated signal. In some examples, the sensors 152 can include a single sensor, as opposed to providing separate sensors for each of the grain cleaning subsystems 118.

[0041] The separator loss sensors 148 provide an indication of the amount of grain loss occurring in the left and right separators (Figure 1 The separator loss sensor 148 can be associated with the left and right separators and can provide separate grain loss signals or a combined or aggregated signal. In some cases, various different types of sensors can also be used to sense grain loss in the separators.

[0042] The agricultural harvester 100 can also include other sensors and measurement mechanisms. For example, the agricultural harvester 100 can include one or more of the following sensors: a header height sensor that senses the height of the header 102 above the ground 111; a stability sensor that senses the oscillation or bounce (and amplitude) of the agricultural harvester 100; a residue setting sensor configured to sense whether the agricultural harvester 100 is configured to chop residue, windrow, etc.; a clean grain bin fan speed sensor that senses the speed of the fan 120; a concave gap sensor that senses the gap between the cylinder 112 and the concave 114; a threshing cylinder speed sensor that senses the cylinder speed of the cylinder 112; a chaffer screen gap sensor that senses the opening size in the chaffer screen 122; a screen mesh gap sensor that senses the opening size in the screen mesh 124; a material other than grain (MOG) moisture sensor that senses the moisture level of the MOG passing through the agricultural harvester 100; one or more machine setting sensors configured to sense various configurable settings of the agricultural harvester 100; a machine orientation sensor that senses the orientation of the agricultural harvester 100; and a crop property sensor that senses various different types of crop properties, such as crop type, crop moisture, and other crop properties. The crop property sensor can also be configured to sense the properties of the severed crop material as the agricultural harvester 100 is processing the crop material. For example, in some cases, the crop property sensor can sense: grain quality, such as broken grain, MOG levels; grain composition, such as starch and protein; and grain feed rate as the grain passes through the feeder housing 106, the clean grain elevator 130, or elsewhere in the agricultural harvester 100. The crop property sensor can also sense the biomass feed rate through the feeder housing 106, through the separator 116, or elsewhere in the agricultural harvester 100. The crop property sensor can also sense the mass flow rate of the grain through the elevator 130 or through other portions of the agricultural harvester 100, or provide other output signals indicative of other sensed variables.

[0043] Before describing how the agricultural harvester 100 generates and uses a functional predicted residue map, a brief description of some items on the agricultural harvester 100 and their operation will first be described. Figure 2 、 Figure 3A and Figure 3BThe drawing descriptions depict receiving a general type of information map and combining information from the information map with georegistered sensor signals generated by on-site sensors, where the sensor signals are indicative of characteristics in a field, such as characteristics of crops or weeds present in the field. Characteristics of the field can include (but are not limited to): characteristics of the field, such as slope, weed density, weed type, soil moisture, surface quality; characteristics of crop properties, such as crop height, crop moisture, crop density, crop status; characteristics of grain properties, such as grain moisture, grain size, grain test weight; and characteristics of machine performance, such as loss level, work quality, fuel consumption, and power utilization. Relationships between characteristic values obtained from the on-site sensor signals and information map values are identified, and the relationships are used to generate a new functional prediction map. The functional prediction map predicts values at different geographic locations in the field, and one or more of those values can be used to control a machine, such as to control one or more subsystems of an agricultural harvester. In some cases, the functional prediction map can be presented to a user, such as an operator of an agricultural work machine (which can be an agricultural harvester). The functional prediction map can be presented to the user visually (e.g., via a display), haptically, or aurally. The user can interact with the functional prediction map to perform editing operations and other user interface operations. In some cases, the functional prediction map can be used to control an agricultural work machine (e.g., an agricultural harvester), presented to an operator or other user, and presented to an operator or user to facilitate one or more of operator or user interaction.

[0044] After describing general methods with reference to Figure 2 , Figure 3A and Figure 3B , more specific methods of generating a functional prediction residue map that can be presented to an operator or user and / or used to control an agricultural harvester 100 are described with reference to Figure 4 and Figure 5 . Again, although this discussion is directed to an agricultural harvester (specifically, a combine harvester), the scope of the present disclosure encompasses other types of agricultural harvesters or other agricultural work machines.

[0045] Figure 2 is a block diagram showing some portions of an example agricultural harvester 100. Figure 2The agricultural harvester 100 is shown to illustratively include one or more processors or servers 201, a data store 202, a geo-location sensor 204, a communication system 206, and one or more field sensors 208 that sense one or more agricultural properties of the field contemporaneously with the harvesting operation. The agricultural properties can include any property that can have an influence on the harvesting operation. Some examples of agricultural properties include properties of the harvesting machine, properties of the field, properties of the plants on the field, and properties of the weather. Other types of agricultural properties are also included. The field sensors 208 generate values corresponding to the sensed properties. The agricultural harvester 100 also includes a predictive model or relationship generator (hereinafter collectively referred to as "predictive model generator 210"), a predictive map generator 212, a control zone generator 213, a control system 214, one or more controllable subsystems 216, and an operator interface mechanism 218. The agricultural harvester 100 can also include various other agricultural harvester functionality 220. For example, the field sensors 208 include on-board sensors 222, remote sensors 224, and other sensors 226 that sense properties of the field during the course of the agricultural operation. The predictive model generator 210 illustratively includes an information variable to field variable model generator 228, and the predictive model generator 210 can include other items 230. The control system 214 includes a communication system controller 229, an operator interface controller 231, a setting controller 232, a path planning controller 234, an infeed rate controller 236, a header and reel controller 238, a belt conveyor belt controller 240, a cover position controller 242, a residue system controller 244, a machine cleanout controller 245, a zone controller 247, and the system 214 can include other items 246. The controllable subsystems 216 include machine and header actuators 248, a propulsion subsystem 250, a steering subsystem 252, a residue subsystem 138, a machine cleanout subsystem 254, and the subsystems 216 can include various other subsystems 256.

[0046] Figure 2 The agricultural harvester 100 is also shown to receive an information map 258. As described below, the information map 258 includes, for example, a vegetation index map, a vegetation map from a prior or a priori operation, or a predicted residue map. However, the information map 258 can also encompass other types of data obtained prior to the harvesting operation or maps from a prior or a previous operation. Figure 2The diagram also shows an operator 260 capable of operating an agricultural harvester 100. The operator 260 interacts with an operator interface mechanism 218. In some examples, the operator interface mechanism 218 may include a joystick, joystick, steering wheel, linkage, pedals, buttons, dials, keypad, user-actuable elements on a user interface display (e.g., icons, buttons, etc.), microphone and speaker (where speech recognition and speech synthesis are provided), and various other types of control devices. Where a touch-sensitive display system is provided, the operator 260 may interact with the operator interface mechanism 218 using touch gestures. The examples provided above are exemplary and not intended to limit the scope of this disclosure. Therefore, other types of operator interface mechanisms 218 may also be used and are within the scope of this disclosure.

[0047] Using communication system 206 or other methods, information diagram 258 can be downloaded to agricultural harvester 100 and stored in data storage device 202. In some examples, communication system 206 may be a cellular communication system, a system communicating via a wide area network or local area network, a system communicating via a near-field communication network, or a communication system configured to communicate via any or a combination of various other networks. Communication system 206 may also include a system for facilitating the download or transfer of information to and from a Secure Digital (SD) card or a Universal Serial Bus (USB) card, or both.

[0048] The geolocation sensor 204 exemplarily senses or detects the geolocation or orientation of the agricultural harvester 100. The geolocation sensor 204 may include (but is not limited to) a Global Navigation Satellite System (GNSS) receiver that receives signals from a GNSS satellite transmitter. The geolocation sensor 204 may also include a Real-Time Kinematic (RTK) component configured to enhance the accuracy of position data derived from 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.

[0049] Field sensor 208 can be one of the above-mentioned... Figure 1Any of the sensors described. The on-site sensors 208 include on-board sensors 222 mounted on the agricultural harvester 100. For example, these sensors can include perception sensors (e.g., rear-facing monocular or stereo camera systems and image processing systems), image sensors inside the agricultural harvester 100 such as a clean grain camera, or a camera mounted to identify material passing through a residue subsystem off the agricultural harvester 100 or from a grain cleaning subsystem off the agricultural harvester 100. The on-site sensors 208 also include remote on-site sensors 224 that capture on-site information. On-site data includes data acquired from sensors on the harvester or acquired by any sensor in the case of detecting data during a harvesting operation.

[0050] The predictive model generator 210 generates a model that indicates a relationship between values sensed by the field sensors 208 and a metric mapped to the field by the information map 258. For example, if the information map 258 maps vegetation index values to different locations in the field, and the field sensors 208 are sensing values indicative of residue spread width, the information variable to field variable model generator 228 generates a predictive residue model that models a relationship between vegetation index and residue spread width. The predictive residue model can also be generated based on values from the information map 258 and a plurality of field data values generated by the field sensors 208. The predictive map generator 212 then generates a functional predictive residue map that predicts values of the plurality of values sensed by the plurality of field sensors 208 at different locations in the field based on the information map 258 using the predictive residue model generated by the predictive model generator 210. In some examples, the type of values in the functional predictive map 263 can be the same as the type of field data sensed by the field sensors 208. In some cases, the type of values in the functional predictive map 263 can have different units than the data sensed by the field sensors 208. In some examples, the type of values in the functional predictive map 263 can be different than the type of data sensed by the field sensors 208, but related to the type of data sensed by the field sensors 208. For example, in some examples, the type of data sensed by the field sensors 208 can dictate the type of values in the functional predictive map 263. In some examples, the type of data in the functional predictive map 263 can be different than the type of data in the information map 258. In some cases, the type of data in the functional predictive map 263 can have different units than the data in the information map 258. In some examples, the type of data in the functional predictive map 263 can be different than the type of data in the information map 258, but related to the type of data in the information map 258. For example, in some examples, the type of data in the information map 258 can dictate the type of data in the functional predictive map 263. In some examples, the type of data in the functional predictive map 263 is different than one or both of the type of field data sensed by the field sensors 208 and the type of data in the information map 258. In some examples, the type of data in the functional predictive map 263 is the same as one of the type of field data sensed by the field sensors 208 or the type of data in the information map 258, and different than the other.

[0051] As Figure 2As shown, the prediction map 264 predicts values of a sensed characteristic (sensed by the field sensors 208) or a characteristic related to the sensed characteristic at a plurality of locations throughout the field based on the information values in the information map 258 at the locations and using a prediction model. For example, if the prediction model generator 210 has generated a prediction model that indicates a relationship between vegetation moisture and residue spread width, given the moisture values at different locations on the field, the prediction map generator 212 generates a prediction map 264 that predicts values of residue spread width at the different locations on the field. The prediction map 264 is generated using the moisture values at those locations obtained from the moisture map, and the relationship between the moisture values and residue spread width obtained from the prediction model.

[0052] Some variations of the data types mapped in the information map 258, the data types sensed by the field sensors 208, and the data types predicted on the prediction map 264 will now be described.

[0053] In some examples, the data type in the information map 258 is different from the data type sensed by the field sensors 208, and the data type in the prediction map 264 is the same as the data type sensed by the field sensors 208. For example, the information map 258 can be a vegetation index map, and the variable sensed by the field sensors 208 can be yield. Thus, the prediction map 264 can be a predicted yield map that maps predicted yield values to different geographic locations in the field. In another example, the information map 258 can be a vegetation index map, and the variable sensed by the field sensors 208 can be crop height. Thus, the prediction map 264 can be a predicted crop height map that maps predicted crop height values to different geographic locations in the field.

[0054] Additionally, in some examples, the data type in the information map 258 is different from the data type sensed by the field sensors 208, and the data type in the prediction map 264 is different from both the data type in the information map 258 and the data type sensed by the field sensors 208. For example, the information map 258 can be a vegetation index map, and the variable sensed by the field sensors 208 can be crop height. Thus, the prediction map 264 can be a predicted biomass map that maps predicted biomass values to different geographic locations in the field. In another example, the information map 258 can be a vegetation index map, and the variable sensed by the field sensors 208 can be yield. Thus, the prediction map 264 can be a predicted speed map that maps predicted harvester speed values to different geographic locations in the field.

[0055] In some examples, the information map 258 is from a previous pass over the field during or prior to the current operation, and the data type is different from the data type sensed by the field sensor 208, while the data type in the prediction map 264 is the same as the data type sensed by the field sensor 208. For example, the information map 258 can be a seed population map generated during planting, and the variable sensed by the field sensor 208 can be stalk size. As such, the prediction map 264 can be a predicted stalk size map mapping predicted stalk size values to different geographic locations in the field. In another example, the information map 258 can be a hybrid map, and the variable sensed by the field sensor 208 can be crop status, such as standing crop or lodged crop. As such, the prediction map 264 can be a predicted crop status map mapping predicted crop status values to different geographic locations in the field.

[0056] In some examples, the information map 258 is from a previous pass over the field during or prior to the current operation, and the data type is the same as the data type sensed by the field sensor 208, and the data type in the prediction map 264 is also the same as the data type sensed by the field sensor 208. For example, the information map 258 can be a yield map generated in a previous year, and the variable sensed by the field sensor 208 can be yield. As such, the prediction map 264 can be a predicted yield map mapping predicted yield values to different geographic locations in the field. In this example, the prediction model generator 210 can use relative yield differences in the georegistered information map 258 from the previous year to generate a prediction model that models a relationship between the relative yield differences on the information map 258 and the yield values sensed by the field sensor 208 during the current harvesting operation. The prediction map generator 210 then uses the prediction model to generate the predicted yield map.

[0057] In some examples, the prediction map 264 can be provided to a control zone generator 213. The control zone generator 213 groups adjacent portions of a region based on data values associated with the adjacent portions of the prediction map 264 into one or more control zones. A control zone can include two or more contiguous portions of a region (e.g., a field) for which a control parameter corresponding to the control zone for controlling a controllable subsystem is constant. For example, the response time to change a setting of a controllable subsystem 216 can not be sufficient to respond satisfactorily to a change in a value contained in a map such as the prediction map 264. In this case, the control zone generator 213 parses the map and identifies control zones of a defined size to accommodate the response time of the controllable subsystem 216. In another example, control zones can be sized to reduce wear caused by excessive actuator movement resulting from continuous adjustment. In some examples, there can be different sets of control zones for individual controllable subsystems 216 or groups of controllable subsystems 216. The control zones can be added to the prediction map 264 to obtain a prediction control zone map 265. Thus, the prediction control zone map 265 can be similar to the prediction map 264 except that the prediction control zone map 265 includes control zone information defining control zones. Thus, a functional prediction map 263 can or can not include control zones as described herein. Both the prediction map 264 and the prediction control zone map 265 are functional prediction maps 263. In one example, the functional prediction map 263 does not include control zones (e.g., the prediction map 264). In another example, the functional prediction map 263 does include control zones (e.g., the prediction control zone map 265). In some examples, if an intercrop production system is implemented, multiple crops can be present in a field at the same time. In this case, the prediction map generator 212 and the control zone generator 213 are able to identify the location and characteristics of two or more crops and then generate the prediction map 264 and the prediction control zone map 265 accordingly.

[0058] It will also be appreciated that the control zone generator 213 can cluster values to generate control zones and that the control zones can be added to the prediction control zone map 265 or to a separate map that only displays the generated control zones. In some examples, the control zones can be used to control and / or calibrate the agricultural harvester 100. In other examples, the control zones can be presented to the operator 260 and used to control or calibrate the agricultural harvester 100, and in other examples, the control zones can be presented to the operator 260 or another user or stored for later use.

[0059] The prediction map 264 or the prediction control zone map 265 or both are provided to the control system 214, which generates control signals based on the prediction map 264 or the prediction control zone map 265 or both. In some examples, the communication system controller 229 controls the communication system 206 to communicate the prediction map 264 or the prediction control zone map 265 or control signals based on the prediction map 264 or the prediction control zone map 265 to other agricultural harvester machines that are harvesting in the same field. In some examples, the communication system controller 229 controls the communication system 206 to transmit the prediction map 264, the prediction control zone map 265 or both to other remote systems.

[0060] The operator interface controller 231 is operable to generate control signals to control the operator interface mechanisms 218. The operator interface controller 231 is also operable to present the prediction map 264 or the prediction control zone map 265 or other information derived from or based on the prediction map 264, the prediction control zone map 265, or both, to the operator 260. The operator 260 can be a local operator or a remote operator. As an example, the controller 231 generates control signals to control a display mechanism to display one or both of the prediction map 264 and the prediction control zone map 265 to the operator 260. The controller 231 can generate operator actuatable mechanisms that are displayed and actuatable by the operator to interact with the displayed map. The operator can edit the map by, for example, correcting residue spread displayed on the map based on the operator's observations. The setting controller 232 can generate control signals to control a variety of settings on the agricultural harvester 100 based on the prediction map 264, the prediction control zone map 265, or both. For example, the setting controller 232 can generate control signals to control the machine and header actuators 248. In response to the generated control signals, the machine and header actuators 248 operate to control, for example, one or more of sieve and chaffer setting, concave gap, cylinder setting, clean grain fan speed setting, header height, header function, reel speed, reel position, belt conveyor function where the agricultural harvester 100 is coupled to a belt conveyor header, grain header function, in-bin spread control, and other actuators 248 that affect other functions of the agricultural harvester 100. The path planning controller 234 illustratively generates control signals to control the steering subsystem 252 to steer the agricultural harvester 100 according to a desired path. The path planning controller 234 can control a path planning system to generate a route for the agricultural harvester 100 and can control the propulsion subsystem 250 and the steering subsystem 252 to steer the agricultural harvester 100 along the route. The feed rate controller 236 can control a variety of subsystems, such as the propulsion subsystem 250 and the machine actuators 248, to control the feed rate based on the prediction map 264 or the prediction control zone map 265 or both. For example, as the agricultural harvester 100 approaches a patch of weeds having a density value above a selected threshold, the feed rate controller 236 can reduce the speed of the machine 100 to maintain a constant feed rate of biomass through the machine. The header and reel controller 238 can generate control signals to control the header or the reel or other header functions. The belt conveyor belt controller 240 can generate control signals to control the belt conveyor belt or other belt conveyor functions based on the prediction map 264, the prediction control zone map 265, or both. The deck position controller 242 can generate control signals to control the position of a deck included on the header based on the prediction map 264 or the prediction control zone map 265 or both. The residue system controller 244 can generate control signals to control the residue subsystem 138 based on the prediction map 264 or the prediction control zone map 265 or both.The machine clean grain controller 245 can generate control signals to control the machine clean grain subsystem 254. Other controllers included on the agricultural harvester 100 can also control other subsystems based on the prediction map 264 or the prediction control zone map 265 or both.

[0061] Figure 3A and Figure 3B A flowchart is shown that illustrates one example of the operation of the agricultural harvester 100 in generating the prediction map 264 and the prediction control zone map 265 based on the information map 258.

[0062] At block 280, the agricultural harvester 100 receives the information map 258. Examples of the information map 258 or receiving the information map 258 are discussed with reference to blocks 282, 284, and 286. As discussed above, the information map 258 maps values of a variable corresponding to a first characteristic to different locations in a field, as indicated by block 282. As indicated by block 281, receiving the information map 258 can involve selecting one or more information maps from a plurality of possible information maps available. For example, one information map can be a vegetation index map generated from aerial images. Another information map can be a map generated during a previous pass through the field that can be performed by a different machine that performed a previous operation in the field, such as a sprayer or other machine. The process of selecting one or more information maps can be manual, semi-automatic, or automatic. The information map 258 is based on data collected prior to the current harvesting operation. This is indicated by block 284. For example, the data can be collected based on aerial images taken during the previous year, or early in the current growing season, or at other times. The data can be based on data detected other than using aerial images. For example, the agricultural harvester 100 can be equipped with a sensor, such as an internal optical sensor, that identifies weed seeds that exit the agricultural harvester 100. The weed seed data detected by the sensor during harvesting in the previous year can be used as data used to generate the information map 258. The sensed weed data can be combined with other data to generate the information map 258. For example, based on the amount of weed seeds that exit the agricultural harvester 100 at different locations and based on other factors, such as whether the seeds were broadcast or dropped in clumps, the weather conditions, such as wind, at the time the seeds dropped or were broadcast, the drainage conditions that can move the seeds in the field, or other information, the locations of those weed seeds can be predicted, and thus the information map 258 maps the predicted seed locations in the field. The data for the information map 258 can be communicated to the agricultural harvester 100 using the communication system 206 and stored in the data storage device 202. The data for the information map 258 can also be provided to the agricultural harvester 100 in other ways using the communication system 206, and this is indicated by block 286 in the flowchart of FIG. 3. In some examples, the information map 258 can be received by the communication system 206.

[0063] At the start of the harvesting operation, the field sensors 208 generate sensor signals indicative of one or more field data values indicative of a characteristic such as a residue characteristic, as indicated by block 288. Examples of field sensors are discussed with reference to blocks 222, 290, and 226. As explained above, the field sensors 208 include on-board sensors 222, such as a rear-facing camera, remote field sensors 224, such as a UAV-based sensor that flies once to collect field data (shown in block 290), or other types of field sensors specified by the field sensors 226. In some examples, data from the on-board sensors is geo-registered using position, heading, or velocity data from the geo-location sensors 204.

[0064] The predictive model generator 210 controls the information variable to the field variable model generator 228 to generate a model that models the relationship between the mapped values included in the information map 258 and the field values sensed by the field sensors 208, as indicated by block 292. The characteristic or data type represented by the mapped values in the information map 258 and the field values sensed by the field sensors 208 can be the same characteristic or data type, or a different characteristic or data type.

[0065] The relationship or model generated by the predictive model generator 210 is provided to the prediction map generator 212. The prediction map generator 212 uses the predictive model and the information map 258 to generate a prediction map 264 that predicts values of the characteristic sensed by the field sensors 208, or a different characteristic related to the characteristic sensed by the field sensors 208, at different geographic locations in the field being harvested, as indicated by block 294.

[0066] It should be noted that, in some examples, the information map 258 can include two or more different maps, or two or more different layers of a single map. Each layer can represent a different data type than another layer, or the layers can have the same data type obtained at different times. Each of the two or more different maps, or each of the two or more different layers of a map, maps different types of variables to geographic locations in the field. In such examples, the prediction model generator 210 generates a prediction model that models relationships between the field data and each of the different variables mapped by the two or more different maps or the two or more different layers of a map. Similarly, the field sensors 208 can include two or more sensors that each sense a different type of variable. Thus, the prediction model generator 210 generates a prediction model that models relationships between each of the types of variables mapped by the information map 258 and each of the types of variables sensed by the field sensors 208. The prediction map generator 212 can use the prediction model and each of the maps or layers in the information map 258 to generate a functional prediction map 263 that predicts values of each sensed characteristic (or characteristics related to the sensed characteristics) sensed by the field sensors 208 at different locations in the field being harvested.

[0067] The prediction map generator 212 configures the prediction map 264 such that the prediction map 264 is manipulable (or usable) by the control system 214. The prediction map generator 212 can provide the prediction map 264 to the control system 214 or the control zone generator 213, or both. Some examples of different ways in which the prediction map 264 can be configured or output will be described with reference to blocks 296, 295, 299, and 297. For example, the prediction map generator 212 configures the prediction map 264 such that the prediction map 264 includes values that are readable by the control system 214 and used as a basis for generating control signals for one or more different controllable subsystems of the agricultural harvester 100, as indicated by block 296.

[0068] The control zone generator 213 can divide the prediction map 264 into control zones based on the values on the prediction map 264. Geographically contiguous values within a threshold of each other can be grouped into a control zone. The threshold can be a default threshold, or the threshold can be set based on operator input, based on input from an automation system, or based on other criteria. The size of the zones can be based on the responsiveness of the control system 214, controllable subsystems 216, based on wear considerations, or other criteria, as indicated by block 295. The prediction map generator 212 configures the prediction map 264 for presentation to an operator or other user. The control zone generator 213 can configure the prediction control zone map 265 for presentation to an operator or other user. This is indicated by block 299. When presented to an operator or other user, the presentation of the prediction map 264 or the prediction control zone map 265 or both can include the predicted values on the prediction map 264 related to geographic locations, the control zones on the prediction control zone map 265 related to geographic locations, and one or more of the set values or control parameters being used based on the values on the map 264 or the zones on the prediction control zone map 265. In another example, the presentation can include more abstract information or more detailed information. The presentation can also include a confidence that indicates how accurate the predicted values on the prediction map 264 or the zones on the prediction control zone map 265 are to the measured values that can be measured by sensors on the agricultural harvester 100 as the agricultural harvester 100 moves through the field. Furthermore, in cases where the information is presented to more than one location, a verification and authorization system can be provided to implement a verification and authorization process. For example, there can be a hierarchy of individuals that are authorized to view and change the information of the maps and other presentations. As an example, an onboard display device can display the maps approximately in real-time locally on the machine, and / or the maps can also be generated at one or more remote locations. In some examples, each physical display device at each location can be associated with a person or user permission level. The user permission level can be used to determine which display indicia are visible on the physical display device, and which values the corresponding person can change. As an example, a local operator of the machine 100 can not be able to see the information corresponding to the prediction map 264 or make any changes to the machine operation. However, a supervisor (e.g., at a remote location) can be able to see the prediction map 264 on a display, but be prevented from making any changes. A manager that can be at a separate remote location can be able to see all of the elements on the prediction map 264, and also be able to change the prediction map 264. In some cases, the prediction map 264 can be accessible and changeable by the manager at a remote location, usable for machine control. This is one example of an authorization hierarchy that can be implemented. The prediction map 264 or the prediction control zone map 265 or both can also be configured in other ways, as indicated by block 297.

[0069] At block 298, inputs from the geo-location sensor 204 and other field sensors 208 are received by the control system. In particular, at block 300, the control system 214 detects inputs from the geo-location sensor 204, identifying the geo-location of the agricultural harvester 100. Block 302 represents the control system 214 receiving sensor inputs indicative of the trajectory or heading of the agricultural harvester 100, and block 304 represents the control system 214 receiving the speed of the agricultural harvester 100. Block 306 represents the control system 214 receiving other information from the various field sensors 208.

[0070] At block 308, the control system 214 generates control signals to control the controllable subsystems 216 based on the prediction map 264 or the prediction control zone map 265 or both, and the inputs from the geo-location sensor 204 and any other field sensors 208. At block 310, the control system 214 applies the control signals to the controllable subsystems. It will be understood that the particular control signals generated and the particular controllable subsystems 216 being controlled can vary based on one or more different things. For example, the control signals generated and the controllable subsystems 216 being controlled can vary based on the type of prediction map 264 or prediction control zone map 265 or both being used. Similarly, the control signals generated, the controllable subsystems 216 being controlled, and the timing of the control signals can vary based on the various delays of the crop stream through the agricultural harvester 100 and the responsiveness of the controllable subsystems 216.

[0071] As an example, the prediction map 264 in the form of a prediction residue map can be used to control one or more subsystems 216. For example, the prediction residue map can include residue characteristic values that are geo-registered to locations within the field being harvested. Residue characteristic values from the prediction residue map can be extracted and used to control one or more components of the residue system 138. For example, the spreader 142 can be controlled to more evenly spread residue or to spread residue at desired locations. Thus, values obtained from the prediction residue map or other types of prediction maps can be used to generate a variety of other control signals to control one or more controllable subsystems 216.

[0072] At block 312, it is determined whether the harvesting operation has been completed. If harvesting has not been completed, the process proceeds to block 314, where field sensor data from the geo-location sensor 204 and the field sensors 208 (and possibly other sensors) is continually read.

[0073] In some examples, at block 316, the agricultural harvester 100 can also detect a learning trigger criterion to perform machine learning on one or more of the prediction map 264, the prediction control zone map 265, the models generated by the prediction model generator 210, the zones generated by the control zone generator 213, one or more control algorithms implemented by the controllers in the control system 214, and other trigger-based learning.

[0074] The learning trigger criterion can include any of a variety of different criteria. Some examples of detecting a trigger criterion are discussed with reference to blocks 318, 320, 321, 322, and 324. For example, in some examples, the trigger-based learning can involve recreating the relationships used to generate the prediction models when a threshold amount of field sensor data is obtained from the field sensors 208. In these examples, receiving more than a threshold amount of field sensor data from the field sensors 208 triggers or causes the prediction model generator 210 to generate a new prediction model used by the prediction map generator 212. Thus, as the agricultural harvester 100 continues the harvesting operation, receiving a threshold amount of field sensor data from the field sensors 208 triggers creating a new relationship represented by the prediction models generated by the prediction model generator 210. Further, a new prediction map 264, prediction control zone map 265, or both can be regenerated using the new prediction models. Block 318 represents detecting a threshold amount of field sensor data for triggering creation of a new prediction model.

[0075] In other examples, the learning trigger criterion can be based on how the field sensor data from the field sensors 208 changes, for example, over time or compared to previous values. For example, if a change in the field sensor data (or a relationship between the field sensor data and the information in the information map 258) is within a selected range, or less than a defined amount, or below a threshold, the prediction model generator 210 does not generate a new prediction model. As a result, the prediction map generator 212 does not generate a new prediction map 264 and / or prediction control zone map 265. However, for example, if the change in the field sensor data is outside of a selected range, greater than a defined amount or threshold, or above a threshold, the prediction model generator 210 generates a new prediction model using all or a portion of the newly received field sensor data used by the prediction map generator 212 to generate a new prediction map 264. At block 320, the change in the field sensor data (e.g., the magnitude of the amount of data outside of the selected range, or the magnitude of the change in the relationship between the field sensor data and the information in the information map 258) can be used as a trigger to cause generation of a new prediction model and prediction map. Continuing the example described above, the threshold, range, and defined amount can be set to a default value, set by an operator or user through user interface interaction, set by an automated system, or otherwise set.

[0076] Other learning trigger criteria can also be used. For example, if the prediction model generator 210 switches to a different information map (different from the initially selected information map 258), the switch to the different information map can trigger the prediction model generator 210, the prediction map generator 212, the control zone generator 213, the control system 214, or other item to relearn. In another example, the agricultural harvester 100 transitioning to a different terrain or a different control zone can also be used as a learning trigger criterion.

[0077] In some cases, the operator 260 can also edit the prediction map 264 or the prediction control zone map 265 or both. Such editing can change the values on the prediction map 264 and / or change the size, shape, location, or existence of the control zones on the prediction control zone map 265. Block 321 shows that the edited information can be used as a learning trigger criterion.

[0078] In some cases, the operator 260 can also observe that the automatic control of the controllable subsystem is not as desired by the operator. In these cases, the operator 260 can provide manual adjustments to the controllable subsystem, which reflects that the operator 260 desires the controllable subsystem to operate differently than commanded by the control system 214. Thus, the operator 260 manually changing the settings can cause one or more of the following to be performed based on the adjustments made by the operator 260 (as indicated by block 322): causing the prediction model generator 210 to relearn the model, causing the prediction map generator 212 to regenerate the map 264, causing the control zone generator 213 to regenerate one or more control zones on the prediction control zone map 265, and causing the control system 214 to relearn the control algorithm or perform machine learning on one or more of the controller components 232-246 in the control system 214. Block 324 represents using other trigger-based learning criteria.

[0079] In other examples, the relearning can be performed periodically or intermittently based on, for example, a selected time interval (e.g., a discrete time interval or a variable time interval), as indicated by block 326.

[0080] If the relearning is triggered (whether based on a learning trigger criterion or based on an elapsed time interval), as indicated by block 326, one or more of the prediction model generator 210, the prediction map generator 212, the control zone generator 213, and the control system 214 perform machine learning to generate new prediction models, new prediction maps, new control zones, and new control algorithms, respectively, based on the learning trigger criterion. The new prediction models, the new prediction maps, and the new control algorithms are generated using any additional data collected since the last learning operation was performed. Performing the relearning is indicated by block 328.

[0081] If the harvesting operation has been completed, the operation moves from block 312 to block 330, in which one or more of the prediction map 264, the prediction control zone map 265, and the prediction model generated by the prediction model generator 210 are stored. The prediction map 264, the prediction control zone map 265, and the prediction model can be stored locally on the data storage device 202 or can be transmitted to a remote system using the communication system 206 to facilitate subsequent use.

[0082] It will be noted that while some examples herein describe the prediction model generator 210 and the prediction map generator 212 receiving an information map when generating a prediction model and a functional prediction map, respectively, in other examples, the prediction model generator 210 and the prediction map generator 212 can receive other types of maps when generating a prediction model and a functional prediction map, respectively, including a prediction map, such as a functional prediction map generated during a harvesting operation.

[0083] Figure 4 is Figure 1 a block diagram of a portion of the agricultural harvester 100 shown. In particular, the prediction model generator 210 and the prediction map generator 212 are shown in more detail, along with the information flow between the various components shown. Figure 4 an example of the prediction model generator 210 and the prediction map generator 212 is shown in more detail. Figure 4 The information flow between the various components shown is also shown. The prediction model generator 210 receives one or more of a vegetation index map 331, a moisture map 332, and a terrain map 333 as an information map. The prediction model generator 210 also receives a geographic location 334 or an indication of a geographic location from the geographic location sensor 204. The field sensor 208 illustratively includes a residue sensor, such as a residue sensor 336, as well as a processing system 338. In some cases, the residue sensor 336 can be located on the agricultural harvester 100. In other examples, the residue sensor 336 is remote from the agricultural harvester 100 and senses an area over which the agricultural harvester has traveled and senses residue characteristics. The processing system 338 processes sensor data generated from the residue sensor 336 to generate processed data, some examples of which are described below.

[0084] In some examples, the residue sensor 336 can be an optical sensor (e.g., a camera) that generates images of areas of the field that have been harvested. In some cases, the optical sensor can be disposed on the agricultural harvester 100 to collect images of areas adjacent to the agricultural harvester 100 (e.g., areas behind, to the side of, or in another direction relative to the agricultural harvester 100 as the agricultural harvester 100 moves through the field during a harvesting operation). The optical sensor can also be located on or within the agricultural harvester 100 to acquire images of one or more portions of the exterior or interior of the agricultural harvester 100. The processing system 338 processes one or more images obtained via the residue sensor 336 to generate processed image data that identifies one or more characteristics of residue in the images. Residue characteristics detected by the processing system 338 can include dimensional spread (width of spread and distance of spread back), residue uniformity, and residue content (e.g., chopped stalk mass, stalk size, weed seeds, etc.).

[0085] The in-field sensor 208 can be or include other types of sensors, such as a camera positioned along a path that severed crop material travels in the agricultural harvester 100 (hereinafter referred to as a “process camera”). The process camera can be located inside the agricultural harvester 100 and can capture images of crop material, including seeds, as the crop material moves through or is expelled from the agricultural harvester 100. Thus, in some examples, the processing system 338 is operable to detect the presence of material traveling through the agricultural harvester 100 during a harvesting operation.

[0086] In other examples, the residue sensor 336 can rely on the wavelength of electromagnetic energy and the way that electromagnetic energy is reflected, absorbed, attenuated, or transmitted through residue material by the residue material. The residue sensor 336 can sense other electromagnetic properties of residue material, such as the dielectric constant, as the residue material travels between two capacitive plates. Other material properties and sensors can also be used. In some examples, raw or processed data from the residue sensor 336 can be presented to the operator 260 via the operator interface mechanism 218. The operator 260 can be on the agricultural harvester 100 or at a remote location.

[0087] This discussion continues with respect to examples in which the residue sensor 336 is an image sensor (e.g., a camera). It should be understood that this is merely one example, and other examples of the above-mentioned sensors are also contemplated herein as residue sensors 336. As Figure 4As shown, the example predictive model generator 210 includes one or more of a residue characteristic versus vegetation index model generator 342, a residue characteristic versus humidity model generator 344, and a residue characteristic versus terrain model generator 346. In other examples, the predictive model generator 210 can include more, fewer, or different components than those shown in the example. Thus, in some examples, the predictive model generator 210 can also include other items 348, which can include other types of predictive model generators to generate other types of residue models. Figure 4

[0088] The model generator 342 determines a relationship between residue characteristics detected in the image data 340 and at a geographic location corresponding to a location to which the image data 340 is georegistered and vegetation index values from the vegetation index map 331 corresponding to the same said location in the field at which the residue characteristics were detected. Based on this relationship established by the model generator 342, the model generator 342 generates a predictive residue model 350. This predictive residue model 350 is used by the residue map generator 352 to predict residue characteristics at different locations in the field based on georegistered vegetation index values at the different locations in the field contained in the vegetation index map 331.

[0089] The model generator 344 determines a relationship between residue characteristics in the processed image data 340 and at a geographic location corresponding to a location to which the image data 340 is georegistered and humidity values at the same said geographic location. Again, the humidity values are georegistered values contained in the humidity map 332. The model generator 344 then generates a predictive residue model 350 that is used by the residue map generator 352 to predict residue characteristics at a location in the field based on a humidity value at the location in the field.

[0090] The model generator 346 determines a relationship between residue characteristics in the processed image data 340 and at a geographic location corresponding to a location to which the image data 340 is georegistered and terrain characteristic values at the same said location from the terrain map 333. The model generator 346 generates a predictive residue model 350 that is used by the residue map generator 352 to predict residue characteristics at a particular location in the field based on terrain characteristic values at the particular location in the field.

[0091] ​In view of the foregoing, the prediction model generator 210 is operable to generate multiple prediction residue models, such as one or more of the prediction residue models generated by model generators 342, 344, and 346. In another example, two or more of the above-mentioned prediction residue models can be combined into a single prediction residue model, which can be used to predict residue characteristics based on two or more of vegetation index values, humidity values, and topographic values ​​at different locations in the field. Any one or a combination of these residue models is generated by... Figure 4 The residue model 350 is uniformly represented in the model.

[0092] The predicted residue model 350 is provided to the prediction map generator 212. Figure 4 In one example, the prediction map generator 212 includes a residue map generator 352. In other examples, the prediction map generator 212 may include additional or different map generators. Thus, in some examples, the prediction map generator 212 may include additional items 358, which may include other types of map generators to generate maps of other types of characteristics. The residue map generator 352 receives a predicted residue model 350 and generates a prediction map based on one or more of a plant index map 331, a humidity map 332, and a topographic map 333, as well as the predicted residue model 350, which predicts residue characteristics at different locations in the field.

[0093] Prediction map generator 212 outputs one or more predicted residue maps 360, which predict one or more residue characteristics, such as residue spread width, residue thickness, and residue contents. The generated predicted residue maps 360 can be provided to control region generator 213 and / or control system 214. Control region generator 213 generates control regions and incorporates those control regions into functional prediction maps (i.e., prediction maps 360) to produce a predicted control region map 265. One or both of prediction map 264 and predicted control region map 265 can be provided to control system 214, which generates control signals based on prediction map 264 and / or predicted control region map 265 to control one or more controllable subsystems 216.

[0094] Figure 5is a flowchart of an example of the operations of the prediction model generator 210 and the prediction map generator 212 in generating the prediction residue model 350 and the prediction residue map 360. At block 362, the prediction model generator 210 and the prediction map generator 212 receive the vegetation index map 331, the humidity map 332, the terrain map 333, or some combination thereof. At block 364, the processing system 338 receives one or more sensor signals from the residue sensor 336. As discussed above, the residue sensor 336 can be: a camera, such as a rearview camera 366; an optical sensor 368, such as a camera that at least partially views the interior of the agricultural harvester; or another type of residue sensor 370. For example, other residue sensors 370 can include impact force sensors or other electromagnetic sensors.

[0095] At block 372, the processing system 338 processes the one or more received sensor signals to generate data indicative of residue characteristics. At block 373, the sensor data can be indicative of residue spread. Residue spread can include one or more dimensions relative to the combine harvester, such as relative to the width of the agricultural harvester, or distance expelled from the agricultural harvester, or distance offset from the agricultural harvester. In some cases, as indicated by block 374, the sensor data can be indicative of residue thickness. Residue thickness is indicative of the depth or amount of residue on the surface of the field. In some cases, as indicated by block 375, the sensor data can be indicative of residue uniformity. Residue uniformity is indicative of the distribution of residue across the surface. In some cases, as indicated by block 376, the sensor data can be indicative of residue content. Residue content is indicative of the type of material in the residue (e.g., weeds, crop plants, stalks, weed seeds, grain, etc.) and / or the quality of the material (e.g., chopped stalk length, seed crush quality, etc.). The sensor data can also include other data, as indicated by block 377.

[0096] At block 382, the prediction model generator 210 also obtains a geographic location corresponding to the image data. For example, the prediction model generator 210 can obtain the geographic location from the geographic location sensor 204 and determine the precise geographic location at which the image was taken or the precise geographic location to which the image data 340 corresponds based on machine delay, machine speed, camera field of view, etc. In some cases, pixels in the image are associated with geographic locations on the field, and the pixel locations of the sensed data are converted to geographic locations on the field.

[0097] At block 384, the predictive model generator 210 generates one or more predictive residue models (e.g., residue models 350) that model a relationship between values obtained from the information maps (e.g., information maps 258) and sensed residue characteristic values or related characteristics by the in-field sensors 208. For example, the predictive model generator 210 can generate a predictive residue model that models a relationship between vegetation index values and sensed residue characteristics indicated by image data obtained from the in-field sensors 208.

[0098] At block 385, the predictive residue models, such as the predictive residue models 350, are provided to the predictive map generator 212 that generates a predictive residue map 360 that maps a predicted residue characteristic based on one or more of the vegetation index map 331, the moisture map 332, and the terrain map 333 and the predictive residue models 350. For example, in some examples, the predictive residue map 360 predicts residue spread, as indicated by block 386. In some examples, the predictive residue map 360 predicts residue thickness, as indicated by block 387. In some examples, the predictive residue map 360 predicts residue uniformity, as indicated by block 388. In some examples, the predictive residue map 360 predicts residue content, as indicated by block 389. In some examples, the predictive residue map 360 predicts other items or some combination of items, as indicated by block 390. Further, the predictive residue map 360 can be generated during the course of the agricultural operation. Thus, the predictive residue map 360 is generated as the agricultural harvester is moving through the field to perform the agricultural operation.

[0099] At block 394, the predictive map generator 212 outputs the predictive residue map 360. At block 391, the predictive residue map generator 212 outputs the predictive residue map for presentation to the operator 260 and possible interaction with the operator 260. At block 393, the predictive map generator 212 can configure the map 360 for use by the control system 214. At block 395, the predictive map generator 212 can also provide the map 360 to the control zone generator 213 for use in control zone generation. At block 397, the predictive map generator 212 also configures the predictive residue map 360 in other ways. The predictive residue map 360 (with or without control zones) is provided to the control system 214. At block 396, the control system 214 generates control signals to control the controllable subsystems 216 based on the predictive residue map 360.

[0100] Figure 6A block diagram illustrating one example of a control zone generator 213 is shown. The control zone generator 213 includes a work machine actuator (WMA) selector 486, a control zone generation system 488, and a regime zone generation system 490. The control zone generator 213 can also include other items 492. The control zone generation system 488 includes a control zone criteria identifier component 494, a control zone boundary definition component 496, a target setting identifier component 498, and other items 520. The regime zone generation system 490 includes a regime zone criteria identification component 522, a regime zone boundary definition component 524, a settings resolver identifier component 526, and other items 528. Before describing the overall operation of the control zone generator 213 in more detail, a brief description of some of the items in the control zone generator 213 and their respective operations will first be provided.

[0101] The agricultural harvester 100 or other work machine can have multiple different types of controllable actuators that perform different functions. The controllable actuators on the agricultural harvester 100 or other work machine are collectively referred to as work machine actuators (WMAs). Each WMA can be independently controlled based on values on the functional prediction map, or the WMAs can be controlled in groups based on one or more values on the functional prediction map. Thus, the control zone generator 213 can generate control zones that correspond to each individually controllable WMA, or to groups of WMAs that are controlled in coordination with one another.

[0102] The WMA selector 486 selects a WMA or group of WMAs for which a corresponding control zone is to be generated. The control zone generation system 488 then generates a control zone for the selected WMA or group of WMAs. Different criteria can be used in identifying the control zone for each WMA or group of WMAs. For example, for one WMA, the WMA response time can be used as a criterion for defining the boundaries of a control zone. In another example, a wear characteristic (e.g., how much a particular actuator or mechanism wears due to its movement) can be used as a criterion for identifying the boundaries of a control zone. The control zone criteria identifier component 494 identifies the specific criteria that will be used to define a control zone for the selected WMA or group of WMAs. The control zone boundary definition component 496 processes values on the functional prediction map in the analysis to define the boundaries of a control zone on the functional prediction map in the analysis based on the values on the functional prediction map in the analysis and based on the control zone criteria for the selected WMA or group of WMAs.

[0103] The target setting identifier component 498 sets the values that will be used to control the target settings of the WMA or WMA group in different control zones. For example, if the selected WMA is the propulsion system 250 and the functional prediction graph under analysis is the functional prediction speed graph 438, the target setting in each control zone can be a target speed setting based on the speed values contained in the functional prediction speed graph 238 within the identified control zone.

[0104] In some examples, where the agricultural harvester 100 is controlled based on the current or future location of the agricultural harvester 100, multiple target settings are possible for a WMA at a given location. In this case, the target settings can have different values and can compete with each other. Therefore, the target settings need to be resolved so that only a single target setting is used to control the WMA. For example, where the WMA is an actuator that is controlled in the propulsion system 250 to control the speed of the agricultural harvester 100, there can be multiple different competing sets of criteria that are considered by the control zone generation system 488 when identifying the control zones and the target settings for the selected WMA in the control zones. For example, different target settings for controlling the speed of the machine can be generated based on, for example, detected or predicted feed rate values, detected or predicted fuel efficiency values, detected or predicted grain loss values, or a combination of these values. However, at any given time, the agricultural harvester 100 cannot travel over the ground at multiple speeds simultaneously. Rather, at any given time, the agricultural harvester 100 travels at a single speed. Therefore, one of the competing target settings is selected to control the speed of the agricultural harvester 100.

[0105] Therefore, in some examples, the dynamic zone generation system 490 generates dynamic zones to resolve the multiple different competing target settings. The dynamic zone criteria identification component 522 identifies criteria for establishing dynamic zones for the selected WMA or WMA group on the functional prediction graph under analysis. Some criteria that can be used to identify or define dynamic zones include, for example, crop type or crop class based on the planting map, or another source of crop type or crop class, weed type, weed density, or crop status such as whether the crop is laid flat, partially laid flat, or standing. Just as each WMA or WMA group can have a corresponding control zone, different WMA or WMA groups can also have a corresponding dynamic zone. The dynamic zone boundary definition component 524 identifies the boundaries of the dynamic zones on the functional prediction graph under analysis based on the dynamic zone criteria identified by the dynamic zone criteria identification component 522.

[0106] In some examples, the dynamic zones can overlap one another. For example, a crop class dynamic zone can overlap some or all of a crop status dynamic zone. In such examples, different dynamic zones can be assigned to a priority hierarchy such that, in the event of overlap between two or more dynamic zones, the dynamic zone assigned a higher position or importance in the priority hierarchy is prioritized over the dynamic zone with a lower position or importance in the priority hierarchy. The priority hierarchy of the dynamic zones can be set manually or can be set automatically using a rules-based system, a model-based system, or other system. As one example, in the event of overlap between a laid crop dynamic zone and a crop class dynamic zone, the laid crop dynamic zone can be assigned a greater importance in the priority hierarchy than the crop class dynamic zone such that the laid crop dynamic zone is prioritized.

[0107] Further, for a given WMA or group of WMAs, each dynamic zone can have a unique setting resolver. The setting resolver identifier component 526 identifies a particular setting resolver for each dynamic zone identified on the functional prediction map under analysis and identifies the particular setting resolver for the selected WMA or group of WMAs.

[0108] Once the setting resolver for a particular dynamic zone is identified, the setting resolver can be used to resolve competing target settings in which more than one target setting is identified based on the control zone. Different types of setting resolvers can have different forms. For example, the setting resolver identified for each dynamic zone can include a human selection resolver in which the competing target settings are presented to an operator or other user for resolution. In another example, the setting resolver can include a neural network or other artificial intelligence or machine learning system. In such a case, the setting resolver can resolve the competing target settings based on predicted quality metrics or historical quality metrics corresponding to each of the different target settings. As an example, an increased vehicle speed setting can decrease the time to harvest a field and decrease the corresponding time-based labor and equipment costs, but can increase grain loss. A decreased vehicle speed setting can increase the time to harvest a field and increase the corresponding time-based labor and equipment costs, but can decrease grain loss. When grain loss or harvesting time is selected as a quality metric, in the event of two competing vehicle speed setting values, the predicted or historical values for the selected quality metric can be used to resolve the speed setting. In certain cases, the setting resolver can be a set of threshold rules that can be used in place of or in addition to the dynamic zones. An example of a threshold rule can be expressed as follows:

[0109] If the predicted biomass value that is within 20 feet of the header of the agricultural harvester 100 is greater than x kilograms (where x is a selected or predetermined value), then the target setting value that is based on the feed rate rather than other competing target settings is used, otherwise the target setting value that is based on the grain loss rather than other competing target settings is used.

[0110] The setting resolver can be a logical component that executes logical rules in identifying the target setting. For example, the setting resolver can resolve the target setting while attempting to minimize the harvesting time or minimize the total harvesting cost or maximize the harvested grain, or other variables that are calculated based on functions of different candidate target settings. The harvesting time can be minimized when the amount of harvesting completed is reduced to or below a selected threshold. The total harvesting cost can be minimized when the total harvesting cost is reduced to or below a selected threshold. The harvested grain can be maximized when the amount of harvested grain is increased to or above a selected threshold.

[0111] Figure 7 is a flowchart illustrating one example of the operation of the control zone generator 213 in generating control zones and dynamic zones for a graph received by the control zone generator 213 for zone processing (e.g., for the in-analysis graph).

[0112] At block 530, the control zone generator 213 receives the in-analysis graph for processing. In one example, as shown in block 532, the in-analysis graph is a functional prediction graph. For example, the in-analysis graph can be one of the functional prediction graphs 436, 437, 438, or 440. Block 534 indicates that the in-analysis graph can also be other graphs.

[0113] At block 536, the WMA selector 486 selects a WMA or group of WMAs for which to generate a control zone on the map in the analysis. At block 538, the control zone criteria identification component 494 obtains control zone defining criteria for the selected WMA or group of WMAs. Block 540 indicates such an example in which the control zone criteria are or include wear characteristics of the selected WMA or group of WMAs. Block 542 indicates such an example in which the control zone defining criteria are or include magnitudes and variations of input source data, such as magnitudes and variations of values on the map in the analysis or input from various field sensors 208. Block 544 indicates such an example in which the control zone defining criteria are or include physical machine characteristics, such as physical dimensions of the machine, speeds of different subsystem operations, or other physical machine characteristics. Block 546 indicates such an example in which the control zone defining criteria are or include responsiveness of the selected WMA or group of WMAs in reaching set values of new commands. Block 548 indicates such an example in which the control zone defining criteria are or include machine performance metrics. Block 550 indicates such an example in which the control zone defining criteria are or include operator preferences. Block 552 indicates such an example in which the control zone defining criteria are or also include other items. Block 549 indicates such an example in which the control zone defining criteria are time-based, meaning that the agricultural harvester 100 will not cross the boundary of the control zone until a selected amount of time has elapsed since the agricultural harvester 100 entered the particular control zone. In some cases, the selected amount of time can be a minimum amount of time. Thus, in some cases, the control zone defining criteria can prevent the agricultural harvester 100 from crossing the boundary of the control zone until at least the selected amount of time has elapsed. Block 551 indicates such an example in which the control zone defining criteria are based on a selected size value. For example, control zone defining criteria based on a selected size value can exclude the definition of control zones that are smaller than the selected size. In some cases, the selected size can be a minimum size.

[0114] At block 554, the dynamic zone criteria identification component 522 obtains dynamic zone defining criteria for the selected WMA or group of WMAs. Block 556 indicates such an example in which the dynamic zone defining criteria are based on manual input from the operator 260 or another user. Block 558 shows such an example in which the dynamic zone defining criteria are based on crop type or crop class. Block 560 shows such an example in which the dynamic zone defining criteria are based on weed type and / or weed density. Block 562 shows such an example in which the dynamic zone defining criteria are based on or include crop status. Block 564 indicates such an example in which the dynamic zone defining criteria are also or include other criteria. For example, the dynamic zone defining criteria are based on or include terrain characteristics.

[0115] At block 566, the control zone boundary defining component 496 generates the boundaries of the control zones on the map under analysis based on the control zone criteria. The dynamic zone boundary defining component 524 generates the boundaries of the dynamic zones on the map under analysis based on the dynamic zone criteria. Block 568 indicates an example in which zone boundaries are identified for both control zones and dynamic zones. Block 570 shows that the target setting identifier component 498 identifies the target setting for each of the control zones. Control zones and dynamic zones can also be generated in other ways, and this is indicated by block 572.

[0116] At block 574, the setting resolver identifier component 526 identifies the setting resolver for the selected WMA in each dynamic zone defined by the dynamic zone boundary defining component 524. As discussed above, the dynamic zone resolver can be a human resolver 576, an artificial intelligence or machine learning system resolver 578, a resolver based on a forecasted or historical quality of each competing target setting 580, a rules-based resolver 582, a performance criteria-based resolver 584, or other resolver 586.

[0117] At block 588, the WMA selector 486 determines whether there are more WMAs or groups of WMAs to process. If there are additional WMAs or groups of WMAs that need to be processed, processing returns to block 436 in which the next WMA or group of WMAs for which control zones and dynamic zones are to be defined is selected. When there are no additional WMAs or groups of WMAs for which control zones or dynamic zones are to be generated left, processing moves to block 590 in which the control zone generator 213 outputs a map for each of the WMAs or groups of WMAs with control zones, target settings, dynamic zones, and setting resolvers. As discussed above, the output map can be presented to the operator 260 or another user; the output map can be provided to the control system 214; or the output map can be output in other ways.

[0118] Figure 8 One example is shown in which the control system 214 controls the operation of the agricultural harvester 100 based on the map output by the control zone generator 213. Thus, at block 592, the control system 214 receives the map of the work site. In some cases, the map can be a functional prediction map that can include control zones and dynamic zones (as shown in block 594). In some cases, the received map can be a functional prediction map that excludes control zones and dynamic zones. Block 596 indicates an example in which the received map of the work site can be an information map with control zones and dynamic zones identified on the information map. Block 598 indicates an example in which the received map can include multiple different maps or multiple different map layers. Block 610 indicates an example in which the received map can also take other forms.

[0119] At block 612, the control system 214 receives a sensor signal from the geo-location sensor 204. The sensor signal from the geo-location sensor 204 can include data indicative of a geo-location 614 of the agricultural harvester 100, a speed 616 of the agricultural harvester 100, a heading 618 of the agricultural harvester 100, or other information 620. At block 622, the zone controller 247 selects a dynamic zone, and at block 624, the zone controller 247 selects a control zone on the map based on the geo-location sensor signal. At block 626, the zone controller 247 selects a WMA or group of WMAs to be controlled. At block 628, the zone controller 247 obtains one or more target settings for the selected WMA or group of WMAs. The target settings obtained for the selected WMA or group of WMAs can come from a variety of different sources. For example, block 630 illustrates an example in which one or more of the target settings for the selected WMA or group of WMAs is based on input from the control zone on the map from the job site. Block 632 illustrates an example in which one or more of the target settings is obtained from manual input by the operator 260 or another user. Block 634 illustrates an example in which the target settings are obtained from the field sensors 208. Block 636 illustrates an example in which one or more target settings are obtained from one or more sensors on other machines simultaneously operating in the same field as the agricultural harvester 100 or from one or more sensors on machines that have operated in the same field in the past. Block 638 illustrates an example in which the target settings are also obtained from other sources.

[0120] At block 640, the zone controller 247 accesses the setting resolver for the selected dynamic zone and controls the setting resolver to resolve the competing target settings into resolved target settings. As discussed above, in some cases, the setting resolver can be a manual resolver, in which case the zone controller 247 controls the operator interface mechanism 218 to present the competing target settings to the operator 260 or another user for resolution. In some cases, the setting resolver can be a neural network or other artificial intelligence or machine learning system, and the zone controller 247 submits the competing target settings to the neural network, artificial intelligence, or machine learning system for selection. In certain cases, the setting resolver can be based on predicted or historical quality metrics, based on threshold rules, or based on a logical component. In any of these latter examples, the zone controller 247 executes the setting resolver to obtain the resolved target settings based on predicted or historical quality metrics, based on threshold rules, or in the case of using a logical component.

[0121] At block 642, in the event that the zone controller 247 has identified a resolved target setting, the zone controller 247 provides the resolved target setting to other controllers in the control system 214 that generate control signals based on the resolved target setting and apply the control signals to the selected WMA or WMA group. For example, in the event that the selected WMA is a machine or header actuator 248, the zone controller 247 provides the resolved target setting to the setting controller 232 or the header / actual controller 238 or both to generate control signals based on the resolved target setting and those generated control signals are applied to the machine or header actuator 248. At block 644, if additional WMAs or additional WMA groups are to be controlled at the current geographic location of the agricultural harvester 100 (as detected at block 612), the process returns to block 626 where the next WMA or WMA group is selected. The process represented by blocks 626 through 644 continues until all WMAs or WMA groups to be controlled at the current geographic location of the agricultural harvester 100 have been addressed. If no additional WMAs or WMA groups remain to be controlled at the current geographic location of the agricultural harvester 100, the process proceeds to block 646 where the zone controller 247 determines whether additional control zones to be considered exist in the selected dynamic zone. If additional control zones to be considered exist, the process returns to block 624 where the next control zone is selected. If no additional control zones need to be considered, the process proceeds to block 648 where a determination is made as to whether additional dynamic zones remain to be considered. The zone controller 247 determines whether additional dynamic zones remain to be considered. If additional dynamic zones remain to be considered, the process returns to block 622 where the next dynamic zone is selected.

[0122] At block 650, the zone controller 247 determines whether the operation being performed by the agricultural harvester 100 is complete. If not, the zone controller 247 determines whether the control zone criteria have been met to continue processing, as shown at block 652. For example, as mentioned above, the control zone defining criteria can include criteria defining when the agricultural harvester 100 can cross the control zone boundary. For example, whether the agricultural harvester 100 can cross the control zone boundary can be defined by a selected time period, meaning that the agricultural harvester 100 is prevented from crossing the zone boundary until the selected amount of time has elapsed. In this case, at block 652, the zone controller 247 determines whether the selected time period has elapsed. Additionally, the zone controller 247 can continuously perform the processing. Thus, the zone controller 247 does not wait for any particular time period before continuing to determine whether the operation of the agricultural harvester 100 is complete. Where the zone controller 247 determines that it is time to continue processing, the processing continues at block 612, where the zone controller 247 again receives input from the geo-location sensor 204. It should also be understood that the zone controller 247 can use a multiple-input, multiple-output controller to simultaneously control the WMAs and WMA groups, rather than sequentially controlling the WMAs and WMA groups.

[0123] Figure 9 FIG. 23 is a block diagram illustrating one example of an operator interface controller 231. In the illustrated example, the operator interface controller 231 includes an operator input command processing system 654, other controller interaction system 656, a speech processing system 658, and an action signal generator 660. The operator input command processing system 654 includes a speech management system 662, a touch gesture management system 664, and other items 666. The other controller interaction system 656 includes a controller input processing system 668 and a controller output generator 670. The speech processing system 658 includes a trigger detector 672, a recognition component 674, a synthesis component 676, a natural language understanding system 678, a dialog management system 680, and other items 682. The action signal generator 660 includes a visual control signal generator 684, an audio control signal generator 686, a haptic control signal generator 688, and other items 690. A brief description of some of the items in the operator interface controller 231 and their associated operations is provided before describing the operation of the example operator interface controller 231 in managing various operator interface actions. Figure 9

[0124] ​The operator input command processing system 654 detects operator inputs on the operator interface mechanisms 218 and processes these command inputs. The speech management system 662 detects speech inputs and manages interaction with the speech processing system 658 to process speech command inputs. The touch gesture management system 664 detects touch gestures on touch sensitive elements in the operator interface mechanisms 218 and processes these command inputs.

[0125] The other controller interaction system 656 manages interaction with other controllers in the control system 214. The controller input processing system 668 detects and processes inputs from other controllers in the control system 214 and the controller output generator 670 generates and provides outputs to other controllers in the control system 214. The speech processing system 658 recognizes speech inputs, determines the meaning of these inputs, and provides outputs indicative of the meaning of the speech inputs. For example, the speech processing system 658 can recognize a speech input from the operator 260 as a set change command in which the operator 260 is commanding the control system 214 to change a setting of a controllable subsystem 216. In such an example, the speech processing system 658 recognizes the content of the speech command, identifies the meaning of the command as a set change command, and returns the meaning of the input to the speech management system 662. The speech management system 662, in turn, interacts with the controller output generator 670 to provide command outputs to the appropriate controller in the control system 214 to complete the speech set change command.

[0126] The voice processing system 658 can be invoked in a variety of different ways. For example, in one example, the voice management system 662 provides input from a microphone (as one of the operator interface mechanisms 218) continuously to the voice processing system 658. The microphone detects voice from the operator 260, and the voice management system 662 provides the detected voice to the voice processing system 658. The trigger detector 672 detects a trigger that indicates that the voice processing system 658 is invoked. In some cases, when the voice processing system 658 receives continuous voice input from the voice management system 662, the speech recognition component 674 performs continuous speech recognition on all speech spoken by the operator 260. In some cases, the voice processing system 658 is configured to be invoked using a wake-up word. That is, in some cases, operation of the voice processing system 658 can be initiated based on recognition of a selected voice word, referred to as a wake-up word. In such examples, in the case that the recognition component 674 recognizes the wake-up word, the recognition component 674 provides an indication to the trigger detector 672 that the wake-up word has been recognized. The trigger detector 672 detects that the voice processing system 658 has been invoked or triggered by the wake-up word. In another example, the voice processing system 658 can be invoked by the operator 260 actuating an actuator on a user interface mechanism, such as by touching an actuator on a touch-sensitive display screen, by pressing a button, or by providing another trigger input. In such examples, the trigger detector 672 can detect that the voice processing system 658 has been invoked when the trigger input via the user interface mechanism is detected. The trigger detector 672 can also detect that the voice processing system 658 has been invoked in other ways.

[0127] Once the voice processing system 658 is invoked, voice input from the operator 260 is provided to the speech recognition component 674. The speech recognition component 674 recognizes linguistic elements in the voice input, such as words, phrases, or other linguistic units. The natural language understanding system 678 identifies a meaning of the recognized speech. The meaning can be any of a natural language output, a command output that identifies a command reflected in the recognized speech, a value output that identifies a value in the recognized speech, or a variety of other outputs that reflect an understanding of the recognized speech. For example, more generally, the natural language understanding system 678 and the voice processing system 568 can understand a meaning of speech recognized in the context of the agricultural harvester 100.

[0128] In some examples, the speech processing system 658 can also generate output that directs the operator 260 through a voice-input based user experience. For example, the dialog management system 680 can generate and manage a dialog with the user in order to identify what the user wishes to do. The dialog can disambiguate user commands, identify one or more particular values needed to perform a user command, or obtain other information from the user or provide other information to the user or both. The synthesis component 676 can generate speech synthesis that can be presented to the user through an audio operator interface mechanism such as a speaker. Thus, the dialog managed by the dialog management system 680 can be exclusively a spoken dialog, or a combination of a visual dialog and a spoken dialog.

[0129] The action signal generator 660 generates action signals to control the operator interface mechanisms 218 based on output from one or more of the operator input command processing system 654, the other controller interaction system 656, and the speech processing system 658. The visual control signal generator 684 generates control signals to control visual items in the operator interface mechanisms 218. The visual items can be lights, display screens, warning indicators, or other visual items. The audio control signal generator 686 generates output to control audio elements of the operator interface mechanisms 218. The audio elements include speakers, audible alert mechanisms, horns, or other audible elements. The haptic control signal generator 688 generates control signals that are output to control haptic elements of the operator interface mechanisms 218. The haptic elements include vibratory elements that can be used to vibrate, for example, the operator's seat, steering wheel, pedals, or joystick used by the operator. The haptic elements can include tactile feedback or force feedback elements that provide tactile feedback or force feedback to the operator through the operator interface mechanisms. The haptic elements can also include a wide variety of other haptic elements.

[0130] Figure 10 is a flowchart showing one example of the operation of the operator interface controller 231 in generating an operator interface display on an operator interface mechanism 218 that can include a touch-sensitive display. Figure 10 One example of how the operator interface controller 231 can detect and process operator interaction with a touch-sensitive display is also shown.

[0131] At block 692, the operator interface controller 231 receives a map. Block 694 indicates an example in which the map is a functional prediction map, while block 696 indicates an example in which the map is another type of map. At block 698, the operator interface controller 231 receives input from the geo-location sensor 204 that identifies a geo-location of the agricultural harvester 100. As shown in block 700, the input from the geo-location sensor 204 can include a heading and a position of the agricultural harvester 100. Block 702 indicates an example in which the input from the geo-location sensor 204 includes a speed of the agricultural harvester 100, while block 704 indicates an example in which the input from the geo-location sensor 204 includes other items.

[0132] At block 706, the visual control signal generator 684 in the operator interface controller 231 controls the touch-sensitive display screen in the operator interface mechanism 218 to generate a display showing all or a portion of the field represented by the received map. Block 708 indicates that the displayed field can include a current position marker that shows a current position of the agricultural harvester 100 relative to the field. Block 710 indicates an example in which the displayed field includes a next work unit marker that identifies a next work unit (or area on the field) in which the agricultural harvester 100 will operate. Block 712 indicates an example in which the displayed field includes an upcoming area display portion that displays areas that have not yet been processed by the agricultural harvester 100, while block 714 indicates an example in which the displayed field includes a previously visited display portion that represents areas of the field that have been processed by the agricultural harvester 100. Block 716 indicates an example in which the displayed field displays a plurality of characteristics of the field that have geo-registered positions on the map. For example, if the received map is a residue map, the displayed field can show lengths of chopped stalks that are present in the field that are geo-registered within the displayed field. The mapped characteristics can be shown in the previously visited areas (as shown in block 714), in the upcoming areas (as shown in block 712), and in the next work unit (as shown in block 710). Block 718 indicates examples in which the displayed field includes other items.

[0133] Figure 11 FIG. 7 is a diagram showing one example of a user interface display 720 that can be generated on a touch-sensitive display screen. In other embodiments, the user interface display 720 can be generated on other types of displays. The touch-sensitive display screen can be mounted in an operator's compartment of an agricultural harvester 100 or on a mobile device or elsewhere. Before continuing with the description of the flowchart shown in FIG. 7, the user interface display 720 will be described. Figure 10 At block 706, the visual control signal generator 684 in the operator interface controller 231 controls the touch-sensitive display screen in the operator interface mechanism 218 to generate a display showing all or a portion of the field represented by the received map. Block 708 indicates that the displayed field can include a current position marker that shows a current position of the agricultural harvester 100 relative to the field. Block 710 indicates an example in which the displayed field includes a next work unit marker that identifies a next work unit (or area on the field) in which the agricultural harvester 100 will operate. Block 712 indicates an example in which the displayed field includes an upcoming area display portion that displays areas that have not yet been processed by the agricultural harvester 100, while block 714 indicates an example in which the displayed field includes a previously visited display portion that represents areas of the field that have been processed by the agricultural harvester 100. Block 716 indicates an example in which the displayed field displays a plurality of characteristics of the field that have geo-registered positions on the map. For example, if the received map is a residue map, the displayed field can show lengths of chopped stalks that are present in the field that are geo-registered within the displayed field. The mapped characteristics can be shown in the previously visited areas (as shown in block 714), in the upcoming areas (as shown in block 712), and in the next work unit (as shown in block 710). Block 718 indicates examples in which the displayed field includes other items.

[0133] Figure 11 FIG. 7 is a diagram showing one example of a user interface display 720 that can be generated on a touch-sensitive display screen. In other embodiments, the user interface display 720 can be generated on other types of displays. The touch-sensitive display screen can be mounted in an operator's compartment of an agricultural harvester 100 or on a mobile device or elsewhere. Before continuing with the description of the flowchart shown in FIG. 7, the user interface display 720 will be described. Figure 10 At block 706, the visual control signal generator 684 in the operator interface controller 231 controls the touch-sensitive display screen in the operator interface mechanism 218 to generate a display showing all or a portion of the field represented by the received map. Block 708 indicates that the displayed field can include a current position marker that shows a current position of the agricultural harvester 100 relative to the field. Block 710 indicates an example in which the displayed field includes a next work unit marker that identifies a next work unit (or area on the field) in which the agricultural harvester 100 will operate. Block 712 indicates an example in which the displayed field includes an upcoming area display portion that displays areas that have not yet been processed by the agricultural harvester 100, while block 714 indicates an example in which the displayed field includes a previously visited display portion that represents areas of the field that have been processed by the agricultural harvester 100. Block 716 indicates an example in which the displayed field displays a plurality of characteristics of the field that have geo-registered positions on the map. For example, if the received map is a residue map, the displayed field can show lengths of chopped stalks that are present in the field that are geo-registered within the displayed field. The mapped characteristics can be shown in the previously visited areas (as shown in block 714), in the upcoming areas (as shown in block 712), and in the next work unit (as shown in block 710). Block 718 indicates examples in which the displayed field includes other items.

[0133] Figure 11 FIG. 7 is a diagram showing one example of a user interface display 720 that can be generated on a touch-sensitive display screen. In other embodiments, the user interface display 720 can be generated on other types of displays. The touch-sensitive display screen can be mounted in an operator's compartment of an agricultural harvester 100 or on a mobile device or elsewhere. Before continuing with the description of the flowchart shown in FIG. 7, the user interface display 720 will be described. Figure 10 At block 706, the visual control signal generator 684 in the operator interface controller 231 controls the touch-sensitive display screen in the operator interface mechanism 218 to generate a display showing all or a portion of the field represented by the received map. Block 708 indicates that the displayed field can include a current position marker that shows a current position of the agricultural harvester 100 relative to the field. Block 710 indicates an example in which the displayed field includes a next work unit marker that identifies a next work unit (or area on the field) in which the agricultural harvester 100 will operate. Block 712 indicates an example in which the displayed field includes an upcoming area display portion that displays areas that have not yet been processed by the agricultural harvester 100, while block 714 indicates an example in which the displayed field includes a previously visited display portion that represents areas of the field that have been processed by the agricultural harvester 100. Block 716 indicates an example in which the displayed field displays a plurality of characteristics of the field that have geo-registered positions on the map. For example, if the received map is a residue map, the displayed field can show lengths of chopped stalks that are present in the field that are geo-registered within the displayed field. The mapped characteristics can be shown in the previously visited areas (as shown in block 714), in the upcoming areas (as shown in block 712), and in the next work unit (as shown in block 710). Block 718 indicates examples in which the displayed field includes other items.

[0134] In the example shown in Figure 11 In the example shown in

[0135] In the example shown in Figure 11 In the example shown in user interface display 720 includes a field display portion 728 that displays at least a portion of the field in which the agricultural harvester 100 is operating. The field display portion 728 is shown with a current position marker 708 that corresponds to the current position of the agricultural harvester 100 in the portion of the field shown in the field display portion 728. In one example, the operator can control the touch sensitive display to zoom in on portions of the field display portion 728 or to move or scroll the field display portion 728 to display different portions of the field. A next work unit 730 is shown as the area of the field directly in front of the current position marker 708 of the agricultural harvester 100. The current position marker 708 can also be configured to identify a direction of travel of the agricultural harvester 100, a speed of travel of the agricultural harvester 100, or both. In Figure 11 In the example shown in

[0136] The size of the next work unit 730 labeled on the field display portion 728 can vary based on a number of different criteria. For example, the size of the next work unit 730 can vary based on the speed of travel of the agricultural harvester 100. Thus, when the agricultural harvester 100 is traveling faster, then the area of the next work unit 730 can be larger than the area of the next work unit 730 if the agricultural harvester 100 is traveling slower. In another example, the size of the next work unit 730 can vary based on the size of the agricultural harvester 100, including equipment on the agricultural harvester 100 (e.g., header 102). For example, the width of the next work unit 730 can vary based on the width of the header 102. The field display portion 728 is also shown displaying a previously visited area 714 and an upcoming area 712. The previously visited area 714 represents an area that has already been harvested, while the upcoming area 712 represents an area that still needs to be harvested. The field display portion 728 is also shown displaying different characteristics of the field. In Figure 11In the example shown, the graph being displayed is a predicted stem length graph. Therefore, multiple different stem length markers are displayed on the field display section 728. A set of stem length display markers 732 is shown in the already visited area 714. A set of stem length display markers 732 is also shown in the upcoming area 712, and a set of stem length display markers 732 is shown in the next work unit 730. Figure 11 The stem length display mark 732 is shown to consist of different symbols indicating areas with similar stem lengths. Figure 11 In the example shown, the "!" symbol indicates an area containing stems of long stem length; the "*" symbol indicates an area containing stems of medium stem length; and the "#" symbol indicates an area containing stems of short stem length. Therefore, the field display section 728 displays different measured or predicted values ​​(or characteristics indicated by said values) located in different areas of the field, and uses various display markers 732 to represent those measured or predicted values ​​(or characteristics indicated by said values). As shown, the field display section 728 includes display markers at specific locations associated with specific locations on the field being displayed, in particular... Figure 11 The example shown includes a stem length display mark 732. In some cases, each location of the field may have a display mark associated with that location. Therefore, in some cases, display marks may be provided at each location of the field display portion 728 to identify attributes of characteristics mapped for each particular location of the field. Thus, this disclosure includes providing, for example, a stem length display mark 732 (as shown in the example shown) at one or more locations on the field display portion 728. Figure 11 Display markers, such as those used in this example environment, are used to identify the attributes, degree, etc., of the characteristic being displayed, thereby identifying the characteristic at a corresponding location in the field being displayed. As previously mentioned, display marker 732 can consist of different symbols, and as described below, these 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 marker associated with that location. Therefore, in some cases, display markers can be provided at each location of the field display portion 728 to identify the nature of the characteristic mapped for each particular location in the field. Thus, this disclosure covers providing display markers at one or more locations on the field display portion 728, such as loss level display marker 732 (as in...). Figure 11 (As in the environment 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.

[0137] In other examples, the graph being displayed can be one or more of the graphs described herein, including an information graph, a plurality of information graphs, a functional prediction graph such as a prediction graph or a prediction control zone graph, or a combination thereof. Accordingly, the indicia and characteristics being displayed will be associated with the information, data, characteristics, and values provided by the one or more graphs being displayed.

[0138] In Figure 11 examples, the user interface display 720 also has a control display portion 738. The control display portion 738 allows the operator to view information and interact with the user interface display 720 in various ways.

[0139] The actuators and display indicia in the display portion 738 can be displayed as, for example, separate items, a fixed list, a scrollable list, a drop-down menu, or a drop-down list. In Figure 11 the illustrated example, the display portion 738 shows information for three different stem lengths corresponding to the three symbols mentioned above. The display portion 738 also includes a set of touch-sensitive actuators with which the operator 260 can interact by touching. For example, the operator 260 can touch the touch-sensitive actuators with a finger to activate the respective touch-sensitive actuators.

[0140] As Figure 11As shown, the display portion 738 includes an interactive flag display portion generally indicated at 741. The interactive flag display portion 741 includes a flag bar 739 that displays flags that have been set automatically or manually. A flag actuator 740 allows the operator 260 to mark a location (e.g., the current location of the agricultural harvester, or another location on the field specified by the operator), and add information indicating the stalk length found at the current location. For example, when the operator 260 actuates the flag actuator 740 by touching the flag actuator 740, the touch gesture management system 664 in the operator interface controller 231 identifies the current location as a location where the agricultural harvester 100 produced a long stalk length. When the operator 260 touches the button 742, the touch gesture management system 664 identifies the current location as a location where the agricultural harvester 100 produced a medium stalk length. When the operator 260 touches the button 744, the touch gesture management system 664 identifies the current location as a location where the agricultural harvester 100 produced a short stalk length. When one of the flag actuators 740, 742, or 744 is actuated, the touch gesture management system 664 can control the visual control signal generator 684 to add a symbol corresponding to the identified stalk length on the field display portion 728 at the location identified by the user. In this way, areas of the field where the predicted values do not accurately identify the actual values can be marked for later analysis, and can be used for machine learning. In other examples, the operator can specify an area in front of or around the agricultural harvester 100 by actuating one of the flag actuators 740, 742, or 744, so that control of the agricultural harvester 100 can be based on the values specified by the operator 260.

[0141] The display portion 738 also includes an interactive marker display portion generally indicated at 743. The interactive marker display portion 743 includes a symbol bar 746 that displays a symbol corresponding to each category of value or characteristic (in the case of stalk length, the stalk length) being tracked on the field display portion 728. The display portion 738 also includes an interactive designator display portion generally indicated at 745. The interactive designator display portion 745 includes a designator bar 748 that displays a designator (which can be a text designator or other designator) that identifies the category of value or characteristic (in the case of stalk length, the stalk length). Figure 11 Figure 10 The symbol in the symbol bar 746 and the designator in the designator bar 748 can include any display feature, such as a different color, shape, pattern, intensity, text, icon, or other display feature, and can be customized through interaction by the operator of the agricultural harvester 100, without limitation.

[0142] ​Display section 738 also includes an interactive value display section indicated approximately at 747. Interactive value display section 747 includes a value display bar 750 displaying the selected value. The selected value corresponds to a characteristic or value, or both, being tracked or displayed on field display section 728. The selected value can be selected by the operator of the harvester 100. The selected value in value display bar 750 defines a range of values ​​or, by virtue of, categorizes other values ​​(e.g., predicted values). The selected value in value display bar 750 can be adjusted by the operator of the harvester 100. In one example, operator 260 can select a specific portion of field display section 728, displaying values ​​in bar 750 for that specific portion. Therefore, the value in bar 750 can correspond to values ​​in display sections 712, 714, or 730.

[0143] Display section 738 also includes an interactive threshold display section indicated approximately at 749. Interactive threshold display section 749 includes a threshold display bar 752 that displays action thresholds. The action threshold in bar 752 can be a threshold corresponding to a selected value in value display bar 750. If the predicted or measured value of the characteristic being tracked or displayed, or both, meets the corresponding action threshold in threshold display bar 752, the control system 214 takes the action identified in bar 754. In some cases, the measured or predicted value can satisfy the corresponding action threshold by reaching or exceeding it. In one example, operator 260 can select a threshold, for example, to change the threshold by touching it in threshold display bar 752. Once selected, operator 260 can change the threshold. The threshold in bar 752 can be configured such that a specified action is performed when the measured or predicted value of the characteristic exceeds, is equal to, or is less than the threshold. In some cases, a threshold may represent a range of values, or a range of deviations from the values ​​selected in the value display bar 750, such that predicted or measured characteristic values ​​that reach or fall within that range satisfy the threshold. For example, in Figure 10In the example of FIG. 7, a predicted value that falls within 20 mm of 200 mm will satisfy the corresponding action threshold, and the control system 214 will take an action such as adjusting the shredder setting. In other examples, the threshold in the threshold display column 752 is separated from the selected value in the value display column 750, such that the value in the value display column 750 defines the classification and display of the predicted or measured value, while the action threshold defines when an action is taken based on the measured or predicted value. For example, while a predicted or measured stalk length value of 100 mm will be designated as a "medium stalk length" for classification and display purposes, the action threshold can be 10 mm, such that an action will not be taken until the stalk length value satisfies the threshold. In other examples, the threshold in the threshold display column 752 can include a distance or a time. For example, in a distance example, the threshold can be a threshold distance from an area of the field in which the measured or predicted value is georeferenced, such that the agricultural harvester 100 must be in the area before an action is taken. For example, a threshold distance value of 10 feet means that an action will be taken when the agricultural harvester is located 10 feet or less from an area of the field in which the measured or predicted value is georeferenced. In examples in which the threshold is a time, the threshold can be a threshold time for the agricultural harvester 100 to reach an area of the field in which the measured or predicted value is georeferenced. For example, a threshold of 5 seconds means that an action will be taken when the agricultural harvester 100 is 5 seconds from an area of the field in which the measured or predicted value is georeferenced. In such examples, the current position and travel speed of the agricultural harvester can be considered.

[0144] The display portion 738 also includes an interactive action display portion indicated generally at 751. The interactive action display portion 751 includes an action display column 754 that displays an action identifier that indicates an action to be taken when the predicted or measured value satisfies the action threshold in the threshold display column 752. The operator 260 can touch the action identifier in the column 754 to change the action to be taken. An action can be taken when the threshold is satisfied. For example, at the bottom of the column 754, if the measured value in the column 750 satisfies the threshold in the column 752, the increase grain cleaning fan speed action and the decrease grain cleaning fan speed action are identified as the actions to be taken. Thus, in some examples, multiple actions can be taken when the threshold is satisfied. For example, in response to the threshold being satisfied, the grain cleaning fan speed can be adjusted, the threshing cylinder speed can be adjusted, and the concave gap can be adjusted.

[0145] The action that can be set in the bar 754 can be any of a variety of different types of actions. For example, the action can include a stop or inhibit action that, when executed, prohibits the agricultural harvester 100 from further harvesting within the area. The action can include a speed change action that, when executed, changes the speed of travel of the agricultural harvester 100 through the field. The action can include a setting change action to change a setting of an internal actuator or another WMA or group of WMAs, or to implement a setting change to change the speed of a threshing cylinder, the speed of a grain cleanout fan, the position of the header (e.g., tilt, height, roll, etc.), and a variety of different other settings. These are merely examples, and a wide variety of other actions are contemplated herein.

[0146] The items shown on the user interface display 720 can be visually controlled. The visual control of the interface display 720 can be performed to capture the attention of the operator 260. For example, the display indicia can be controlled to modify the intensity, color, or pattern of the displayed display indicia. Additionally, the display indicia can be controlled to flash. As an example, the described changes to the visual appearance of the display indicia are provided. Thus, other aspects of the visual appearance of the display indicia can be changed. Thus, the display indicia can be modified in a desired manner in various situations in order to, for example, capture the attention of the operator 260. Furthermore, while a particular number of items are displayed on the user interface display 720, this need not be the case. In other examples, more or fewer items, including more or fewer particular items, can be included on the user interface display 720.

[0147] Now returning to Figure 12The flowchart of Figure 7 continues the description of the operation of the operator interface controller 231. At block 760, the operator interface controller 231 detects an input that sets a flag and controls the touch-sensitive user interface display 720 to display the flag on the field display portion 728. The detected input can be an operator input (as shown at 762) or an input from another controller (as shown at 764). At block 766, the operator interface controller 231 detects a field sensor input from one of the field sensors 208 that indicates a measured property of the field. At block 768, the visual control signal generator 684 generates a control signal to control the user interface display 720 to display actuators for modifying the user interface display 720 and for modifying machine control. For example, block 770 indicates that one or more of the actuators for setting or modifying the values in the fields 739, 746, and 748 can be displayed. Thus, the user can set the flags and modify the properties of the flags. For example, the user can modify the stalk length and the stalk length designator corresponding to the flag. Block 772 indicates that the action threshold in field 752 is displayed. Block 776 indicates that the action in field 754 is displayed, and block 778 indicates that the measured field data in field 750 is displayed. Block 780 indicates that a variety of other information and actuators can also be displayed on the user interface display 720.

[0148] At block 782, the operator input command processing system 654 detects and processes operator inputs corresponding to interactions with the user interface display 720 performed by the operator 260. In the case where the user interface mechanism on which the user interface display 720 is displayed is a touch-sensitive display screen, the interaction inputs made by the operator 260 with the touch-sensitive display screen can be touch gestures 784. In some cases, the operator interaction inputs can be inputs made using a click device 786 or other operator interaction input device 788.

[0149] At block 790, the operator interface controller 231 receives a signal indicating an alarm condition. For example, block 792 indicates that a signal can be received by the controller input processing system 668 indicating that the detected value in column 750 satisfies the threshold condition present in column 752. As explained previously, the threshold condition can include the value being below the threshold, the value being at the threshold, or the value being above the threshold. Block 794 shows that the action signal generator 660 can respond to receiving the alarm condition by generating a visual alarm using the visual control signal generator 684, generating an audio alarm using the audio control signal generator 686, generating a haptic alarm using the haptic control signal generator 688, or by using any combination of these, to alert the operator 260. Similarly, as shown in block 796, the controller output generator 670 can generate outputs to other controllers in the control system 214 so that these controllers perform the corresponding actions identified in column 754. Block 798 shows that the operator interface controller 231 can also detect and process alarm conditions in other ways.

[0150] Block 900 shows that the speech management system 662 can detect and process inputs that invoke the speech processing system 658. Block 902 shows that performing speech processing can include using the dialog management system 680 to have a conversation with the operator 260. Block 904 shows that the speech processing can include providing signals to the controller output generator 670 to automatically perform control operations based on the speech input.

[0151] Table 1 below shows an example of a conversation between the operator interface controller 231 and the operator 260. In Table 1, the operator 260 invokes the speech processing system 658 using a trigger word or wake-up word that is detected by the trigger detector 672. In the example shown in Table 1, the wake-up word is "Johnny."

[0152] Table 1

[0153] Operator: "Johnny, tell me about the current weed seeds in the residue."

[0154] Operator interface controller: "Alligator grass is 55%. Crabgrass is 25%. Horseweed is 20%."

[0155] Table 2 shows an example in which the speech synthesis component 676 provides outputs to the audio control signal generator 686 to provide audio updates intermittently or periodically. The interval between updates can be time-based (such as every five minutes), or coverage or distance-based (such as every five acres), or exception-based (such as when a measured value is greater than a threshold).

[0156] Table 2

[0157] Operator interface controller: "Stalk length has an average of 150 mm over the last 10 minutes."

[0158] The example shown in Table 3 shows that some actuators or user input mechanisms on the touch sensitive display 720 can be supplemented with voice dialog. The example in Table 3 shows that the action signal generator 660 can generate action signals to automatically control residue distribution in the field being harvested.

[0159] Table 3

[0160] Human: "Johnny, spread the residue to the right."

[0161] Operator interface controller: "Residue is being spread 10 feet to the right."

[0162] The example shown in Table 4 shows that the action signal generator 160 can generate signals to control the residue subsystem in ways other than shown in Table 3.

[0163] Table 4

[0164] Human: "Johnny, chop the stalks a little longer for the next acre."

[0165] Operator interface controller: "The stalks will be chopped a little longer for the next acre."

[0166] Returning again to Figure 2 , block 906 shows that the operator interface controller 231 can also detect and handle 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 that an alert or output message should be presented to the operator 260. Block 908 shows that the output can be an audio message. Block 910 shows that the output can be a visual message, and block 912 shows that the output can be a haptic message. Until the operator interface controller 231 determines that the current harvesting operation is complete, as shown in block 914, the process returns to block 698, where the geographic location of the harvester 100 is updated, and the process continues as described above to update the user interface display 720.

[0167] Once the operation is complete, any desired values that were displayed or have been displayed on the user interface display 720 can be saved. These values can also be used in machine learning to improve different parts of the prediction model generator 210, the prediction map generator 212, the control zone generator 213, the control algorithm, or other items. The saved desired values are indicated by block 916. These values can be saved locally on the agricultural harvester 100, or the values can be saved at a remote server location or sent to another remote system.

[0168] It can be seen that the information map is obtained by an agricultural harvester, which shows vegetation index values, moisture values, and terrain values at different geographic locations of a field being harvested. On-board sensors on the harvester sense residue characteristics as the agricultural harvester moves through the field. A prediction map generator generates a prediction map that predicts control values for different locations in the field based on the values in the information map and the residue characteristics sensed by the on-board sensors, which in some examples can be values of the residue characteristics. A control system controls controllable subsystems based on the control values in the prediction map.

[0169] The control values are values on which actions can be based. As described herein, the control values can include any value (or a property indicated by or derived from the value) that can be used to control the agricultural harvester 100. The control values can be any value that is indicative of an agricultural property. The control values can be predicted values, measured values, or detected values. The control values can include any value provided by a map (such as any of the maps described herein), for example, the control values can be values provided by an information map, values provided by a priori information map, or values provided by a prediction map (e.g., a functional prediction map). The control values can also include any of the properties indicated by or derived from values detected by any of the sensors described herein. In other examples, the control values can be provided by an operator of the agricultural machine, such as a command input by an operator of the agricultural machine.

[0170] The present discussion has mentioned processors and servers. In some examples, the processors and servers include computer processors with associated memory and timing circuitry, not separately shown. The processors and servers are functional parts of the systems or devices to which they belong, and are activated by, and facilitate the functionality of, other components or items in those systems.

[0171] Moreover, a number of user interface displays have been discussed. The display can take a variety of different forms and can have a variety of different user-actuatable operator interface mechanisms disposed thereon. For example, the user-actuatable operator interface mechanisms can be text boxes, check boxes, icons, links, drop-down menus, search boxes, etc. The user-actuatable operator interface mechanisms can also be actuated in a variety of different ways. For example, the user-actuatable operator interface mechanisms can be actuated using an operator interface mechanism such as a pointing device (such as a trackball or mouse, a hardware button, a switch, a joystick or keyboard, a thumb switch or thumb pad, etc.), a virtual keyboard or other virtual actuator. Furthermore, where the screen on which the user-actuatable operator interface mechanisms are displayed is a touch-sensitive screen, the user-actuatable operator interface mechanisms can be actuated using touch gestures. Also, the user-actuatable operator interface mechanisms can be actuated using voice commands using voice recognition functionality. Voice recognition can be implemented using a voice detection device such as a microphone and software for recognizing detected voice and executing commands based on received voice.

[0172] A number of data storage devices have also been discussed. It should be noted that each data storage device can be divided into a plurality of data storage devices. In some examples, one or more of the data storage devices can be local to the system that accesses the data storage device, all of the data storage devices can be located remotely from the system that utilizes the data storage devices, or one or more data storage devices can be local while other data storage devices are remote. All of these configurations are contemplated by the present disclosure.

[0173] Furthermore, the drawings illustrate a number of blocks that are associated with the functionality that is attributed to them. It should be noted that the functionality attributed to a block can be performed by less than or more than one component. Moreover, the functionality attributed to a block can be performed by the same or different component at different times. In different examples, some functionality can be added and some functionality can be removed.

[0174] It should be noted that the above discussion has described a variety of different systems, components, logic, and interactions. It should be understood that any or all of such systems, components, logic, and interactions can be implemented by hardware items, such as processors, memories, or other processing components, including but not limited to artificial intelligence components such as neural networks, some of which are described below, that perform the functions associated with those systems, components, logic, or interactions. Moreover, any or all of the systems, components, logic, and interactions can be implemented by software that is loaded into memory and subsequently executed by a processor or server or other computing component, as described below. Any or all of the systems, components, logic, and interactions can also be implemented by different combinations of hardware, software, firmware, etc., some examples of which are described below. These are some examples of different structures that can be used to implement any or all of the systems, components, logic, and interactions described above. Other structures can also be used.

[0175] Figure 2 is a block diagram of an agricultural harvester 600, which can be similar to the agricultural harvester 100 shown in Figure 12 The agricultural harvester 600 communicates with elements in the remote server architecture 500. In some examples, the remote server architecture 500 can provide computing, software, data access, and storage services that do not require end users to know the physical location or configuration of the system that delivers the services. In various examples, the remote server can deliver the services over a wide area network, such as the Internet, using appropriate protocols. For example, the remote server can deliver an application over a wide area network, and the application can be accessed through a web browser or any other computing component. Figure 2 The software or components shown in and the data associated therewith can be stored on servers at a remote location. Computing resources in the remote server environment can be consolidated at a remote data center location, or the computing resources can be dispersed to multiple remote data centers. The remote server infrastructure can deliver services through a shared data center, even though the service appears as a single point of access for a user. Thus, the components and functionality described herein can be provided from a remote server at a remote location using a remote server architecture. Alternatively, the components and functionality can be provided from a server, or the components and functionality can be installed directly or otherwise on a client device.

[0176] In the example shown in Figure 12 some items are similar to those shown in Figure 12 and these items are similarly numbered. Figure 12 It is specifically shown that the prediction model generator 210 or the prediction map generator 212, or both, can be located at a server location 502 that is remote from the agricultural harvester 600. Thus, inFigure 12 In the example shown, the agricultural harvester 600 accesses the system via a remote server location 502.

[0177] Figure 2 Another example of a remote server architecture is also described. Figure 2 It shows Figure 13 Some components can be located at a remote server location 502, while others can be located elsewhere. As an example, data storage device 202 can be located at a separate location from location 502 and accessed via a remote server at location 502. Regardless of their location, these components can be directly accessed by the agricultural harvester 600 via a network (such as a wide area network or local area network); these components can be hosted as a service at a remote site; or they can be 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 an operator, user, or system. For example, a physical carrier wave can be used instead of an electromagnetic carrier wave, or a physical carrier wave can be used in addition to an electromagnetic carrier wave. In some examples, where wireless telecommunications service coverage is poor or nonexistent, 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 a 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. The collected information can then be forwarded to another network when the machine containing the received information reaches a location with wireless telecommunications service coverage or other available wireless coverage. For example, when the 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 combine harvester 600 until it enters an area with wireless communication coverage. The combine harvester 600 itself can transmit the information to another network.

[0178] It will also be noted that Figures 14-15 The components or parts thereof can be arranged 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.

[0179] In some examples, the remote server architecture 500 can include network security measures. Without limitation, these measures include encryption of data on storage devices, encryption of data sent between network nodes, authentication of personnel or processes accessing data, and the use of ledgers to record metadata, data, data transfers, data access, and data transformations. In some examples, the ledgers can be distributed and immutable (e.g., implemented as blockchains).

[0180] Figure 13 is a simplified block diagram of one illustrative example of a handheld computing device or mobile computing device that can be used as a user's or customer's handheld device 16, in which the present system (or a portion thereof) can be deployed. For example, a mobile device can be deployed in an operator's cab of an agricultural harvester 100 for use in generating, processing, or displaying the graphs discussed above. Figure 2 is an example of a handheld device or mobile device.

[0181] Figure 2 A general block diagram of components of a client device 16 is provided, which can run some of the components shown in Figure 14 may interact with some of the components shown in Figure 14 , or both. In the device 16, a communication link 13 is provided that allows the handheld device to communicate with other computing devices, and in some examples a channel is provided for automatically receiving information (e.g., by scanning). Examples of the communication link 13 include allowing communication over one or more communication protocols, such as wireless services for providing cellular access to a network, and protocols providing local wireless connectivity to a network.

[0182] In other examples, an application can be received on a removable Secure Digital (SD) card connected to an interface 15. The interface 15 and the communication link 13 are in communication with the processor 17 (which can also be embodied by a processor or server from other figures) along a bus 19 that is also connected to memory 21 and input / output (I / O) components 23, as well as a clock 25 and a positioning system 27.

[0183] In one example, the I / O components 23 are provided to facilitate input and output operations. The I / O components 23 of various examples of the device 16 can include input components, such as buttons, touch sensors, optical sensors, microphones, touch screens, proximity sensors, accelerometers, orientation sensors, and output components, such as display devices, speakers, and / or printer ports. Other I / O components 23 can also be used.

[0184] Clock 25 illustratively includes a real-time clock component that outputs the time of day and date. Clock 25 can also illustratively provide timing functions for processor 17.

[0185] Positioning system 27 illustratively includes a component that outputs the current geographic position of device 16. Positioning system 27 can include, for example, a global positioning system (GPS) receiver, a LORAN system, a dead reckoning system, a cellular triangulation system, or other positioning system. Positioning system 27 can also include, for example, mapping software or navigation software that generates desired maps, navigation routes, and other geographic functions.

[0186] Memory 21 stores operating system 29, network settings 31, applications 33, application configuration settings 35, data store 37, communication drivers 39, and communication configuration settings 41. Memory 21 can include all types of tangible volatile and non-volatile computer-readable memory devices. Memory 21 can also include computer storage media (described below). Memory 21 stores computer readable instructions that, when executed by processor 17, cause processor to perform computer-implemented steps or functions according to the instructions. Processor 17 can also be activated by other components to facilitate the functions of those components.

[0187] Figure 15 One example is shown in which device 16 is a tablet computer 600. In Figure 14 In this example, computer 601 is shown with a user interface display screen 602. Screen 602 can be a touch screen that receives input from a pen or stylus or a pen-enabled interface. Tablet computer 600 can also use an on-screen virtual keyboard. Of course, computer 601 can also be attached to a keyboard or other user input device, such as by a suitable attachment mechanism, such as a wireless link or a USB port. Computer 601 can also illustratively receive sound input.

[0188] Figure 16 Similarly Figure 2 , except that the device is a smart phone 71. Smart phone 71 has a touch sensitive display 73 that displays icons or tiles or other user input mechanisms 75. Mechanisms 75 can be used by a user to run applications, make calls, perform data transfer operations, etc. In general, smart phone 71 is built on a mobile operating system and provides more advanced computing capability and connectivity than a feature phone.

[0189] Note that other forms of device 16 are possible.

[0190] Figure 16 is one example of a computing environment in which Figure 2 elements of the system can be deployed. Reference is made to 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. The components of computer 810 can include, but are not limited to, a processing unit 820 (which can include a processor or server from the previous figures), a system memory 830, and a system bus 821 that couples various system components including the system memory to the processing unit 820. The system bus 821 can be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. Regarding Figure 16 The described memory and programs can be deployed in Figure 16 corresponding portions of

[0191] Computer 810 typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by computer 810 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media can comprise computer storage media and communication media. Computer storage media is different from, and does not include, a modulated data signal or carrier wave. It includes hardware storage media including both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer 810. Communication media can embody computer readable instructions, data structures, program modules or other data in a modulated data signal or carrier wave. The term "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.

[0192] System memory 830 includes computer storage media in the form of volatile and / or nonvolatile memory such as read only memory (ROM) 831 and random access memory (RAM) 832. A basic input / output system 833 (BIOS), containing the basic routines that help to transfer information between elements within computer 810, such as during start-up, is typically stored in ROM 831. RAM 832 typically contains data and / or program modules that are immediately accessible to and / or presently being operated on by processing unit 820. By way of example, and not limitation, as Figure 16Operating system 834, application programs 835, other program modules 836, and program data 837 are shown.

[0193] The computer 810 can also include other removable / non-removable volatile / non-volatile computer storage media. By way of example, and not limitation, such computer storage media can include Figure 16 Hard disk drive 841, which reads from or writes to non-removable, nonvolatile magnetic media, is shown. Other removable / non-removable volatile / non-volatile computer storage media that can be used with the computer 810 include, but are not limited to, magnetic tape cassettes, flash memory cards, DVDs, digital video tape, solid state RAM, solid state ROM, and the like. Hard disk drive 841 is typically connected to the system bus 821 through a non-removable memory interface such as interface 840, and magnetic disk drive 855 is typically connected to the system bus 821 by a removable memory interface, such as interface 850.

[0194] Alternatively, or in addition, the functionality described herein can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Program-specific Integrated Circuits (ASICs), Program-specific Standard Products (e.g., ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0195] The drives and their associated computer storage media discussed above and illustrated in Figure 16 provide storage of computer-readable instructions, data structures, program modules and other data for the computer 810. In this regard, the hard disk drive 841 is illustrated as storing operating system 844, application programs 845, other program modules 846, and program data 847. Note that these components can either be the same as or different from operating system 834, application programs 835, other program modules 836, and program data 837. Operating system 844, application programs 845, other program modules 846, and program data 847 are given different numbers here to ​ A user can enter commands and information into the computer 810 through input devices such as a keyboard 862, a microphone 863, and a pointing device 861, such as a mouse, trackball or touch pad. Other input devices (not shown) can include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit 820 through a user input interface 860 that is coupled to the system bus 821, but can be connected by other interface and bus structures, as will be

[0196]

[0197] ​The computer 810 is operated in a networking environment using logical connections to one or more remote computers, such as a remote computer 880. The remote computer 880 can be a personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer 810, although only a memory storage device 881 has been illustrated. The logical connections depicted include a

[0198] When used in a LAN networking environment, the computer 810 is connected to the LAN 871 through a network interface or adapter 870. When used in a WAN networking environment, the computer 810 typically includes a modem 872 or other means for establishing communications over the WAN 873, such as the Internet. In a networking environment, program modules can be stored in the remote memory storage device 881. The aforementioned device and ​ It is shown that a remote application 885 can reside on the remote computer 880.

[0199] It should also be noted that the different examples described herein 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 contemplated herein.

[0200] Example 1 is an agricultural work machine comprising:

[0201] a communication system that receives an information map, the information map comprising values of an agricultural property corresponding to different geographical locations in a field;

[0202] a geographical location sensor that detects a geographical location of the agricultural work machine;

[0203] a field sensor that detects values of a residue property corresponding to the geographical location;

[0204] a prediction map generator that generates a functional predicted agricultural map of the field based on the values of the agricultural property in the information map and based on the values of the residue property, the functional predicted agricultural map mapping predicted values of the residue property to the different geographical locations in the field;

[0205] a controllable subsystem; and

[0206] a control system that generates control signals to control the controllable subsystem based on the geographical location of the agricultural work machine and based on the predicted values of the residue property in the functional predicted agricultural map.

[0207] Example 2 is the agricultural work machine of any or all preceding examples, wherein the prediction map generator comprises:

[0208] a predicted residue subsystem characteristic map generator that generates a functional predicted residue subsystem characteristic map that maps predicted results of residue subsystem characteristics to the different geographic locations of the field.

[0209] Example 3 is the agricultural work machine of any or all of the preceding examples, wherein the control system comprises:

[0210] a residue subsystem controller that generates a residue subsystem control signal based on the geographic location and the functional predicted residue subsystem characteristic map, and controls a residue subsystem that is the controllable subsystem based on the residue subsystem control signal.

[0211] Example 4 is the agricultural work machine of any or all of the preceding examples, wherein the residue subsystem controller controls a residue spreader of the residue subsystem based on the residue subsystem control signal.

[0212] Example 5 is the agricultural work machine of any or all of the preceding examples, wherein the information map comprises a vegetation index map that maps vegetation index values that are the agricultural characteristics to the different geographic locations in the field.

[0213] Example 6 is the agricultural work machine of any or all of the preceding examples, wherein the information map comprises a moisture map that maps moisture values that are the agricultural characteristics to the different geographic locations in the field.

[0214] Example 7 is the agricultural work machine of any or all of the preceding examples, wherein the information map comprises a terrain map that maps terrain characteristic values that are the agricultural characteristics to the different geographic locations in the field.

[0215] Example 8 is the agricultural work machine of any or all of the preceding examples, wherein the residue characteristics comprise residue spread in one or more dimensions.

[0216] Example 9 is the agricultural work machine of any or all of the preceding examples, wherein the residue characteristics comprise residue uniformity.

[0217] Example 10 is the agricultural work machine of any or all of the preceding examples, wherein the control system further comprises:

[0218] an operator interface controller that generates a user interface graphical representation of the functional predicted agricultural map, the user interface graphical representation including a field portion having one or more markers indicating the predicted values of the residue characteristic at one or more geographic locations on the field portion.

[0219] Example 11 is a computer-implemented method of controlling an agricultural work machine, comprising:

[0220] obtaining an information map including values of an agricultural characteristic corresponding to different geographic locations in a field;

[0221] detecting a geographic location of the agricultural work machine;

[0222] detecting, with an on-site sensor, a value of a residue characteristic corresponding to a geographic location;

[0223] generating a functional predicted agricultural map of the field based on the values of the agricultural characteristic in the information map and based on the value of the residue characteristic corresponding to the geographic location, the functional predicted agricultural map mapping predicted control values to the different geographic locations in the field; and

[0224] controlling a controllable subsystem based on the geographic location of the agricultural work machine and based on the control values in the functional predicted agricultural map.

[0225] Example 12 is the computer-implemented method of any or all preceding examples, wherein generating a functional predicted agricultural map comprises:

[0226] generating a functional predicted residue characteristic map that maps predicted residue characteristics as the control values to the different geographic locations of the field.

[0227] Example 13 is the computer-implemented method of any or all preceding examples, wherein the functional predicted residue characteristic map maps predicted residue spread values as the predicted residue characteristics to the different geographic locations of the field.

[0228] Example 14 is the computer-implemented method of any or all preceding examples, wherein controlling a controllable subsystem comprises:

[0229] generating a residue control signal based on the detected geographic location and the functional predicted residue characteristic map; and

[0230] controlling the controllable subsystem based on the residue control signal to control a residue spreader of the agricultural work machine.

[0231] Example 15 is the computer-implemented method of any or all preceding examples, wherein the functional predicted residue characteristic map maps predicted residue content values as a prediction of a residue characteristic to the different geographic locations on the field.

[0232] Example 16 is the computer-implemented method of any or all preceding examples, wherein controlling a controllable subsystem comprises:

[0233] generating a residue control signal based on the detected geographic location and the functional predicted residue characteristic map; and

[0234] controlling the controllable subsystem based on the residue control signal to control a residue chopper of the agricultural work machine.

[0235] Example 17 is the computer-implemented method of any or all preceding examples, wherein the functional predicted residue characteristic map maps predicted residue uniformity values as the predicted residue characteristic to the different geographic locations of the field.

[0236] Example 18 is the computer-implemented method of any or all preceding examples, wherein controlling a controllable subsystem comprises:

[0237] generating a residue control signal based on the detected geographic location and the functional predicted residue characteristic map; and

[0238] controlling the controllable subsystem based on the residue control signal.

[0239] Example 19 is an agricultural work machine comprising:

[0240] a communication system that receives an information map comprising values of an agricultural characteristic corresponding to different geographic locations in a field;

[0241] a geographic location sensor that detects a geographic location of the agricultural work machine;

[0242] a field sensor that detects values of a residue characteristic corresponding to geographic locations;

[0243] a prediction model generator that generates a predicted agricultural model based on values of the agricultural characteristic in the information map at the geographic locations and values of the residue characteristic at the geographic locations detected by the field sensor, the predicted agricultural model modeling a relationship between the agricultural characteristic and the residue characteristic;

[0244] a prediction map generator that generates a functional predicted agriculture map of the field that maps predicted control values to the different geographic locations in the field based on values of the agricultural characteristics in the information map and based on the predictive agriculture model;

[0245] a controllable subsystem; and

[0246] a control system that generates control signals to control the controllable subsystem based on the geographic locations of the agricultural work machine and on the control values in the functional predicted agriculture map.

[0247] Example 20 is the agricultural work machine of any or all preceding examples, wherein the control system includes:

[0248] a residue controller that generates residue control signals based on the detected geographic locations and the functional predicted agriculture map and controls one or more of a residue shredder, a residue spreader, and a seed expeller as the controllable subsystem based on the residue control signals.

[0249] Although the subject matter has been described in language specific to structural features or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. An agricultural work machine (100), comprising: a communication system (206) that receives an information map (258) that includes values of an agricultural property corresponding to different geographic locations in a field; a geographic location sensor (204) that detects a geographic location of the agricultural work machine (100); a field sensor (208) that detects values of a residue property corresponding to the geographic location; a predicted map generator (212) that generates a functional predicted agricultural map of the field based on the values of the agricultural property in the information map (258) and based on the values of the residue property, the functional predicted agricultural map mapping predicted values of the residue property to the different geographic locations in the field; a controllable subsystem (216); and a control system (214) that generates a control signal to control the controllable subsystem (216) based on the geographic location of the agricultural work machine (100) and based on the predicted values of the residue property in the functional predicted agricultural map. the predicted map generator includes:

2. The agricultural work machine of claim 1, wherein, a predicted residue subsystem property map generator that generates a functional predicted residue subsystem property map that maps predicted results of a residue subsystem property to the different geographic locations of the field. the control system includes:

3. The agricultural work machine of claim 2, wherein, a residue subsystem controller that generates a residue subsystem control signal based on the geographic location and the functional predicted residue subsystem property map and controls a residue subsystem as the controllable subsystem based on the residue subsystem control signal. the residue subsystem controller controls a residue spreader of the residue subsystem based on the residue subsystem control signal.

4. The agricultural work machine of claim 3, wherein, the information map includes a vegetation index map that maps vegetation index values as the agricultural property to the different geographic locations in the field.

5. The agricultural work machine of claim 1, wherein, the information map includes a moisture map that maps moisture values as the agricultural property to the different geographic locations in the field.

6. The agricultural work machine of claim 1, wherein, the information map includes a terrain map that maps terrain property values as the agricultural property to the different geographic locations in the field.

7. The agricultural work machine of claim 1, wherein, the residue property includes residue spread in one or more dimensions.

8. The agricultural work machine of claim 1, wherein, 9. A computer-implemented method of controlling an agricultural work machine (100), comprising: obtaining an information map (258) that includes values of an agricultural property corresponding to different geographic locations in a field; detecting a geographic location of the agricultural work machine (100); detecting values of a residue property corresponding to a geographic location with a field sensor (208); ​ generating a functional predictive agronomic map of the field based on values of the agricultural properties in the information map (258) and on values of the residue properties corresponding to the geographic locations, the functional predictive agronomic map mapping predicted control values to the different geographic locations in the field; and controlling a controllable subsystem (216) based on the geographic location of the agricultural work machine (100) and on the control values in the functional predictive agronomic map.

10. An agricultural work machine (100) comprising: a communication system (206) that receives an information map (258) comprising values of agricultural properties corresponding to different geographic locations in a field; a geographic location sensor (204) that detects a geographic location of the agricultural work machine; a field sensor (208) that detects values of residue properties corresponding to geographic locations; a predictive model generator (210) that generates a predictive agronomic model based on values of the agricultural properties at the geographic locations in the information map and values of the residue properties at the geographic locations detected by the field sensor, the predictive agronomic model modeling a relationship between the agricultural properties and the residue properties; a predictive map generator (212) that generates a functional predictive agronomic map of the field based on values of the agricultural properties in the information map and on the predictive agronomic model, the functional predictive agronomic map mapping predicted control values to the different geographic locations in the field; a controllable subsystem (216); and a control system (214) that generates control signals to control the controllable subsystem (216) based on the geographic location of the agricultural work machine (100) and on the control values in the functional predictive agronomic map.

10. An agricultural work machine (100) comprising: a communication system (206) that receives an information map (258) comprising values of agricultural properties corresponding to different geographic locations in a field; a geographic location sensor (204) that detects a geographic location of the agricultural work machine; a field sensor (208) that detects values of residue properties corresponding to geographic locations; a predictive model generator (210) that generates a predictive agronomic model based on values of the agricultural properties at the geographic locations in the information map and values of the residue properties at the geographic locations detected by the field sensor, the predictive agronomic model modeling a relationship between the agricultural properties and the residue properties; a predictive map generator (212) that generates a functional predictive agronomic map of the field based on values of the agricultural properties in the information map and on the predictive agronomic model, the functional predictive agronomic map mapping predicted control values to the different geographic locations in the field; a controllable subsystem (216); and a control system (214) that generates control signals to control the controllable subsystem (216) based on the geographic location of the agricultural work machine (100) and on the control values in the functional predictive agronomic map.

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