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 feature values at different geographic locations of a field. As the agricultural work machine moves through the field, on-board sensors sense agricultural features. A prediction map generator generates a prediction map of predicted agricultural features at different locations in the field based on relationships between values in the one or more information maps and the agricultural features sensed by the on-board sensors. The prediction map can be output and used for automated machine control.

Description

Technical Field

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

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

[0003] Various conditions in the field can have a variety of adverse effects on harvesting operations. Therefore, when encountering such conditions during harvesting, the operator may try modifying the harvester controls.

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

[0005] One or more infographics are obtained through agricultural machinery. These infographics map one or more agricultural feature values ​​to different geographic locations in the field. As the agricultural machinery moves across the field, field sensors on the machinery detect the agricultural features. A prediction map generator generates prediction maps of the predicted agricultural features at different locations in the field based on the relationships between the values ​​in the one or more infographics and the agricultural features sensed by the field sensors. The prediction maps can be output and used for automated machine control.

[0006] The present invention is provided to introduce the chosen concepts in a simplified form, which are further described in the detailed description below. The present invention is not intended to identify key or essential features of the claimed subject matter, nor is it intended to help determine the scope of the claimed subject matter. The claimed subject matter is not limited to examples addressing any or all of the shortcomings pointed out in the background art. Attached Figure Description

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

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

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

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

[0011] Figure 5 This is a flowchart illustrating an example of an agricultural harvester receiving a map, detecting field features, and generating a functional prediction map to present or control the operation of the agricultural harvester or both during harvesting operations.

[0012] Figure 6A This is a block diagram illustrating an example of a prediction model generator and a prediction graph generator.

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

[0014] Figure 7 The flowchart illustrates an example of the operation of an agricultural harvester, including generating a predictive map using prior information maps and field sensor inputs.

[0015] Figure 8 This is a block diagram illustrating an example of a control area generator.

[0016] Figure 9 It is a diagram. Figure 8 The flowchart shows an example of the operation of the control area generator.

[0017] Figure 10 The diagram illustrates a flowchart showing an example of the operation of a control system when selecting target settings to control an agricultural harvester.

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

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

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

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

[0022] Figures 15 to 17 An example of a mobile device that can be used in agricultural harvesters is shown.

[0023] Figure 18 This is a block diagram illustrating an example of a computing environment that can be used for agricultural harvesters. Detailed Implementation

[0024] To facilitate an understanding of the principles of this disclosure, reference will now be made to the examples illustrated in the accompanying drawings, and these examples will be described using specific language. However, it should be understood that this disclosure is not intended to limit its scope. Any changes and additional modifications to the described apparatus, systems, and methods, as well as any further application of the principles of this disclosure, are entirely contemplated, as would normally occur to those skilled in the art to which this disclosure pertains. In particular, it is entirely conceivable that features, components, steps, and / or combinations thereof described for one example may be combined with features, components, steps, and / or combinations thereof described for other examples of this disclosure.

[0025] This specification relates to generating functional prediction maps by combining prior data (previous data) with field data acquired concurrently with agricultural operations, and more specifically, generating functional prediction crop moisture maps. In some examples, functional prediction crop moisture maps can be used to control agricultural machinery, such as combine harvesters. Unless the machine settings also change, the performance of a combine harvester may degrade when it engages areas of varying crop moisture. For example, in areas of reduced crop moisture, a combine harvester may move rapidly across the ground and move material through the machine at an increased feed rate. When encountering areas of increased crop moisture, the combine harvester's speed on the ground may decrease, thereby reducing the combine harvester's feed rate, or the combine harvester may experience blockages, spillage, or other problems. For example, areas of increased crop moisture may have crop plants with different physical structures compared to areas of reduced crop moisture. For instance, in areas of increased crop moisture, some plants may have thicker stems, wider leaves, larger or more heads, etc. In other examples, increased crop moisture content leads to increased crop plant mass, and field areas with increased crop moisture may have crop plants with larger biomass values. These changes in plant structure in areas of varying crop moisture can also alter the performance of agricultural harvesters as they pass through such areas.

[0026] A vegetation index map schematically maps vegetation index values ​​(which can indicate vegetation growth) at different geographic locations within a field of interest. An example of a vegetation index includes the normalized difference vegetation index (NDVI). Many other vegetation indices exist, all of which are within the scope of this disclosure. In some examples, vegetation indices can be derived (obtained) from sensor readings of one or more bands of electromagnetic radiation reflected by the plants. Without limitation, these bands can be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum.

[0027] Vegetation index maps can therefore be used to identify the presence and location of vegetation. In some examples, vegetation index maps enable the identification and georeferencing of crops in the presence of bare soil, crop residues, or other vegetation (including crops or other weeds). For example, at the beginning of the growing season, when crops are in their growth stage, vegetation indices can show the progress of crop development. Therefore, if a vegetation index map is generated early in the growing season or midway through the growing season, it can indicate the progress of crop plant development. For example, a vegetation index map can indicate whether plants are underdeveloped, whether sufficient canopy has been established, or other plant characteristics indicating plant development.

[0028] Topographical features are graphically plotted to show the ground elevation across different geographic locations within a field of interest. Since ground slope indicates changes in elevation, two or more elevation values ​​allow for the calculation of slope across areas with known elevation values. Greater granularity of slope can be achieved by using more areas with known elevation values. As a combine harvester travels across the terrain in a known direction, the harvester's pitch and roll can be determined based on the ground slope (i.e., areas of elevation change). Topographical features mentioned below may include (but are not limited to) elevation, slope (e.g., including the machine's orientation relative to the slope), and ground profile (e.g., roughness).

[0029] Soil property maps exemplarily plot soil property values ​​(which can represent soil type, soil moisture, soil cover, soil structure, and various other soil characteristics) at different geographic locations within a field of interest. Therefore, soil property maps provide a geographic reference for the soil properties of a field of interest. Soil type can refer to a taxonomic unit in soil science, where each soil type includes a defined set of shared properties. Soil types can include, for example, sandy soil, clay, silty soil, peat soil, chalky soil, loam, and various other soil types. Soil moisture can refer to the amount of water held or otherwise contained in the soil. Soil moisture can also be referred to as soil wettability. Soil cover can refer to the amount of species or material covering the soil, including vegetation material such as crop residues or cover crops, fragments, and various other species or materials. Typically, in agricultural terminology, soil cover includes a measure of residual crop residues, such as the amount of remaining plant stems, and a measure of cover crops. Soil structure can refer to the arrangement of the solid parts of the soil and the pore space between these solid parts. Soil structure can include the arrangement of individual particles (e.g., individual particles of sand, silt, and clay). Soil structure can be described using gradations (degree of aggregation), categories (average size of aggregates), and forms (types of aggregates), as well as various other descriptions. These are just examples. Various other characteristics and properties of soil can be mapped to soil property values ​​on a soil property map.

[0030] These soil property maps can be generated based on data collected during another operation corresponding to the field of interest, such as previous agricultural operations in the same season, such as planting or spraying, and previous agricultural operations in past seasons, such as previous harvesting. The agricultural machinery performing these operations can have onboard sensors that detect characteristics indicating soil properties, such as soil type, soil moisture, soil cover, soil structure, and various other characteristics indicating various other soil properties. Furthermore, the operating characteristics of the agricultural machinery during the prior operation (previous operation), machine settings or performance characteristics, and other data can be used to generate the soil property map. For example, header height data indicating the height of the harvester's header across different geographical locations in the field of interest during the previous harvesting operation, and weather data indicating weather conditions, such as precipitation or wind data during transition periods (e.g., the time since the last harvesting operation and the time when the soil property map was generated), can be used to generate a soil moisture map. For example, by knowing the header height, the amount of remaining plant residue (e.g., crop straw) can be known or estimated, and the level of soil moisture can be predicted along with precipitation data. This is just one example.

[0031] In other examples, surveys of the field of interest can be conducted using various machines equipped with sensors (e.g., imaging systems) or by humans. Data collected during these surveys can be used to generate soil property maps. For example, an aerial survey can be conducted over the field of interest, imaging the field, and a soil property map can be generated based on the image data. In another example, humans can enter the field with or without the aid of sensors to collect various data or samples, and a soil property map of the field can be generated based on this data or samples. For example, humans can collect core samples at different geographical locations within the field of interest. These core samples can be used to generate a soil property map of the field. In other examples, soil property maps can be based on user or operator input, such as input from farm managers, which can provide various data collected or observed by the user or operator.

[0032] In addition, soil property maps can be obtained from remote sources, such as third-party service providers or government agencies, such as the U.S. Department of Agriculture's Natural Resources Defense Council (NRCS), the U.S. Geological Survey (USGS), and various other remote sources.

[0033] In some examples, soil characteristic maps can be derived from sensor readings of one or more bands of electromagnetic radiation reflected by the surface of the soil (or field). These bands can be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum, without limitation.

[0034] Historical crop moisture maps illustratively map crop moisture values ​​at different geographic locations within one or more fields of interest. These historical crop moisture maps are collected from past harvesting operations on the field(s). Crop moisture maps can display crop moisture in units of crop moisture values. An example of a unit of crop moisture value includes, for example, a numerical value, such as a percentage. In some examples, historical crop moisture maps can be derived from sensor readings of one or more crop moisture sensors. Without limitation, these crop moisture sensors can include capacitive sensors, microwave sensors, or conductivity sensors, etc. In some examples, crop moisture sensors can utilize one or more electromagnetic radiation bands to detect crop moisture.

[0035] Therefore, this discussion pertains to examples in which the system receives one or more of the following: historical crop moisture maps, vegetation index maps, topographic maps, soil property maps, or maps generated during prior operations; and also uses field sensors during harvesting operations to detect features as variables indicating crop moisture. The system generates a model that models the relationship between historical crop moisture values, vegetation index values, topographic feature values, or soil property values ​​from one or more received maps and field data from field sensors. This model is used to generate a functional predictive crop moisture map that predicts crop moisture in the field. The functional predictive crop moisture map generated during harvesting operations can be presented to the operator or other user, or both, who is automatically controlling the agricultural harvester during the harvesting operation. In some examples, the maps received by the system plot feature values ​​other than crop moisture (e.g., "non-crop moisture values"), such as vegetation index values, topographic feature values, or soil property values. In some examples, the maps received by the system plot historical values ​​of crop moisture.

[0036] 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 should be understood that this description is also applicable to other types of harvesters, such as cotton harvesters, sugarcane harvesters, self-propelled hay harvesters, slashers, or other agricultural operating machines. Therefore, this disclosure is intended to cover the various types of harvesters described, and is therefore not limited to combine harvesters. Moreover, this disclosure relates to other types of operating machines, such as agricultural seeders and sprayers in which the generation of predictive maps can be applied, construction equipment, forestry equipment, and lawn management equipment. Therefore, this disclosure is intended to cover these various types of harvesters and other operating machines, and is therefore not limited to combine harvesters.

[0037] like Figure 1As shown, the agricultural harvester 100 schematically includes an operator's cab 101, which may have various different operator interface mechanisms for controlling the agricultural harvester 100. The agricultural harvester 100 includes front-end equipment, such as a header 102 and a cutter generally indicated by 104. The agricultural harvester 100 also includes a feed chamber 106, a feed accelerator 108, and a thresher generally indicated by 110. The feed chamber 106 and the feed accelerator 108 form part of a material handling subsystem 125. The header 102 is pivotally coupled to the frame 103 of the agricultural harvester 100 along a pivot axis 105. One or more actuators 107 drive the header 102 to move about the axis 105 in a direction generally indicated by arrow 109. Thus, the vertical position of the header 102 above the ground 111 (header height) can be controlled by actuating the actuators 107 that travel above the ground. Figure 1 As not shown, the agricultural harvester 100 may further include one or more actuators that operate to apply a tilt angle, a roll angle, or both to the header 102 or a portion thereof. Tilt refers to the angle at which the cutter 104 engages with the crop. For example, the tilt angle is increased by controlling the header 102 to point the distal edge 113 of the cutter 104 more towards the ground. The tilt angle is decreased by controlling the header 102 to point the distal edge 113 of the cutter 104 more away from the ground. The roll angle refers to the orientation of the header 102 about the longitudinal axis of the agricultural harvester 100.

[0038] The threshing machine 110 schematically includes a threshing rotor 112 and a set of recesses 114. Additionally, the agricultural harvester 100 includes a separator 116. The agricultural harvester 100 also includes a cleaning subsystem or cleaning chamber (collectively referred to as cleaning subsystem 118), which includes a cleaning fan 120, a chaff screen 122, and a screen 124. The material handling subsystem 125 also includes a discharge threshing drum 126, a tailings elevator 128, a clean grain elevator 130, and an unloading screw conveyor 134 and a nozzle 136. The clean grain elevator moves clean grain into a clean grain trough 132. The agricultural harvester 100 also includes a stubble subsystem 138, which may include a shredder 140 and a spreader 142. The agricultural harvester 100 also includes a propulsion subsystem, which includes an engine driving ground engagement components 144 (e.g., wheels or tracks). In some examples, the combine harvester within the scope of this disclosure may have more than one of any of the subsystems mentioned above. In some examples, the agricultural harvester 100 may have a left cleaning subsystem and a right cleaning subsystem, a separator, etc., which are... Figure 1 Not shown in the image.

[0039] In operation, and as an overview, the combine harvester 100 schematically moves across the field in the direction indicated by arrow 147. As the combine harvester 100 moves, the header 102 (and associated reel 164) engages the crop to be harvested and collects the crop toward the cutter 104. The operator of the combine harvester 100 can be a local human operator, a remote human operator, or an automated system. Operator commands are commands issued by the operator. The operator of the combine harvester 100 can determine one or more of the header 102's height setting, tilt angle setting, or roll angle setting. For example, the operator inputs one or more settings (described in more detail below) into the control system of the control actuator 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 associated actuators (not shown) that operate to change the tilt angle and roll angle of the header 102. Actuator 107 maintains the cutter head 102 at a height above ground 111 based on a height setting, and maintains it at the desired tilt and roll angles where applicable. Each of the height, roll, and tilt settings can be implemented independently of the others. The control system responds to cutter head errors (e.g., the difference between the height setting and the measured height of the cutter head 104 above ground 111, and in some examples, tilt and roll angle errors) with a responsiveness determined based on a selected sensitivity level. If the sensitivity level is set at a higher sensitivity level, the control system responds to smaller cutter head position errors and attempts to reduce the detected errors faster than if the sensitivity level were lower.

[0040] Returning to the description of the operation of the agricultural harvester 100, after the crop is cut by the cutter 104, the cut crop material moves toward the feed accelerator 108 via a conveyor in the feed chamber 106, which accelerates the crop material into the threshing machine 110. The crop material is threshed by the rotor 112, which rotates the crop against the recess 114. The threshed crop material is moved by the separator rotor in the separator 116, where a portion of the stubble is moved toward the stubble subsystem 138 by the discharge threshing drum 126. This portion of stubble conveyed to the stubble subsystem 138 is chopped by the stubble chopper 140 and spread on the field by the spreader 142. In other configurations, the stubble is released from the agricultural harvester 100 into a stockpile. In other examples, the stubble subsystem 138 may include a weed seed remover (not shown), such as a seed bagger or other seed collector, or a seed crusher or other seed destroyer.

[0041] The grain falls into the cleaning subsystem 118. A husk sieve 122 separates some of the larger pieces of material from the grain, and a screen 124 separates some of the finer pieces of material from the clean grain. The clean grain falls onto a screw conveyor that moves the grain to the inlet of a clean grain elevator 130, which moves the clean grain upwards, thereby storing it in a clean grain trough 132. Stubble is removed from the cleaning subsystem 118 by an airflow generated by a cleaning fan 120. The cleaning fan 120 directs air upwards along an airflow path through the screen and husk sieve. The airflow carries the stubble from the agricultural harvester 100 backwards toward the stubble handling subsystem 138.

[0042] Tail feeder 128 returns the tail feed to thresher 110, where it is re-threshed. Alternatively, the tail feed may be conveyed by tail feeder or another conveyor to a separate re-threshing unit, where it is also re-threshed.

[0043] Figure 1 Also shown in one example, the agricultural harvester 100 includes a ground speed sensor 146, one or more separator loss sensors 148, a grain cleaning camera 150, a front view image capture mechanism 151 which may be in the form of a stereo camera or a monocular camera, and one or more loss sensors 152 disposed in the grain cleaning subsystem 118.

[0044] Ground speed sensor 146 senses the travel speed of the agricultural harvester 100 on the ground. Ground speed sensor 146 can sense the travel speed of the agricultural harvester 100 by sensing the rotational speed of ground-engaging components (such as wheels or tracks), drive shafts, axles, or other components. In some cases, a positioning system can be used to sense the travel speed, such as a global positioning system (GPS), dead reckoning system, long range navigation (LORAN) system, or various other systems or sensors that provide an indication of travel speed.

[0045] Loss sensor 152 schematically provides output signals indicating the amount of grain loss occurring on the right and left sides of the grain cleaning subsystem 118. In some examples, sensor 152 is an impact sensor that counts grain impacts per unit time or per unit distance traveled to provide an indication of grain loss occurring at the grain cleaning subsystem 118. The impact sensors for the right and left sides of the grain cleaning subsystem 118 can provide individual signals or combined or aggregated signals. In some examples, sensor 152 may include a single sensor, rather than providing a separate sensor for each grain cleaning subsystem 118.

[0046] Separator loss sensor 148 provides indication of the left and right separators (in Figure 1 (Not shown separately) The grain loss signal in the separator. The separator loss sensor 148 can be associated with the left and right separators and can provide separate grain loss signals or combined or aggregated signals. In some cases, various different types of sensors can also be used to sense grain loss in the separator.

[0047] The agricultural harvester 100 may also include other sensors and measuring mechanisms. For example, the agricultural harvester 100 may include one or more of the following sensors: a header height sensor that senses the height of the header 102 above the ground 111; a stability sensor that senses vibration or jumping (and amplitude) of the agricultural harvester 100; a stubble setting sensor configured to sense whether the agricultural harvester 100 is configured to chop stubble, create a stockpile, etc.; a cleaning chamber fan speed sensor for sensing the speed of the cleaning fan 120; a recess gap sensor that senses the gap between the rotor 112 and the recess 114; a threshing rotor speed sensor that senses the rotor speed of the rotor 112; a tailings characteristic sensor; a husk screen gap sensor that senses the size of the opening in the husk screen 122; a screen mesh gap sensor that senses the size of the opening in the screen mesh 124; and a material other than grain sensor. A grain (MOG) humidity sensor senses the humidity level of the MOG passing through the harvester 100; one or more machine setting sensors are configured to sense various configurable settings of the harvester 100; a machine orientation sensor senses the orientation of the harvester 100; and a crop characteristic sensor senses various types of crop characteristics (such as crop type, crop humidity, and other crop characteristics). The crop characteristic sensor can also be configured to sense characteristics of the cut crop material as it is processed by the harvester 100. For example, in some cases, the crop characteristic sensor may sense: grain quality (such as broken grain, MOG level); grain composition (such as starch and protein); and grain feed rate as the grain travels through the feed chamber 106, the clean grain elevator 130, or other locations within the harvester 100. Grain characteristics may also be sensed. Grain characteristics may include, but are not limited to, grain humidity, grain size, and grain test weight. Waste characteristics include waste level, waste flow rate, waste volume, and waste composition. The crop characteristic sensor can also sense the feed rate of biomass through the feed chamber 106, through the separator 116, or elsewhere in the harvester 100. The crop characteristic sensor can also sense the feed rate through the elevator 130 or other parts of the harvester 100 as the grain mass flow rate, or provide other output signals indicating other sensed variables. The crop characteristic sensor may include one or more crop moisture sensors that sense the moisture content of the crop being harvested by the harvester. An internal material distribution sensor can sense the material distribution inside the harvester 100.

[0048] Crop moisture sensors may include capacitive moisture sensors. In one example, a capacitive moisture sensor may include a moisture measuring unit for containing a sample of crop material and a capacitor for determining the dielectric properties of the sample. In other examples, a crop moisture sensor may be a microwave sensor or a conductivity sensor. In other examples, a crop moisture sensor may utilize the wavelength of electromagnetic radiation to sense the moisture content of the crop material. The crop moisture sensor may be disposed within the feed chamber 106 (or otherwise have a sensing path to the crop material within the feed chamber 106) and configured to sense the moisture of the harvested crop material passing through the feed chamber 106. In other examples, the crop moisture sensor may be located in other areas within the agricultural harvester 100, such as in a clean grain lift, a clean grain auger, or in a grain tank. It should be noted that these are merely examples of crop moisture sensors, and various other crop moisture sensors are contemplated.

[0049] In some examples, crop moisture is the ratio of water to other plant material (e.g., the dry matter or total biomass of a grain). In other examples, crop moisture may relate to the amount of water outside the plant, such as dew, frost, or rain. Crop moisture can be measured in absolute terms, such as the percentage of water by mass or volume of material. In other examples, crop moisture may be reported in relative categories, such as “high, medium, low,” “wet, typical / normal, dry,” etc. Crop moisture can be measured in a variety of ways. In some examples, crop moisture may be related to crop color, such as green or brown and the distribution of green areas throughout the plant. In some examples, it may be related to the rate at which crop color changes from green to brown during aging. In other examples, crop moisture can be measured using characteristics in which electric fields or electromagnetic waves interact with water molecules. These characteristics, without limitation, include dielectric constant, resonance, reflection, absorption, or transmission. In other examples, crop humidity may be related to plant morphology, such as 3D leaf shape (e.g., corn leaves “rolling”), leaf stomatal diameter that affects plant temperature, and relative stem diameter over time.

[0050] Before describing how the agricultural harvester 100 generates a functional predictive crop moisture map and uses this predictive crop moisture map for control, a brief description of some items on the agricultural harvester 100 and their operation will be provided first. Figure 2The description in Figure 3 illustrates the following: A general type of prior information map is received, and information from the prior information map is combined with georeferenced sensor signals generated by field sensors. These sensor signals indicate characteristics of the field, such as the characteristics of crops or weeds present in the field. These field characteristics may include, but are not limited to, field features (such as slope, weed intensity, weed type, soil moisture, surface quality); crop characteristics (such as crop height, crop moisture, crop density, crop condition); grain characteristics (such as grain moisture, grain size, grain test weight); and machine performance characteristics (such as loss level, work quality, fuel consumption, and power utilization). The relationship between the feature values ​​obtained from the field sensor signals and the prior information map values ​​is identified, and this relationship is used to generate a new functional prediction map. The functional prediction map predicts values ​​at different geographic locations in the field, and one or more of these values ​​can be used to control one or more subsystems of a machine, such as an agricultural harvester. In some cases, the functional prediction map can be presented to a user, such as an operator of an agricultural operating machine, which may be an agricultural harvester. Function prediction maps can be presented to users visually (e.g., via a display), tactilely, or audibly. Users can interact with function prediction maps to perform editing operations and other user interface actions. In some cases, function prediction maps can be used to control agricultural machinery (such as agricultural harvesters), presented to operators or other users, or presented to operators or users for operator or user interaction, among other things.

[0051] In reference Figure 2 After describing the general method in Figure 3, refer to... Figure 4 and Figure 5 A more specific method for generating a functionally predictive crop moisture map is described, which can be presented to an operator or user, or used to control an agricultural harvester 100, or both. Again, although this discussion is directed to agricultural harvesters, and particularly combine harvesters, the scope of this disclosure extends to other types of agricultural harvesters or other agricultural operating machines.

[0052] Figure 2 This is a block diagram showing some parts of an example agricultural harvester 100. Figure 2An agricultural harvester 100 is shown schematically including one or more processors or servers 201, a data storage device 202, a geolocation sensor 204, a communication system 206, and one or more field sensors 208 that sense one or more agricultural features of the field during harvesting operations. Agricultural features can include any features capable of influencing the harvesting operations. Some examples of agricultural features include features of the harvester, the field, the plants on the field, and the weather. Other types of agricultural features are also included. The field sensors 208 generate values ​​corresponding to the sensed features. The agricultural harvester 100 also includes a prediction model or relation generator (collectively referred to as "prediction model generator 210"), a prediction map generator 212, a control area generator 213, a control system 214, one or more controllable subsystems 216, and an operator interface mechanism 218. The agricultural harvester 100 may also include a variety of other agricultural harvester functions 220. The field sensors 208 include, for example, onboard sensors 222, remote sensors 224, and other sensors 226 that sense features of the field during agricultural operations. The predictive model generator 210 schematically includes a prior information variable to field variable model generator 228, and the predictive model generator 210 may include other items 230. The control system 214 includes a communication system controller 229, an operator interface controller 231, a settings controller 232, a path planning controller 234, a feed speed controller 236, a header and reel controller 238, a belt conveyor controller 240, a platform position controller 242, a stubble system controller 244, a machine cleaning controller 245, and a zone controller 247, and the control system 214 may include other items 246. The controllable subsystem 216 includes a machine and header actuator 248, a propulsion subsystem 250, a steering subsystem 252, a stubble subsystem 138, a machine cleaning subsystem 254, and the controllable subsystem 216 may include various other subsystems 256. For example, control system 214 may generate one or more control signals to control material handling subsystem 125 to control or compensate the internal material distribution within agricultural harvester 100 based on a received functional prediction map (with or without control area).

[0053] Figure 2The agricultural harvester 100 is also shown to receive one or more prior information maps 258. As described below, the one or more prior information maps 258 include, for example, vegetation index maps or vegetation maps, topographic maps, or soil property maps from prior operations in the field. However, the one or more prior information maps 258 may also encompass other types of data obtained prior to the harvesting operation or maps from prior operations, such as historical crop humidity maps from the past few years containing contextual information associated with historical crop humidity. This contextual information may include, but is not limited to, one or more weather conditions during the growing season, the presence of pests, geographical location, soil type, irrigation, treatment applications, etc. Weather conditions may include, but are not limited to, seasonal precipitation, the presence of hail that can damage crops, the presence of strong winds, and seasonal temperatures. Some examples of pests broadly include: insects, fungi, weeds, bacteria, viruses, etc. Some examples of treatment applications include herbicides, pesticides, fungicides, fertilizers, mineral supplements, etc. Figure 2 The diagram also illustrates that operator 260 can operate agricultural harvester 100. Operator 260 interacts with operator interface mechanism 218. In some examples, operator interface mechanism 218 may include joysticks, control levers, steering wheels, linkages, pedals, buttons, dials, keypads, user-actuable elements (such as icons, buttons, etc.) on a user interface display device, microphones and speakers (where speech recognition and speech synthesis are provided), and various other types of control devices. Where a touch-sensitive display system is provided, operator 260 may interact with operator interface mechanism 218 using touch gestures. The examples described above are provided as illustrative examples and are not intended to limit the scope of this disclosure. Therefore, other types of operator interface mechanisms 218 may be used, and other types of operator interface mechanisms are within the scope of this disclosure.

[0054] The prior information map 258 can be downloaded to the agricultural harvester 100 and stored in the data storage device 202 using the communication system 206 or other means. In some examples, the communication system 206 may be a cellular communication system, a system for communication over a wide area network or a local area network, a system for communication over a near-field communication network, or a communication system configured to communicate over any one or a combination of various other networks. The communication system 206 may also include systems that facilitate the downloading or transfer of information to or from a secure digital (SD) card or a universal serial bus (USB) card, or both.

[0055] The geolocation sensor 204 schematically senses or detects the geolocation or orientation of the agricultural harvester 100. The geolocation sensor 204 may include, but is not limited to, a GNSS receiver that receives signals from a Global Navigation Satellite System (GNSS) satellite transmitter. The geolocation sensor 204 may also include a real-time kinematic (RTK) component configured to improve the accuracy of the position data derived from the GNSS signals. The geolocation sensor 204 may include any of a dead reckoning system, a cellular triangulation system, or a variety of other geolocation sensors.

[0056] The field sensor 208 can be referenced above. Figure 1 Any of the sensors described. Field sensor 208 includes onboard sensor 222 mounted on the agricultural harvester 100. Such sensors may include, for example, an impact plate sensor, a radiation attenuation sensor, or an image sensor (e.g., a grain-clearing camera) inside the agricultural harvester 100. Field sensor 208 may also include remote field sensor 224 that captures field information. Field data includes data acquired from sensors mounted on the agricultural harvester, or data acquired by any sensor that detects data during harvesting operations.

[0057] After being retrieved by the agricultural harvester 100, the prior infographic selector 209 can filter or select one or more specific prior infographics 258 for use by the predictive model generator 210. In one example, the prior infographic selector 209 selects a infographic based on a comparison of contextual information in the prior infographic with current contextual information. For example, a historical crop moisture map could be selected from a year in the past few years in which the weather conditions during the growing season were similar to those of this year. Alternatively, for example, a historical crop moisture map could be selected from a year in the past few years when the contextual information is dissimilar. For example, a “dry” previous year (i.e., with drought or reduced precipitation) could be selected for the historical crop moisture map, while the current year is “wet” (i.e., with increased precipitation or flooding). While this relationship may be reversed, it can still be a useful historical relationship. For example, areas that are dry in a dry year may be areas with higher crop moisture in a wet year because these areas are likely to retain more water in a wet year. Current contextual information can include contextual information beyond the immediate contextual information. For example, the current contextual information may include, but is not limited to, a set of information corresponding to the current growing season, a dataset corresponding to the winter before the current growing season, or a dataset corresponding to the past few years, etc.

[0058] Contextual information can also be used to correlate regions with similar contextual characteristics, regardless of whether the geographic location corresponds to the same location on prior information map 258. For example, historical crop moisture values ​​from other fields in regions with similar topography or soil characteristics, or both, can be used as prior information map 258 to create a predicted crop moisture map. For example, contextual feature information associated with different locations can be applied to locations on prior information map 258 with similar feature information.

[0059] Predictive model generator 210 generates a model indicating the relationship between values ​​sensed by field sensors 208 and features mapped to the field by prior information map 258. For example, if prior information map 258 maps vegetation index values ​​to different locations in the field, and field sensors 208 sense values ​​indicating crop moisture, then prior information variable to field variable model generator 228 generates a predictive crop moisture model that models the relationship between vegetation index values ​​and crop moisture values. Then, predictive map generator 212 uses the predictive crop moisture model generated by predictive model generator 210 to generate a functional predictive crop moisture map that predicts crop moisture values ​​at different locations in the field based on prior information map 258. Alternatively, for example, if prior information map 258 maps historical crop moisture values ​​to different locations in the field, and field sensors 208 positively sense values ​​indicating crop moisture, then prior information variable to field variable model generator 228 produces a predictive crop moisture model that models the relationship between historical crop moisture values ​​(with or without contextual information) and current crop moisture values. Then, the prediction map generator 212 uses the predicted crop moisture model generated by the prediction model generator 210 to generate a functional prediction crop moisture map based on the prior information map 258, predicting crop moisture values ​​at different locations in the field. Alternatively, for example, if the prior information map 258 maps topographic feature values ​​to different locations in the field, and the field sensor 208 is positively sensing values ​​indicating crop moisture, the prior information variable to field variable model generator 228 generates a predicted crop moisture model that models the relationship between topographic feature values ​​and crop moisture values. Then, the prediction map generator 212 uses the predicted crop moisture model generated by the prediction model generator 210 to generate a functional prediction crop moisture map based on the prior information map 258, predicting crop moisture values ​​at different locations in the field. Alternatively, for example, if prior information map 258 maps soil property values ​​to different locations in the field, and field sensor 208 is positively sensing values ​​indicating crop moisture, then prior information variable to field variable model generator 228 generates a predictive crop moisture model that models the relationship between soil property values ​​and crop moisture values. Then, prediction map generator 212 uses the predictive crop moisture model generated by prediction model generator 210 to generate a functional predictive crop moisture map based on prior information map 258, predicting crop moisture values ​​at different locations in the field. In some examples, the type of data in functional predictive map 263 can be the same as the type of field data sensed by field sensor 208. In some cases, the type of data in functional predictive map 263 can have different units than the data sensed by field sensor 208.In some examples, the type of data in the functional prediction graph 263 may be different from, but related to, the type of data sensed by the field sensor 208. For example, in some examples, the field data type may indicate the type of data in the functional prediction graph 263. In some examples, the type of data in the functional prediction graph 263 may be different from the data type in the prior information graph 258. In some cases, the type of data in the functional prediction graph 263 may have a different unit than the data in the prior information graph 258. In some examples, the type of data in the functional prediction graph 263 may be different from, but related to, the data type in the prior information graph 258. For example, in some examples, the data type in the prior information graph 258 may indicate the type of data in the functional prediction graph 263. In some examples, the type of data in the functional prediction graph 263 may be different from one or both of the field data type sensed by the field sensor 208 and the data type in the prior information graph 258. In some examples, the type of data in the functional prediction graph 263 is the same as one or both of the field data type sensed by the field sensor 208 and the data type in the prior information graph 258. In other examples, the type of data in the functional prediction graph 263 is the same as one of the field data type sensed by the field sensor 208 or the data type in the prior information graph 258, but different from the other.

[0060] Continuing with the previous example, prediction map generator 212 can use the values ​​in prior information map 258 and the model generated by prediction model generator 210 to generate a functional prediction map 263 predicting crop moisture at different locations in the field. Prediction map generator 212 then outputs prediction map 264.

[0061] like Figure 2As shown, prediction map 264 uses prior information values ​​from prior information map 258 at various locations in the field (or locations with similar contextual information even in different fields) and a prediction model to predict the values ​​of features at these locations in the field (the same features sensed by one or more field sensors 208) or the values ​​of features related to features sensed by one or more field sensors 208. For example, if prediction model generator 210 has generated a prediction model indicating the relationship between vegetation index values ​​and crop moisture, then, given vegetation index values ​​at different locations in the field, prediction map generator 212 generates prediction map 264 predicting crop moisture values ​​at different locations in the field. The vegetation index values ​​at these locations obtained from prior information map 258 and the relationship between vegetation index values ​​and crop moisture obtained from the prediction model are used to generate prediction map 264. Alternatively, for example, if the prediction model generator 210 has already generated a prediction model indicating the relationship between historical crop moisture values ​​and crop moisture, then, given historical crop moisture values ​​at different locations on the field, the prediction map generator 212 generates a prediction map 264 predicting the crop moisture values ​​at those different locations on the field. The historical crop moisture values ​​at these locations obtained from the prior information map 258, and the relationship between the historical crop moisture values ​​and crop moisture obtained from the prediction model, are used to generate the prediction map 264. Alternatively, for example, if the prediction model generator 210 has already generated a prediction model indicating the relationship between topographic feature values ​​and crop moisture, then, given topographic feature values ​​at different locations on the field, the prediction map generator 212 generates a prediction map 264 predicting the crop moisture values ​​at those different locations on the field. The topographic feature values ​​at these locations obtained from the prior information map 258, and the relationship between the topographic feature values ​​and crop moisture obtained from the prediction model, are used to generate the prediction map 264. Alternatively, for example, if the prediction model generator 210 has already generated a prediction model indicating the relationship between soil property values ​​and crop moisture, then, given the soil property values ​​at different locations on the field, the prediction map generator 212 generates a prediction map 264 that predicts the crop moisture values ​​at those different locations on the field. The soil property values ​​at these locations obtained from the prior information map 258 and the relationship between the soil property values ​​and crop moisture obtained from the prediction model are used to generate the prediction map 264.

[0062] The following will describe some changes in the data types mapped in prior information graph 258, the data types sensed by field sensor 208, and the data types predicted in prediction graph 264.

[0063] In some examples, the data type in the prior infographic 258 differs from the data type sensed by the field sensor 208, but the data type in the prediction infographic 264 is the same as the data type sensed by the field sensor 208. For example, the prior infographic 258 could be a vegetation index map, and the variable sensed by the field sensor 208 could be crop moisture. The prediction infographic 264 could then be a predicted crop moisture map that maps predicted crop moisture values ​​to different geographic locations in the field. In another example, the prior infographic 258 could be a vegetation index map, and the variable sensed by the field sensor 208 could be crop height. The prediction infographic 264 could then be a predicted crop height map that maps predicted crop height values ​​to different geographic locations in the field.

[0064] Furthermore, in some examples, the data type in the prior information map 258 differs from the data type sensed by the field sensor 208, and the data type in the prediction map 264 differs from both the data type in the prior information map 258 and the data type sensed by the field sensor 208. For example, the prior information map 258 could be a vegetation index map, and the variable sensed by the field sensor 208 could be crop moisture. The prediction map 264 could then be a predicted biomass map that maps predicted biomass values ​​to different geographic locations in the field. In another example, the prior information map 258 could be a vegetation index map, and the variable sensed by the field sensor 208 could be yield. The prediction map 264 could then be a predicted velocity map that maps predicted harvester velocity values ​​to different geographic locations in the field.

[0065] In some examples, the prior information map 258 is derived from previous passage through the field during a priori operations, and its data type differs from the data type sensed by the field sensor 208, but the data type in the prediction map 264 is the same as that sensed by the field sensor 208. For example, the prior information map 258 could be a topographic map generated during planting, and the variable sensed by the field sensor 208 could be crop moisture. The prediction map 264 could then be a predicted crop moisture map that maps predicted crop moisture values ​​to different geographic locations within the field.

[0066] In some examples, the prior information map 258 is derived from a field previously traversed during a prior operation, and the data type is the same as that sensed by the field sensor 208, and the data type in the prediction map 264 is also the same as that sensed by the field sensor 208. For example, the prior information map 258 could be a crop moisture map generated during the previous year, and the variable sensed by the field sensor 208 could be crop moisture. The prediction map 264 could then be a predicted crop moisture map that maps predicted crop moisture values ​​to different geographic locations within the field. In such an example, the prediction model generator 210 could use the relative crop moisture differences from the geographic reference prior information map 258 from the previous year to generate a prediction model that models the relationship between the relative crop moisture differences on the prior information map 258 and the crop moisture values ​​sensed by the field sensor 208 during the current harvest operation. The prediction model is then used by the prediction map generator 212 to generate a predicted yield map.

[0067] In some examples, prediction map 264 may be provided to control zone generator 213. Control zone generator 213 groups adjacent portions of a region into one or more control zones based on data values ​​in prediction map 264 associated with those adjacent portions. A control zone may include two or more consecutive portions of a region (such as a field) for which control parameters corresponding to the control zones used to control the controllable subsystem are constant. For example, the response time to changing the settings of controllable subsystem 216 may not be satisfactory in responding to changes in values ​​contained in a map such as prediction map 264. In this case, control zone generator 213 analyzes the map and identifies control zones with defined dimensions to accommodate the response time of controllable subsystem 216. In another example, the size of the control zone may be determined to reduce wear caused by excessive actuator movement due to continuous adjustment. In some examples, there may be different groups of control zones for each controllable subsystem 216 or group of controllable subsystems 216. Control zones may be added to prediction map 264 to obtain prediction control zone map 265. The predicted control area map 265 can therefore be similar to the predicted map 264, except that the predicted control area map 265 includes control area information defining the control area. Therefore, as described herein, the functional predicted map 263 may or may not include a control area. Both the predicted map 264 and the predicted control area map 265 are functional predicted maps 263. In one example, the functional predicted map 263 does not include a control area, as in the predicted map 264. In another example, the functional predicted map 263 does include a control area, as in the predicted control area map 265. In some examples, if an intercropping production system is implemented, multiple crops may coexist in the field. In this case, the predicted map generator 212 and the control area generator 213 are able to identify the location and characteristics of two or more crops, and then generate the predicted map 264 and the predicted control area map 265 accordingly.

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

[0069] Predictive map 264 or predictive control area map 265, or both, is provided to control system 214, which generates control signals based on predictive map 264 or predictive control area map 265, or both. In some examples, communication system controller 229 controls communication system 206 to communicate predictive map 264 or predictive control area map 265, or control signals based on predictive map 264 or predictive control area map 265, to other agricultural harvesters harvesting in the same field. In some examples, communication system controller 229 controls communication system 206 to send predictive map 264, predictive control area map 265, or both, to other remote systems.

[0070] Operator interface controller 231 is operable to generate control signals to control operator interface mechanism 218. Operator interface controller 231 is also operable to present operator 260 with prediction map 264 or prediction control area map 265, or other information derived from or based on prediction map 264, prediction control area map 265, or both. Operator 260 can be a local operator or a remote operator. As an example, controller 231 generates control signals to control the display mechanism to display one or both of prediction map 264 and prediction control area map 265 to operator 260. Controller 231 can generate operator-actuable mechanisms that are shown and can be actuated by the operator to interact with the displayed maps. The operator can edit the maps, for example, by correcting crop moisture values ​​displayed on the maps based on the operator's observation. Setting controller 232 can generate control signals based on prediction map 264, prediction control area map 265, or both to control various settings on agricultural harvester 100. For example, controller 232 can generate control signals to control the machine and header actuator 248. In response to the generated control signals, the machine and header actuator 248 operate to control, for example, screen and chaff screen settings, concave clearance, rotor settings, grain clearing fan speed settings, header height, header function, reel speed, reel position, belt conveyor function (where the harvester 100 is coupled to the belt conveyor header), corn header function, internal distribution control, and other actuators 248 affecting other functions of the harvester 100. Path planning controller 234 schematically generates control signals to control steering subsystem 252 to turn the harvester 100 according to a desired path. Path planning controller 234 can control the path planning system to generate a route for the harvester 100 and can control propulsion subsystem 250 and steering subsystem 252 to turn the harvester 100 along that route. The feed rate controller 236 can control various subsystems (such as the propulsion subsystem 250 and the machine actuator 248) to control the feed rate based on prediction map 264 or prediction control area map 265, or both. For example, when the harvester 100 approaches an area where crop moisture content is above a selected threshold, the feed rate controller 236 can reduce the speed of the harvester 100 to maintain a constant feed rate of grain or biomass through the machine. The header and reel controller 238 can generate control signals to control the header or reel or other header functions. The belt conveyor controller 240 can generate control signals based on prediction map 264, prediction control area map 265, or both to control the belt conveyor belt or other belt conveyor functions.The tabletop position controller 242 can generate control signals based on prediction map 264 or prediction control area map 265, or both, to control the position of the tabletop included on the harvester. The stubble system controller 244 can generate control signals based on prediction map 264 or prediction control area map 265, or both, to control the stubble subsystem 138. The machine cleaning controller 245 can generate control signals to control the machine cleaning subsystem 254. For example, based on different types of seeds or weeds passing through the harvester 100, a specific type of machine cleaning operation or the frequency of performing cleaning operations can be controlled. Other controllers included on the harvester 100 can also control other subsystems based on prediction map 264 or prediction control area map 265, or both.

[0071] Figure 3A and Figure 3B (Hereinafter referred to as Figure 3) shows a flowchart illustrating an example of the operation of an agricultural harvester 100 in generating a prediction map 264 and a prediction control area map 265 based on prior information map 258.

[0072] At 280, the agricultural harvester 100 receives a priori information map 258. Examples of the priori information map 258 or receiving the priori information map 258 are discussed with respect to boxes 282, 284, and 286. As discussed above, as shown in box 282, the priori information map 258 maps the values ​​of variables corresponding to a first feature to different locations in the field. For example, a priori information map may be a map generated during a priori operation or a map generated based on data from a priori operation on the field (e.g., a previous spraying operation performed by a sprayer). Data for the priori information map 258 may also be collected in other ways. For example, data may be collected based on aerial images or measurements taken during the previous year, earlier in the current growing season, or at other times. This information may also be based on data detected or collected in other ways (besides using aerial images). For example, data for the priori information map 258 may be transmitted to the agricultural harvester 100 using communication system 206 and stored in data storage device 202. The data from the prior information diagram 258 can also be provided to the agricultural harvester 100 in other ways using the communication system 206, and this is represented by box 286 in the flowchart of Figure 3. In some examples, the prior information diagram 258 can be received by the communication system 206.

[0073] In box 287, the prior infographic selector 209 can select one or more infographics from a plurality of candidate prior infographics received in box 280. For example, historical crop moisture maps from multiple years can be received as candidate prior infographics. Each of these infographics can contain contextual information, such as weather patterns over a period of time (e.g., one year), pest surges over a period of time (e.g., one year), soil properties, topographic features, etc. Contextual information can be used to select which historical crop moisture map should be selected. For example, weather conditions over a period of time (e.g., the current year) or current soil properties of the field can be compared with the weather conditions and soil properties in the contextual information of each candidate prior infographic. The result of this comparison can be used to select which historical crop moisture map should be selected. For example, years with similar weather conditions often lead to similar crop moisture or crop moisture trends in the field. In some cases, years with opposite weather conditions can also be used to predict crop moisture based on historical crop moisture. For example, an area with low crop moisture in a dry year may have high crop moisture in a wet year because the area may retain more moisture. The process of selecting one or more prior infographics by the prior infographic selector 209 can be manual, semi-automatic, or automatic. In some examples, during harvesting operations, the prior infographic selector 209 can continuously or intermittently determine whether different prior infographics have a better relationship with field sensor values. If a different prior infographic is more closely associated with the field data, the prior infographic selector 209 can replace the currently selected prior infographic with the more relevant one.

[0074] At the start of the harvesting operation, field sensor 208 generates sensor signals indicating one or more field data values ​​that indicate plant characteristics, such as crop moisture, as shown in box 288. Examples of field sensor 288 are discussed with respect to boxes 222, 290, and 226. As explained above, field sensor 208 includes airborne sensor 222, remote field sensor 224 (such as a UAV-based sensor that collects field data on each flight (as shown in box 290)), or other types of field sensors specified by field sensor 226. In some examples, position, heading, or speed data from geolocation sensor 204 is georeferenced against data from airborne sensors.

[0075] The predictive model generator 210 controls the prior information variable to field variable model generator 228 to generate a model that models the relationship between the mapped values ​​contained in the prior information graph 258 and the field values ​​sensed by the field sensor 208, as shown in box 292. The features or data types represented by the mapped values ​​in the prior information graph 258 and the field values ​​sensed by the field sensor 208 can be the same features or data types or different features or data types.

[0076] The relation or model generated by the prediction model generator 210 is provided to the prediction map generator 212. The prediction map generator 212 uses the prediction model and the prior information map 258 to generate a prediction map 264, which predicts the values ​​of features at different geographical locations in the field being harvested, or the values ​​of different features related to the features sensed by the field sensor 208, as shown in box 294.

[0077] It should be noted that in some examples, the prior information map 258 may include two or more different maps or two or more different layers of a single map. Each layer may represent a data type different from that of another layer, or the layers may have the same data type acquired at different times. Each map in the two or more different maps or each layer in the two or more different layers of a map maps different types of variables to geographic locations in the field. In such an example, the predictive model generator 210 generates a predictive model that models the relationship between the field data and each of the different variables mapped by the two or more different maps or two or more different layers. Similarly, the field sensor 208 may include two or more sensors, each sensing a different type of variable. Therefore, the predictive model generator 210 generates a predictive model that models the relationship between each type of variable mapped by the prior information map 258 and each type of variable sensed by the field sensor 208. The prediction map generator 212 can use each of the graphs or layers in the prediction model and prior information graph 258 to generate a functional prediction map 263 that predicts the value of each sensed feature (or feature associated with the sensed feature) at different locations in the field being harvested, as sensed by the field sensor 208.

[0078] Prediction map generator 212 configures prediction map 264 such that prediction map 264 can be operated (or consumed) by control system 214. Prediction map generator 212 can provide prediction map 264 to control system 214 or to control area generator 213 or both. Some examples of different ways in which prediction map 264 can be configured or output are described with respect to boxes 296, 295, 299 and 297. For example, prediction map generator 212 configures prediction map 264 such that prediction map 264 includes values ​​that can be read by control system 214 and used as the basis for generating one or more control signals in different controllable subsystems of agricultural harvester 100, as shown in box 296.

[0079] Control zone generator 213 can divide prediction map 264 into control zones based on values ​​on prediction map 264. Values ​​of consecutive geographic locations within each other's thresholds can be grouped into control zones. Thresholds can be default thresholds, or thresholds can be set based on operator input, input from the automation system, or other criteria. Zone sizes can be based on the responsiveness of control system 214, controllable subsystem 216, wear considerations, or other criteria, as shown in box 295. Prediction map generator 212 configures prediction map 264 for presentation to operators or other users. Control zone generator 213 can configure prediction control zone map 265 for presentation to operators or other users. This is indicated by box 299. When presented to operators or other users, the presentation of prediction map 264 or prediction control zone map 265, or both, can include geographic location-related predicted values ​​on prediction map 264, geographic location-related control zones on prediction control zone map 265, and one or more of the setting values ​​or control parameters used based on the predicted values ​​on prediction map 264 or the zones on prediction control zone map 265. In another example, the presentation may include more abstract or more detailed information. The presentation may also include a confidence level indicating the accuracy of the predicted values ​​on prediction map 264 or the area on prediction control map 265 as measured by sensors on the harvester 100 moving across the field. Additionally, where information is presented to more than one location, an authentication / authorization system may be provided to implement the authentication and authorization process. For example, there may be a hierarchy of individuals authorized to view and modify the map and other presented information. As an example, the onboard display device may display the map locally only on the machine in near real-time, or the map may be generated at one or more remote locations. In some examples, each physical display device at each location may be associated with a person or user permission level. The user permission level may be used to determine which display markers are visible on the physical display devices and which values ​​the corresponding person can change. For example, the local operator of the harvester 100 may not be able to see the information corresponding to prediction map 264 or make any changes to the machine's operation. However, a supervisor at a remote location might be able to see Predictive Map 264 on a monitor but cannot make any changes. A manager at a separate remote location might be able to see all elements on Predictive Map 264 and also be able to modify it for machine control. This is an example of an authorization hierarchy that can be implemented. Predictive Map 264, or Predictive Control Area Map 265, or both, can also be configured in other ways, as shown in box 297.

[0080] In box 298, the control system receives input from geolocation sensor 204 and other field sensors 208. Box 300 indicates that the control system 214 receives input from geolocation sensor 204 identifying the geolocation of the harvester 100. Box 302 indicates that the control system 214 receives sensor input indicating the trajectory or heading of the harvester 100, and box 304 indicates that the control system 214 receives the speed of the harvester 100. Box 306 indicates that the control system 214 receives other information from various field sensors 208.

[0081] In block 308, control system 214 generates control signals to control controllable subsystem 216 based on prediction map 264 or prediction control area map 265 or both, and inputs from geographic location sensor 204 and any other field sensors 208. In block 310, control system 214 applies the control signals to the controllable subsystem. It should be understood that the specific control signals generated and the specific controllable subsystem 216 controlled can vary based on one or more different factors. For example, the generated control signals and the controllable subsystem 216 controlled can be based on the type of prediction map 264 or prediction control area map 265 or both being used. Similarly, the timing of the generated control signals, the controllable subsystem 216 controlled, and the control signals can be based on various delays in the crop flow through agricultural harvester 100 and the responsiveness of controllable subsystem 216.

[0082] As an example, the prediction map 264 generated in the form of a predicted crop moisture map can be used to control one or more controllable subsystems 216. For example, the functional predicted crop moisture map may include predicted values ​​of crop moisture at a location within a geographically referenced field being harvested. The functional predicted crop moisture map can be extracted and used to control the steering subsystem 252 and the propulsion subsystem 250, respectively. By controlling the steering subsystem 252 and the propulsion subsystem 250, the feed rate of material or grain moving through the agricultural harvester 100 can be controlled. Similarly, the header height can be controlled to collect more or less material, and therefore the header height can also be controlled to control the feed rate of material through the agricultural harvester 100. In other examples, header control can be achieved if the predicted crop moisture in front of the machine mapped on one part of the header by the prediction map 264 is higher than the predicted crop moisture in front of the machine mapped on another part of the header, resulting in a difference in biomass entering one side of the header compared to the other side. For example, the speed of the belt conveyor on one side of the header can be increased or decreased relative to the speed of the belt conveyor on the other side of the header to account for additional biomass. Therefore, the header and reel controller 238 can be controlled using georeferenced predictions present in the predicted crop moisture map to control the belt conveyor speed on the header. The examples for feed rate and header control using functional predicted crop moisture maps described above are provided by way of example only. Therefore, a variety of other control signals can be generated using predictions obtained from the predicted crop moisture map or other types of functional prediction maps 263 to control one or more of the controllable subsystems 216.

[0083] In box 312, it is determined whether the harvesting operation has been completed. If the harvesting is not completed, the process proceeds to box 314, where field sensor data from geolocation sensor 204 and field sensor 208 (and possibly other sensors) are read.

[0084] In some examples, at box 316, the agricultural harvester 100 may also detect learning trigger criteria to perform machine learning on one or more of the following: the prediction map 264, the prediction control region map 265, the model generated by the prediction model generator 210, the region generated by the control region generator 213, one or more control algorithms implemented by the controller in the control system 214, and other trigger learning.

[0085] The learning trigger criterion can include any of a variety of different criteria. Some examples of detecting trigger criters are discussed with respect to boxes 318, 320, 321, 322, and 324. For example, in some examples, triggering learning may include recreating the relationships used to generate the predictive model when a threshold amount of field sensor data is received from field sensor 208. In such examples, an amount of field sensor data received from field sensor 208 exceeding a threshold triggers or causes predictive model generator 210 to generate a new predictive model used by predictive map generator 212. Thus, as the agricultural harvester 100 continues its harvesting operation, receiving a threshold amount of field sensor data from field sensor 208 triggers the creation of a new relationship represented by the predictive model generated by predictive model generator 210. Furthermore, the new predictive model can be used to regenerate a new predictive map 264, a predictive control area map 265, or both. Box 318 represents detecting a threshold amount of field sensor data used to trigger the creation of a new predictive model.

[0086] In other examples, the learning trigger criterion can be based on the degree of change in field sensor data from field sensor 208, such as the degree of change over time or compared to previous values. For example, if the change within the field sensor data (or the relationship between the field sensor data and the information in the prior information map 258) is within a selected range, less than a defined amount, or below a threshold, a new predictive model is not generated by predictive model generator 210. As a result, predictive map generator 212 does not generate a new predictive map 264, predictive control area map 265, or both. However, for example, if the change within the field sensor data is outside the selected range, greater than a defined amount, or above a threshold, predictive model generator 210 uses all or part of the newly received field sensor data used by predictive map generator 212 to generate a new predictive map 264 to generate a new predictive model. In box 320, changes in the field sensor data (such as the magnitude of the amount by which the data exceeds a selected range or the magnitude of the change in the relationship between the field sensor data and the information in the prior information map 258) can be used as triggers to cause the generation of new predictive models and predictive maps. Continuing with the example described above, thresholds, ranges, and defined quantities can be set to default values, set by operators or users through user interface interaction, set by the automation system, or otherwise.

[0087] Other learning trigger criteria can also be used. For example, if the predictive model generator 210 switches to a different prior infographic (different from the initially selected prior infographic 258), the switch to a different prior infographic can trigger relearning by the predictive model generator 210, the predictive map generator 212, the control area generator 213, the control system 214, or others. In another example, the transition of the agricultural harvester 100 to different terrain or to a different control area can also be used as a learning trigger criterion.

[0088] In some cases, operator 260 can also edit the prediction graph 264 or the prediction control area graph 265, or both. Editing can change the values ​​on the prediction graph 264, change the size, shape, position, or presence of the control area on the prediction control area graph 265, or both. Box 321 shows that the edited information can be used as a learning trigger criterion.

[0089] In some cases, operator 260 may also observe that the automatic control of the controllable subsystem is not as the operator expects. In this case, operator 260 may provide manual adjustments to the controllable subsystem, reflecting operator 260's expectation that the controllable subsystem will operate in a manner different from that commanded by control system 214. Therefore, manual changes to the settings by operator 260 may result in one or more of the following: based on operator 260's adjustments (as shown in box 322), predictive model generator 210 relearns the model, predictive graph generator 212 regenerates graph 264, control area generator 213 regenerates one or more control areas on predictive control area graph 265, and control system 214 relearns the control algorithm or performs machine learning on one or more of the controller components 232 to 246 in control system 214. Box 324 indicates the use of other triggering learning criteria.

[0090] In other examples, relearning can be performed periodically or intermittently, for example based on a chosen time interval, such as a discrete time interval or a variable time interval, as shown in box 326.

[0091] If relearning is triggered (whether based on a learning trigger criterion or on the elapsed time interval, as shown in box 326), one or more of the predictive model generator 210, predictive graph generator 212, control region generator 213, and control system 214 perform machine learning to generate a new predictive model, a new predictive graph, a new control region, and a new control algorithm, respectively, based on the learning trigger criterion. The new predictive model, new predictive graph, and new control algorithm are generated using any additional data collected since the last learning operation. The execution of relearning is indicated by box 328.

[0092] If the harvesting operation is complete, the operation moves from box 312 to box 330, where one or more of the prediction map 264, prediction control area map 265, and prediction model generated by prediction model generator 210 are stored. Prediction map 264, prediction control area map 265, and prediction model can be stored locally on data storage device 202 or sent to a remote system for later use using communication system 206.

[0093] It will be noted that while some examples in this paper describe the predictive model generator 210 and the predictive graph generator 212 receiving prior information graphs when generating the predictive model and the functional prediction graph, respectively, in other examples, the predictive model generator 210 and the predictive graph generator 212 may receive other types of graphs, including prediction graphs, such as functional prediction graphs generated during the harvesting operation, when generating the predictive model and the functional prediction graph.

[0094] Figure 4 yes Figure 1 A block diagram of a portion of an agricultural harvester 100 is shown. Specifically, Figure 4 Examples of the prediction model generator 210 and the prediction graph generator 212 are shown in more detail. Figure 4The diagram also illustrates the information flow between the different components shown. As shown, the prediction model generator 210 receives one or more of the following as prior information maps: vegetation index map 332, historical crop moisture map 333, topographic map 341, or soil property map 343, or prior operation map 400. The historical crop moisture map 333 includes historical crop moisture values ​​335 indicating crop moisture values ​​in the field during past harvests. The historical crop moisture map 333 also includes contextual data 337 indicating situations or conditions that may have affected crop moisture values ​​in the past(one or more) years. For example, contextual data 337 may include soil characteristics such as soil type, soil moisture, soil cover or soil structure, topographic features such as elevation or slope, planting date, harvest date, fertilizer application, seed type (hybrid, etc.), the amount of weeds present, the amount of pests present, weather conditions such as rainfall, snow cover, hail, wind, temperature, etc. The historical crop moisture map 333 may also include other items, as indicated by box 339. As shown in the example, vegetation index map 332, topographic map 341, and soil property map 343 do not include additional information. However, in other examples, vegetation index map 332, topographic map 341, soil property map 343, and prior operation map 400 may also include other items. As an example, weed growth affects vegetation index readings. Therefore, herbicide application in the temporal relationship of vegetation index sensing used to generate vegetation index map 332 can be contextual information included in vegetation index map 332 to provide context for vegetation index values. Vegetation index map 332, topographic map 341, or soil property map 343 may also include various other types of information.

[0095] In addition to receiving one or more of the following as prior information maps: vegetation index map 332, historical crop moisture map 333, topographic map 341, or soil property map 343, or prior operation map 400, the prediction model generator 210 also receives a geographic location 334 or a geographic location indication from the geographic location sensor 204. The field sensor 208 schematically includes a crop moisture sensor 336 and a processing system 338. The processing system 338 processes the sensor data generated from the crop moisture sensor 336. In some examples, the crop moisture sensor 336 may be located on the agricultural harvester 100.

[0096] In some examples, crop moisture sensor 336 may include a capacitive moisture sensor. In one example, the capacitive moisture sensor may include a moisture measuring unit for containing a sample of crop material and a capacitor for determining the dielectric properties of the sample. In other examples, the crop moisture sensor may be a microwave sensor or a conductivity sensor. In other examples, the crop moisture sensor may utilize the wavelength of electromagnetic radiation to sense the moisture content of the crop material. The crop moisture sensor may be disposed within the feed chamber 106 (or otherwise have a sensing path to the crop material within the feed chamber 106) and configured to sense the moisture of the harvested crop material passing through the feed chamber 106. In other examples, the crop moisture sensor may be located in other areas within the agricultural harvester 100, for example, in a clean grain lift, in a clean grain auger, or in a grain tank. It should be noted that these are merely examples of crop moisture sensors, and various other crop moisture sensors are contemplated. Processing system 338 processes one or more sensor signals generated by crop moisture sensor 336 to generate processed sensor data identifying one or more crop moisture values. The processing system 338 can also geolocate values ​​received from the field sensors 208. For example, the location of the harvester when it receives a signal from the field sensors 208 may not be the precise location of the crop moisture. This is because a certain amount of time has passed between the time when the harvester initially contacts the crop plant and the time when the crop plant material is sensed by the crop moisture sensor 336 or other field sensors 208. Therefore, the transient time between the initial contact with the plant and the time when the crop material is sensed within the harvester is taken into account when geolocating the sensed data. By doing so, the crop moisture value can be geolocated to a precise location in the field. Since the cut crop travels along the header in a direction transverse to the harvester's direction of travel, the crop moisture value is typically geolocated to a V-shaped area behind the harvester as the harvester travels in the forward direction.

[0097] Processing system 338 allocates or assigns the total crop moisture detected by the crop moisture sensor back to an earlier geographic reference area (e.g., along the width of the harvester's header and different lateral positions of the harvester's ground speed) based on the travel time of the crop from different parts of the agricultural harvester during each measurement interval or time. For example, processing system 338 allocates the measured total crop moisture back to a geographic reference area traversed by the harvester's header during different measurement intervals or times. Processing system 338 allocates or assigns the total crop moisture from a specific measurement interval or time to a previously traversed geographic reference area, which is part of a V-shaped region.

[0098] In some examples, the crop moisture sensor 336 may rely on different types of radiation and the manner in which the radiation is reflected, absorbed, attenuated, or transmitted through the crop material. The crop moisture sensor 336 may sense other electromagnetic properties, such as dielectric constant, as the crop material passes between two capacitor plates. Other material properties and sensors may also be used. In some examples, raw or processed data from the crop moisture sensor 336 may be presented to the operator 260 via an operator interface mechanism 218. The operator 260 may be on the agricultural harvester 100 or at a remote location.

[0099] This discussion focuses on an example where crop humidity sensor 336 detects a value indicating crop humidity. It should be understood that this is merely an example, and other sensors mentioned above are also considered herein as examples of crop humidity sensor 336. Figure 4 As shown, the prediction model generator 210 includes a vegetation index to crop moisture model generator 342, a historical crop moisture to crop moisture model generator 344, a soil property to crop moisture model generator 345, a terrain feature to crop moisture model generator 346, and a prior operation to crop moisture model generator 348. In other examples, compared to Figure 4 The predictive model generator 210 may include additional, fewer, or different components than those shown in the examples. Therefore, in some examples, the predictive model generator 210 may also include other items 349, which may include other types of predictive model generators to generate other types of crop moisture models. For example, other model generators 349 may include specific features, such as specific vegetation index features, such as crop growth or crop health; specific soil property features, such as soil type, soil moisture, soil cover, or soil structure; or specific terrain features, such as slope or altitude.

[0100] The vegetation index-to-crop moisture model generator 342 identifies the relationship between the field crop moisture data 340 at the geographic location corresponding to the field crop moisture data 340 and the vegetation index values ​​at the same location in the field corresponding to the field crop moisture data 340 from the vegetation index map 332. Based on this relationship established by the vegetation index-to-crop moisture model generator 342, the vegetation index-to-crop moisture model generator 342 generates a predictive crop moisture model. The predictive crop moisture model is used by the prediction map generator 212 to predict crop moisture at different locations in the field based on the georeferenced vegetation index values ​​in the vegetation index map 332 contained at the same location in the field.

[0101] The historical crop humidity to crop humidity model generator 344 identifies the relationship between crop humidity at a geographic location corresponding to the geolocation of the on-site crop humidity data 340, as represented in the on-site crop humidity data 340, and historical crop humidity at the same location (or at a location in the historical crop humidity map 333 with contextual data 337 similar to the current region or year). The historical crop humidity map 335 contains georeferenced and context-referenced values ​​included in the historical crop humidity map. The historical crop humidity to crop humidity model generator 344 then generates a predicted crop humidity model, which the map generator 212 uses to predict crop humidity at a location in the field based on the historical crop humidity values ​​335.

[0102] The soil property to crop moisture model generator 345 identifies the relationship between the field crop moisture data 340 at a geographic location corresponding to the field crop moisture data 340 and the soil property values ​​from the soil property map 343 corresponding to the same geographic location of the field crop moisture data 340 in the field. Based on this relationship established by the soil property to crop moisture model generator 345, the soil property to crop moisture model generator 345 generates a predicted crop moisture model. The predicted crop moisture model is used by the prediction map generator 212 to predict crop moisture at different locations in the field based on the georeferenced soil property values ​​in the soil property map 343 contained at the same location in the field.

[0103] The topographic feature to crop moisture model generator 346 identifies the relationship between the field crop moisture data 340 at a geographic location corresponding to the geolocation of the field crop moisture data 340 and the topographic feature values ​​from topographic map 341 corresponding to the geolocation of the field crop moisture data 340 in the field. Based on this relationship established by the topographic feature to crop moisture model generator 346, the topographic feature to crop moisture model generator 346 generates a predicted crop moisture model. The predicted crop moisture model is used by the prediction map generator 212 to predict crop moisture at different locations in the field based on the georeferenced topographic feature values ​​in topographic map 341 contained at the same location in the field.

[0104] The prior operation-to-crop humidity model generator 348 identifies the relationship between the field crop humidity data 340 at a geographic location corresponding to the field crop humidity data 340 being geolocated, and the prior operation feature values ​​from the prior operation map 400 corresponding to the same geographic location of the field crop humidity 340 in the field. Based on this relationship established by the prior operation-to-crop humidity model generator 348, the prior operation-to-crop humidity model generator 348 generates a predicted crop humidity model. The predicted crop humidity model is used by the prediction map generator 212 to predict crop humidity at different locations in the field based on the georeferenced prior operation feature values ​​in the prior operation map 400 contained at the same location in the field.

[0105] In light of the above, the prediction model generator 210 is operable to generate multiple prediction crop moisture models, such as one or more of the prediction crop moisture models generated by model generators 342, 344, 345, 346, 348, and 349. In another example, two or more of the prediction crop moisture models described above can be combined into a single prediction crop moisture model, which predicts crop moisture based on vegetation index values ​​at different locations in the field, historical crop moisture values, soil property values, topographic feature values, or prior operational feature values, or both. Any one of these crop moisture models or combinations thereof in Figure 4 The data is uniformly represented by the crop humidity model 350.

[0106] The crop moisture prediction model 350 is provided to the prediction map generator 212. Figure 4 In one example, the prediction map generator 212 includes a crop moisture map generator 352. In other examples, the prediction map generator 212 may include additional, fewer, or different map generators. The crop moisture map generator 352 receives a prediction crop moisture model 350, which predicts crop moisture based on field data 340 and one or more of a vegetation index map 332, a historical crop moisture map 333, a topographic map 341, or a soil property map 343.

[0107] The crop moisture map generator 352 can generate a functional predictive crop moisture map 360, which predicts crop moisture at different locations in the field based on one or more of the following: vegetation index values, historical crop moisture values, topographic feature values, or soil property values, and a predictive crop moisture model 350. The generated functional predictive crop moisture map 360 (with or without control zones) can be provided to a control zone generator 213, a control system 214, or both. The control zone generator 213 generates control zones and merges those control zones into the functional predictive map (i.e., prediction map 360) to produce a predictive control zone map 265. One or both of the functional predictive map 264 or the predictive control zone map 265 can be presented to an operator 260 or other user or provided to the control system 214, which generates control signals to control one or more of the controllable subsystems 216 based on the prediction map 264, the predictive control zone map 265, or both.

[0108] Figure 5 This is a flowchart illustrating an example of the operation of the prediction model generator 210 and the prediction map generator 212 in generating a prediction crop moisture model 350 and a functional prediction crop moisture map 360. In block 362, the prediction model generator 210 and the prediction map generator 212 receive one or more previous vegetation index maps 332, one or more historical crop moisture maps 333, one or more previous topographic maps 341, one or more soil property maps 434, or one or more prior operation maps 400, or a combination thereof. Also in block 362, field sensor signals are received from field sensors, such as crop moisture sensor signals from crop moisture sensor 336.

[0109] In box 363, the prior infographic selector 209 selects one or more specific prior infographics 250 for use by the predictive model generator 210. In one example, the prior infographic selector 209 selects one infographic from multiple candidate infographics based on a comparison of contextual information in the candidate infographics with current contextual information. For example, a candidate historical crop humidity infographic can be selected from a previous year whose growing season weather conditions were similar to those of the current year. Alternatively, for example, even if the current year has average or above-average precipitation levels, a candidate historical crop humidity infographic can be selected from a previous year with low average precipitation levels, as historical crop humidity infographics associated with previous years with low average precipitation, as discussed above, may still have a useful relationship between historical crop humidity and crop humidity. In some examples, the prior infographic selector 209 may change the prior infographic being used when it detects that one of the other candidate prior infographics is more closely related to the field-sensitized crop humidity.

[0110] In block 372, processing system 338 processes one or more received sensor signals (e.g., one or more sensor signals received from field sensor 208, or crop moisture sensor 336) to generate a crop moisture value indicating the moisture content of the harvested crop material.

[0111] In box 382, ​​the predictive model generator 210 also obtains the geographic location corresponding to the sensor signal. For example, the predictive model generator 210 can obtain the geographic location from the geographic location sensor 204 and determine the precise geographic location to which the sensed crop moisture in the field belongs based on machine latency (e.g., machine processing speed) and machine speed. For example, the exact time at which the crop moisture sensor signal is captured may not correspond to the time when the crop is cut from the ground. Therefore, the location of the harvester 100 at the time the crop moisture sensor signal is acquired may not correspond to the location where the crop was planted. Instead, the current field crop moisture sensor signal corresponds to the location in the field behind the harvester 100 because a certain amount of time has passed between the initial contact between the crop and the harvester and when the crop reaches the crop moisture sensor 336.

[0112] In box 384, prediction model generator 210 generates one or more prediction crop moisture models, such as crop moisture model 350, which model the relationship between at least one of vegetation index values, historical mapped moisture values, topographic feature values, or soil property values ​​obtained from a prior information map (such as prior information map 258), and the crop moisture being sensed by field sensor 208. For example, prediction model generator 210 may generate prediction crop moisture models based on vegetation index values, historical crop moisture values, topographic feature values, or soil property values, and the sensed crop moisture indicated by sensor signals obtained from field sensor 208.

[0113] In box 386, a predicted crop moisture model (such as predicted crop moisture model 350) is provided to a prediction map generator 212, which generates a functional predicted crop moisture map based on a vegetation index map, a historical crop moisture map 333, a topographic map 341, a soil property map 343, and the predicted crop moisture model 350. This functional predicted crop moisture map maps the predicted crop moisture to different geographic locations in the field. For example, in some examples, the functional predicted crop moisture map 360 predicts crop moisture. In other examples, the functional predicted crop moisture map 360 predicts other items. Furthermore, the functional predicted crop moisture map 360 can be generated during the agricultural harvesting operation. Therefore, the functional predicted crop moisture map 360 is generated as an agricultural harvester moves through the field to perform the agricultural harvesting operation.

[0114] In block 394, the prediction map generator 212 outputs a functional predicted crop humidity map 360. In block 393, the prediction map generator 212 configures the functional predicted crop humidity map 360 for use by the control system 214. In block 395, the prediction map generator 212 can also provide map 360 to the control zone generator 213 for the generation and merging of control zones. In block 397, the prediction map generator 212 also configures map 360 in other ways. The functional predicted crop humidity map 360 (with or without control zones) is provided to the control system 214. In block 396, the control system 214 generates control signals based on the functional predicted crop humidity map 360 (with or without control zones) to control the controllable subsystem 216.

[0115] Control system 214 can generate control signals to control one or more headers or other machine actuators 248, such as the position or spacing of a control panel. Control system 214 can generate control signals to control propulsion subsystem 250. Control system 214 can generate control signals to control steering subsystem 252. Control system 214 can generate control signals to control stubble subsystem 138. Control system 214 can generate control signals to control machine cleaning subsystem 254. Control system 214 can generate control signals to control thresher 110. Control system 214 can generate control signals to control material handling subsystem 125. Control system 214 can generate control signals to control crop cleaning subsystem 118. Control system 214 can generate control signals to control communication system 206. Control system 214 can generate control signals to control operator interface mechanism 218. Control system 214 can generate control signals to control various other controllable subsystems 256.

[0116] In an example where the control system 214 receives a function prediction diagram or has added a control area, the header / reel controller 238 controls the header or other machine actuator 248 to control the height, tilt, or roll of the header 102. In another example where the control system 214 receives a function prediction diagram or has added a control area, the feed rate controller 236 controls the propulsion subsystem 250 to control the travel speed of the harvester 100. In another example where the control system 214 receives a function prediction diagram or has added a control area, the path planning controller 234 controls the steering subsystem 252 to steer the harvester 100. In yet another example where the control system 214 receives a function prediction diagram or has added a control area, the stubble system controller 244 controls the stubble subsystem 138. In yet another example where the control system 214 receives a function prediction diagram or has added a control area, the setting controller 232 controls the threshing settings of the threshing machine 110. In another example where the control system 214 receives a function prediction diagram or a function prediction diagram with added control areas, controller 232 or another controller 246 controls the material handling subsystem 125. In another example where the control system 214 receives a function prediction diagram or a function prediction diagram with added control areas, controller 232 controls the crop cleaning subsystem 118. In another example where the control system 214 receives a function prediction diagram or a function prediction diagram with added control areas, machine cleaning controller 245 controls the machine cleaning subsystem 254 on the harvester 100. In another example where the control system 214 receives a function prediction diagram or a function prediction diagram with added control areas, communication system controller 229 controls the communication system 206. In another example where the control system 214 receives a function prediction diagram or a function prediction diagram with added control areas, operator interface controller 231 controls the operator interface mechanism 218 on the harvester 100. In another example where the control system 214 receives a function prediction diagram or a function prediction diagram with added control areas, platform position controller 242 controls the machine / crown actuator 248 to control the platform on the harvester 100. When control system 214 receives a function prediction map or a function prediction map with added control areas, belt conveyor controller 240 controls machine / header actuator 248 to control the belt conveyor belt on agricultural harvester 100. In another example where control system 214 receives a function prediction map or a function prediction map with added control areas, other controllers 246 control other controllable subsystems 256 on agricultural harvester 100.

[0117] This demonstrates that the system employs a priori information maps features such as vegetation index values, historical crop moisture values, topographic features, or soil properties to different locations in the field. The system also utilizes one or more field sensors that sense indicative features (e.g., crop moisture) and generate a model that models the relationship between the crop moisture sensed in the field and the features mapped onto the priori information map. Therefore, the system uses the model and the priori information map to generate a functional prediction map, which can be configured for use by the control system or presented to local or remote operators or other users. For example, the control system can use the map to control one or more systems of a combine harvester.

[0118] Figure 6A yes Figure 1 A block diagram of an example portion of an agricultural harvester 100 is shown. Specifically, Figure 6A Examples of the prediction model generator 210 and the prediction map generator 212 are shown in particular. In the illustrated example, the prior information map 258 is the prior operation map 400. The prior operation map 400 may include crop moisture maps at different locations in the field from prior operations on the field. Figure 6A It is also shown that the prediction model generator 210 and the prediction map generator can alternatively or in addition to the prior information map 258 receive a prediction crop moisture map, such as a functional prediction crop moisture map 360. The functional prediction crop moisture map 360 can be used similarly to the prior information map 258 because the model generator 210 models the relationship between the information provided by the functional prediction crop moisture map 360 and the features sensed by the field sensor 208. Therefore, the map generator 212 can use this model to generate a functional prediction map that predicts based on one or more values ​​in the functional prediction crop moisture map at different locations in the field and predicts features sensed by the field sensor 208 at those locations in the field, or features indicating the sensed features, based on the prediction model. Figure 6A As shown, the prediction model generator 210 and prediction map generator 212 can also receive other maps 401, such as other prior information maps or other prediction maps, for example, other prediction crop humidity maps generated in a different manner than the functional prediction crop humidity map 360. In another example, other maps 401 may include historical crop humidity maps, such as historical crop humidity map 333 or another historical crop humidity map generated during the harvesting operation of the previous season, which includes historical values ​​of crop humidity corresponding to different locations in the field.

[0119] Moreover, in Figure 6AIn the example shown, the field sensor 208 may include one or more of the following: agricultural feature sensor 402, operator input sensor 404, and processing system 406. The field sensor 208 may also include other sensors 408.

[0120] Agricultural feature sensor 402 senses values ​​indicating agricultural features. Operator input sensor 404 senses various operator inputs. Inputs may be setting inputs or other control inputs for controlling settings on the agricultural harvester 100, such as steering inputs and other inputs. Thus, when operator 260 changes settings or provides command inputs through operator interface mechanism 218, such as inputs detected by operator input sensor 404, the operator input sensor provides a sensor signal indicating the sensed operator input.

[0121] The processing system 406 can receive sensor signals from one or more of the agricultural feature sensor 402 and the operator input sensor 404, and generate an output indicating the sensed features. For example, the processing system 406 can receive sensor input from the agricultural feature sensor 402 and generate an output indicating agricultural features. The processing system 406 can also receive input from the operator input sensor 404 and generate an output indicating the sensed operator input.

[0122] Predictive model generator 210 may include a crop moisture to agricultural feature model generator 410 and a crop moisture to command model generator 414. In other examples, predictive model generator 210 may include additional, fewer, or other model generators 415. For example, predictive model generator 210 may include specific agricultural feature or specific operator command model generators, such as model generators using specific agricultural features (e.g., biomass or biomass characteristics, yield or yield characteristics, and various other agricultural features) or model generators using specific operator commands (e.g., header height settings, speed settings, threshing rotor settings, and various other machine settings). Predictive model generator 210 may receive geographic location 334 or geographic location indication from geographic location sensor 204 and generate predictive model 426 that models the relationship between information in one or more of the prior information graphs 258 or information in the functional predictive crop moisture graph 360 and one or more of the following items: agricultural features sensed by agricultural feature sensor 402 and operator input commands sensed by operator input sensor 404.

[0123] The crop humidity-to-agricultural feature model generator 410 generates a model that models the relationship between crop humidity values ​​(which can be found on the predicted crop humidity map 360, the prior operation map 400, or other map 401) and agricultural features sensed by the agricultural feature sensor 402. The crop humidity-to-agricultural feature model generator 410 generates a prediction model 426 corresponding to this relationship.

[0124] The crop humidity-to-operator command model generator 414 generates a model that models the relationship between crop humidity values ​​reflected in the predicted crop humidity map 360, the prior operation map 400, or other map 401, and operator input commands sensed by the operator input sensor 404. The crop humidity-to-operator command model generator 414 generates a prediction model 426 corresponding to this relationship.

[0125] Other model generators 415 may include, for example, model generators for specific agricultural features or specific operator commands, such as model generators using specific agricultural features (e.g., biomass or biomass characteristics, yield or yield characteristics, and various other agricultural features), or model generators using specific operator commands (e.g., header height settings, speed settings, threshing rotor settings, and various other machine settings).

[0126] The prediction model 426 generated by the prediction model generator 210 may include one or more prediction models, which may be generated by the crop humidity to agricultural feature model generator 410 and the crop humidity to operator command model generator 414, as well as other model generators that may be included as part of other items 415.

[0127] exist Figure 6A In one example, the prediction graph generator 212 includes a prediction agricultural feature graph generator 416 and a prediction operator command graph generator 422. In other examples, the prediction graph generator 212 may include additional, fewer, or other graph generators 434.

[0128] The predictive agricultural feature map generator 416 receives a predictive model 426 (such as a predictive model generated by the crop humidity to agricultural feature model generator 410) that models the relationship between crop humidity and agricultural features sensed by the agricultural feature sensor 402, as well as one or more of a prior information map 258, a functional predictive crop humidity map 360, or other maps 401. Based on one or more crop humidity values ​​from the prior information map 258, the functional predictive crop humidity map 360, or one or more of other maps 401 at different locations in the field, and based on the predictive model 426, the predictive agricultural feature map generator 416 generates a functional predictive agricultural feature map 427 that predicts the agricultural feature values ​​(or values ​​indicating agricultural features) at those locations in the field.

[0129] The predictive operator command graph generator 422 receives a predictive model 426 (e.g., a predictive model generated by the crop humidity to command model generator 414) that models the relationship between crop humidity and operator command input detected by the operator input sensor 404, as well as one or more of a prior information graph 258, a functional predictive crop humidity graph 360, or other graphs 401. Based on crop humidity values ​​from the prior information graph 258, the functional predictive crop humidity graph 360, or other graphs at different locations in the field, and based on the predictive model 426, the predictive operator command graph 440 predicts operator command input at those locations in the field.

[0130] Prediction graph generator 212 outputs one or more of functional prediction graphs 427 and 440. Each of functional prediction graphs 427 and 440 can be provided to control area generator 213, control system 214, or both. Control area generator 213 generates and combines control areas to provide a control area to functional prediction graph 427 or to functional prediction graph 440, or both. Any or all of functional prediction graphs 427 and 440 (with or without control areas) can be provided to control system 214, which generates control signals based on one or all of functional prediction graphs 427 and 440 (with or without control areas) to control one or more controllable subsystems 216. Any or all of graphs 427 and 440 (with or without control areas) can be presented to operator 260 or another user.

[0131] Figure 6B This is a block diagram showing some examples of the real-time (field) sensor 208. Figure 6B Some of the sensors shown, or different combinations thereof, may simultaneously have sensor 402 and processing system 406, while others may act as... Figure 6A and 7 The sensor 402 described is in Figure 6A and 7 The processing system 406 is separate. Figure 6B Some of the possible field sensors 208 shown are illustrated and described relative to the previous figures and are similarly numbered. Figure 6BThe field sensors 208 shown may include an operator input sensor 480, a machine sensor 482, a harvested material property sensor 484, a field and soil property sensor 485, an environmental characteristic sensor 487, and may include a variety of other sensors 226. The operator input sensor 480 may be a sensor that senses operator input via an operator interface mechanism 218. Therefore, the operator input sensor 480 can sense user movements of linkages, joysticks, steering wheels, buttons, dials, or pedals. The operator input sensor 480 can also sense user interactions with other operator input structures, such as interactions with a touchscreen, a microphone utilizing voice recognition, or any of the various other operator input mechanisms.

[0132] Machine sensor 482 can sense various features of the agricultural harvester 100. For example, as discussed above, machine sensor 482 may include machine speed sensor 146, separator loss sensor 148, grain cleaning camera 150, forward-view image capture mechanism 151, loss sensor 152, or geolocation sensor 204, examples of which are described above. Machine sensor 482 may also include machine setting sensor 491 that senses machine settings. (See above references) Figure 1Examples of machine setups are described. A front-end equipment (e.g., header) position sensor 493 can sense the position of the header 102, reel 164, cutter 104, or other front-end equipment relative to the frame of the harvester 100. For example, sensor 493 can sense the height of the header 102 above the ground. Machine sensor 482 may also include a front-end equipment (e.g., header) orientation sensor 495. Sensor 495 can sense the orientation of the header 102 relative to the harvester 100 or relative to the ground. Machine sensor 482 may include a stability sensor 497. Stability sensor 497 senses vibrational or bouncing motions (and amplitude) of the harvester 100. Machine sensor 482 may also include a stubble setup sensor 499 configured to sense whether the harvester 100 is configured to chop stubble, produce a stockpile, or otherwise process stubble. Machine sensor 482 may include a cleaning chamber fan speed sensor 551 that senses the speed of the cleaning fan 120. Machine sensor 482 may include a recess gap sensor 553 for sensing the gap between the rotor 112 and the recess 114 on the harvester 100. Machine sensor 482 may include a husk sieve gap sensor 555 for sensing the size of the openings in the husk sieve 122. Machine sensor 482 may include a threshing rotor speed sensor 557 for sensing the rotor speed of the rotor 112. Machine sensor 482 may include a rotor pressure sensor 559 for sensing the pressure used to drive the rotor 112. Machine sensor 482 may include a sieve gap sensor 561 for sensing the size of the openings in the sieve 124. Machine sensor 482 may include a MOG humidity sensor 563 for sensing the humidity level of the MOG passing through the harvester 100. Machine sensor 482 may include a machine orientation sensor 565 for sensing the orientation of the harvester 100. Machine sensor 482 may include a material feed rate sensor 567 for sensing the feed rate of material as it travels through the feed chamber 106, the clean grain elevator 130, or other locations within the harvester 100. Machine sensor 482 may include a biomass sensor 569 that senses biomass traveling through feed chamber 106, separator 116, or other locations within the harvester 100. Machine sensor 482 may include a fuel consumption sensor 571 that senses the rate of fuel consumption of the harvester 100 over time. Machine sensor 482 may include a power utilization sensor 573 that senses power utilization in the harvester 100 (such as which subsystems are using power), or the rate at which subsystems are using power, or the distribution of power among subsystems within the harvester 100. Machine sensor 482 may include a tire pressure sensor 577 that senses the inflation pressure in the tires 144 of the harvester 100. Machine sensor 482 may include a variety of other machine performance sensors or machine characteristic sensors (as shown in box 575).The machine performance sensor and machine characteristic sensor 575 can sense the machine performance or characteristics of the agricultural harvester 100.

[0133] While crop material is being processed by the agricultural harvester 100, the harvest material characteristic sensor 484 can sense characteristics of the cut crop material. Crop characteristics may include things such as crop type, crop moisture content, grain quality (e.g., broken grain), MOG level, grain composition (e.g., starch and protein), MOG moisture content, and other crop material properties. Other sensors can sense straw “toughness,” corn and ear adhesion, and other characteristics that can be beneficially used to control processing for better grain capture, reduced grain damage, lower power consumption, reduced grain loss, etc.

[0134] The field and soil properties sensor 485 can sense the characteristics of fields and soils. Field and soil properties may include soil moisture, soil density, presence and location of water accumulation, soil type, and other soil and field characteristics.

[0135] The environmental feature sensor 487 can sense one or more environmental features. Environmental features may include things such as wind direction and speed, precipitation, fog, dust level or other obfuscation or other environmental characteristics.

[0136] Figure 7 A flowchart illustrating an example of the operation of the predictive model generator 210 and the predictive map generator 212 in generating one or more predictive models 426 and one or more functional predictive maps 427 and 440 is shown. In box 442, the predictive model generator 210 and the predictive map generator 212 receive maps. The maps received by the predictive model generator 210 or the predictive map generator in generating one or more predictive models 426 and one or more functional predictive maps 427 and 440 may be a prior information map 258, such as a prior operation map 400 created using data obtained during prior operations in the field. The maps received by the predictive model generator 210 or the predictive map generator in generating one or more predictive models 426 and one or more functional predictive maps 427 and 440 may be a functional predictive crop moisture map 360. Other maps may be received and indicated by box 401, such as other prior information maps or other predictive maps, such as other predictive crop moisture maps.

[0137] In box 444, the prediction model generator 210 receives sensor signals containing sensor data from field sensors 208. The field sensors may be one or more of agricultural feature sensors 402 and operator input sensors 404. Agricultural feature sensors 402 sense agricultural features. Operator input sensors 404 sense operator input commands. The prediction model generator 210 may also receive other field sensor inputs, such as those indicated by box 408.

[0138] In block 454, processing system 406 processes data contained in one or more sensor signals received from one or more field sensors 208 to obtain processed data 409, such as... Figure 6A As shown. Data contained in one or more sensor signals may be in its raw format, which is processed to receive processed data 409. For example, a temperature sensor signal includes resistance data, which can be processed into temperature data. In other examples, processing may include digitizing, encoding, formatting, scaling, filtering, or classifying the data. The processed data 409 may indicate one or more of agricultural features or operator input commands. The processed data 409 is provided to the predictive model generator 210.

[0139] Back Figure 7 In box 456, the prediction model generator 210 also receives geographic location 334 from the geographic location sensor 204, such as Figure 6A As shown. Geographic location 334 can be associated with the geographic location obtained from the sensed variables sensed by the field sensor 208. For example, the predictive model generator 210 can obtain geographic location 334 from geographic location sensor 204 and determine the precise geographic location from which the processed data 409 is derived based on machine latency, machine speed, etc.

[0140] In box 458, prediction model generator 210 generates one or more prediction models 426 that model the relationship between the mapped values ​​in the received graph and the features represented in the processed data 409. For example, in some cases, the mapped values ​​in the received graph may be crop moisture values, and prediction model generator 210 uses the mapped values ​​from the received graph and features sensed by field sensors 208 (as represented in the processed data 409) or related features (such as features associated with features sensed by field sensors 208) to generate the prediction model.

[0141] One or more prediction models 426 are provided to the prediction map generator 212. In block 466, the prediction map generator 212 generates one or more functional prediction maps. The functional prediction maps can be a functional prediction agricultural feature map 427 and a functional prediction operator command map 440, or any combination of these maps. The functional prediction agricultural feature map 427 predicts agricultural feature values ​​(or agricultural features indicated by said values) at different locations in the field. The functional prediction operator command map 440 predicts expected or possible operator command inputs at different locations in the field. Furthermore, one or more of the functional prediction maps 427 and 440 can be generated during the agricultural operation process. Thus, as the agricultural harvester 100 moves through the field to perform agricultural operations, one or more prediction maps 427 and 440 are generated along with the performance of the agricultural operation.

[0142] In block 468, the prediction graph generator 212 outputs one or more functional prediction graphs 427 and 440. In block 470, the prediction graph generator 212 can configure the graphs to be presented to operator 260 or another user, and to be interacted with by operator 260 or another user. In block 472, the prediction graph generator 212 can configure the graphs for use by the control system 214. In block 474, the prediction graph generator 212 can provide one or more prediction graphs 427 and 440 to the control area generator 213 to generate and merge control areas. In block 476, the prediction graph generator 212 configures one or more prediction graphs 427 and 440 in other ways. One or more functional prediction graphs 427 (with or without control areas) and 440 (with or without control areas) can be presented to operator 260 or another user, or also provided to the control system 214.

[0143] In block 478, control system 214 then generates control signals to control the controllable subsystem based on one or more functional prediction maps 427 and 440 (or functional prediction maps 427 and 440 with control areas) and inputs from geolocation sensor 204.

[0144] Control system 214 can generate control signals to control one or more headers or other machine actuators 248, such as the position or spacing of a control panel. Control system 214 can generate control signals to control propulsion subsystem 250. Control system 214 can generate control signals to control steering subsystem 252. Control system 214 can generate control signals to control stubble subsystem 138. Control system 214 can generate control signals to control machine cleaning subsystem 254. Control system 214 can generate control signals to control thresher 110. Control system 214 can generate control signals to control material handling subsystem 125. Control system 214 can generate control signals to control crop cleaning subsystem 118. Control system 214 can generate control signals to control communication system 206. Control system 214 can generate control signals to control operator interface mechanism 218. Control system 214 can generate control signals to control various other controllable subsystems 256.

[0145] In an example where the control system 214 receives a function prediction diagram or has added a control area, the header / reel controller 238 controls the header or other machine actuator 248 to control the height, tilt, or roll of the header 102. In another example where the control system 214 receives a function prediction diagram or has added a control area, the feed rate controller 236 controls the propulsion subsystem 250 to control the travel speed of the harvester 100. In another example where the control system 214 receives a function prediction diagram or has added a control area, the path planning controller 234 controls the steering subsystem 252 to steer the harvester 100. In yet another example where the control system 214 receives a function prediction diagram or has added a control area, the stubble system controller 244 controls the stubble subsystem 138. In yet another example where the control system 214 receives a function prediction diagram or has added a control area, the setting controller 232 controls the threshing settings of the threshing machine 110. In another example where the control system 214 receives a function prediction diagram or a function prediction diagram with added control areas, controller 232 or another controller 246 controls the material handling subsystem 125. In another example where the control system 214 receives a function prediction diagram or a function prediction diagram with added control areas, controller 232 controls the crop cleaning subsystem 118. In another example where the control system 214 receives a function prediction diagram or a function prediction diagram with added control areas, machine cleaning controller 245 controls the machine cleaning subsystem 254 on the harvester 100. In another example where the control system 214 receives a function prediction diagram or a function prediction diagram with added control areas, communication system controller 229 controls the communication system 206. In another example where the control system 214 receives a function prediction diagram or a function prediction diagram with added control areas, operator interface controller 231 controls the operator interface mechanism 218 on the harvester 100. In another example where the control system 214 receives a function prediction diagram or a function prediction diagram with added control areas, platform position controller 242 controls the machine / crown actuator 248 to control the platform on the harvester 100. When control system 214 receives a function prediction map or a function prediction map with added control areas, belt conveyor controller 240 controls machine / header actuator 248 to control the belt conveyor belt on agricultural harvester 100. In another example where control system 214 receives a function prediction map or a function prediction map with added control areas, other controllers 246 control other controllable subsystems 256 on agricultural harvester 100.

[0146] Figure 8A block diagram illustrating an example of a control area generator 213 is shown. The control area generator 213 includes a work machine actuator (WMA) selector 486, a control area generation system 488, and a regime area generation system 490. The control area generator 213 may also include other items 492. The control area generation system 488 includes a control area standard identifier component 494, a control area boundary definition component 496, a target setting identifier component 498, and other items 520. The regime area generation system 490 includes a regime area standard identifier component 522, a regime area boundary definition component 524, a set resolver identifier component 526, and other items 528. Before describing the overall operation of the control area generator 213 in more detail, a brief description of some of the items in the control area generator 213 and their corresponding operations will be provided first.

[0147] The agricultural harvester 100 or other operating machine may have various types of controllable actuators that perform different functions. The controllable actuators on the agricultural harvester 100 or other operating machine are collectively referred to as operating machine actuators (WMAs). Each WMA can be controlled independently based on values ​​on a function prediction map, or WMAs can be controlled in groups based on one or more values ​​on the function prediction map. Therefore, the control area generator 213 can generate control areas corresponding to each individual controllable WMA, or corresponding to groups of WMAs with coordinated control.

[0148] WMA selector 486 selects the WMA or WMA group for which a corresponding control region is to be generated. Control region generation system 488 then generates a control region for the selected WMA or WMA group. For each WMA or WMA group, different criteria can be used to identify the control region. For example, for a WMA, the WMA response time can be used as a criterion for defining the boundary of the control region. In another example, wear characteristics (e.g., the degree of wear of a particular actuator or mechanism due to its movement) can be used as a criterion for identifying the boundary of the control region. Control region criterion identifier component 494 identifies the specific criterion that will be used to define the control region for the selected WMA or WMA group. Control region boundary definition component 496 processes the values ​​on the function prediction map in the analysis to define the boundary of the control region on the function prediction map based on the values ​​on the function prediction map in the analysis and based on the control region criteria of the selected WMA or WMA group.

[0149] The target setting identifier component 498 sets the value of the target setting, which will be used to control the WMA or WMA group in different control zones. For example, if the selected WMA is a header or other machine actuator 248, and the functional prediction map in the analysis is a functional prediction crop moisture map 360, then the target setting in each control zone can be a target speed setting based on the crop moisture contained in the functional prediction crop moisture map 360.

[0150] In some examples, when controlling the harvester 100 based on its current or future position, multiple target settings are possible for the WMA at a given position. In this case, the target settings may have different values ​​and may compete with each other. Therefore, it is necessary to resolve the target settings so that only a single target setting is used to control the WMA. For example, in the case where the WMA is an actuator controlled in the propulsion system 250 to control the speed of the harvester 100, there may be multiple different competing sets of criteria, which are considered by the control area generation system 488 when identifying the control area and the target setting of the WMA selected in the control area. For example, different target settings for controlling the speed of an agricultural harvester can be generated based on, for example, detected or predicted crop moisture values, historical crop moisture values, detected or predicted agricultural characteristic values, detected or predicted vegetation index values, detected or predicted soil characteristic values, detected or predicted terrain characteristic values, detected or predicted feed rate values, detected or predicted fuel efficiency values, detected or predicted grain loss values, or combinations of these values. It should be noted that these are merely examples, and various WMA target settings can be based on a variety of other values ​​or combinations of values. However, at any given time, the agricultural harvester 100 cannot travel on the ground at multiple speeds simultaneously. Instead, 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.

[0151] Therefore, in some examples, the dynamic zone generation system 490 generates dynamic zones to resolve multiple different competing target settings. The dynamic zone criterion identification component 522 identifies the criteria used to establish dynamic zones on the selected WMA or WMA group on the functional prediction map in the analysis. Some criteria that can be used to identify or define dynamic zones include, for example, crop moisture, agricultural characteristics, topographic features, vegetation index characteristics, soil properties, operator command input, crop type or crop species (e.g., crop type or crop species based on the planting map, or crop type or crop species based on another source of crop type or crop species), weed type, weed intensity, or crop state (such as whether the crop is lodged, partially lodged, or upright). These are just some examples of criteria that can be used to identify or define dynamic zones. Just as each WMA or WMA group may have a corresponding control zone, different WMAs or WMA groups may have corresponding dynamic zones. The dynamic zone boundary definition component 524 identifies the boundaries of the dynamic zones on the functional prediction map in the analysis based on the dynamic zone criteria identified by the dynamic zone criterion identification component 522.

[0152] In some examples, dynamic zones may overlap. For instance, a crop moisture dynamic zone may partially or completely overlap with a crop state dynamic zone. In such examples, different dynamic zones can be assigned priority levels such that, in the case of two or more overlapping dynamic zones, the dynamic zone assigned a higher priority level or importance takes precedence over the dynamic zone with a lower priority level or importance. The priority levels of dynamic zones can be set manually or automatically using rule-based systems, model-based systems, or other systems. As an example, in the case of an overlap between a lodged crop dynamic zone and a crop moisture dynamic zone, the lodged crop dynamic zone can be assigned greater importance in the priority level than the crop moisture dynamic zone, thus giving priority to the lodged crop dynamic zone.

[0153] Furthermore, for a given WMA or WMA group, each dynamic region may have a unique setting resolver. The setting resolver identifier component 526 identifies a specific setting resolver for each dynamic region identified on the function prediction graph in the analysis, and for a specific setting resolver for the selected WMA or WMA group.

[0154] Once a setting resolver is identified for a specific dynamic zone, it 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 take different forms. For example, a setting resolver identified for each dynamic zone could include a human-selected resolver, in which the competing target setting is presented to an operator or other user for resolution. In another example, the setting resolver could include a neural network or other artificial intelligence or machine learning system. In this case, the setting resolver can resolve competing target settings based on a predicted or historical quality metric corresponding to each of the different target settings. As an example, an increased vehicle speed setting might reduce harvesting time and corresponding time-based labor and equipment costs, but could increase grain loss. A decreased vehicle speed setting might increase harvesting time and corresponding time-based labor and equipment costs, but could reduce grain loss. When grain loss or harvesting time is selected as a quality metric, given two competing vehicle speed setting values, the predicted or historical value of the selected quality metric can be used to resolve the speed setting. In some cases, setting the parser can be a set of threshold rules, which can be used to replace or supplement the dynamic region. Examples of threshold rules can be expressed as follows:

[0155] If the predicted crop moisture value within 20 feet of the header of the agricultural harvester 100 is greater than x (where x is the selected or predetermined value), the target setting value selected based on feed rate rather than other competing target settings is used; otherwise, the target setting value based on grain loss rather than other competing target settings is used.

[0156] A setting parser can be a logical component that executes logical rules when identifying a target setting. For example, a setting parser can parse a target setting while attempting to minimize harvest time, minimize total harvest cost, or maximize harvested grain, or other variables calculated as a function of different candidate target settings. Harvesting time can be minimized when the amount harvested is reduced to or below a selected threshold. Total harvest cost can be minimized when it is reduced to or below a selected threshold. Harvested grain can be maximized when the amount harvested is increased to or above a selected threshold.

[0157] Figure 9 This is a flowchart illustrating an example of the operation of the control region generator 213 when it receives a graph for region processing (e.g., a graph in analysis) and generates control regions and dynamic regions.

[0158] In box 530, the control region generator 213 receives the graph in the analysis for processing. In one example, as shown in box 532, the graph in the analysis is a function prediction graph. For example, the graph in the analysis could be one of function prediction graphs 360, 427, or 440. Box 534 indicates that the graph in the analysis could also be other graphs.

[0159] In box 536, WMA selector 486 selects the WMA or WMA group for which a control zone is to be generated on the graph in the analysis. In box 538, control zone criterion identification component 494 obtains the control zone definition criteria for the selected WMA or WMA group. Box 540 indicates an example where the control zone criteria are or include wear characteristics of the selected WMA or WMA group. Box 542 indicates an example where the control zone definition criteria are or include the amplitude and variation of input source data, such as the amplitude and variation of values ​​on the graph in the analysis or the amplitude and variation of inputs from various field sensors 208. Box 544 indicates an example where the control zone definition criteria are or include physical machine characteristics, such as the physical dimensions of the machine, the speed of operation of different subsystems, or other physical machine characteristics. Box 546 indicates an example where the control zone definition criteria are or include the responsiveness of the selected WMA or WMA group when a setpoint for a new command is reached. Box 548 indicates an example where the control zone definition criteria are or include machine performance metrics. Box 550 indicates an example where the control zone definition criterion is or includes operator preferences. Box 552 indicates an example where the control zone definition criterion is or includes other items. Box 549 indicates an example where the control zone definition criterion is time-based, meaning that the harvester 100 will not cross the boundary of the control zone until a selected amount of time has elapsed since the harvester 100 entered the specific control zone. In some cases, the selected amount of time may be a minimum amount of time. Therefore, in some cases, the control zone definition criterion can prevent the harvester 100 from crossing the boundary of the control zone until at least the selected amount of time has elapsed. Box 551 indicates an example where the control zone definition criterion is based on a selected size value. For example, a control zone definition criterion based on a selected size value can exclude the definition of control zones smaller than the selected size. In some cases, the selected size may be a minimum size.

[0160] In box 554, the dynamic zone standard identification component 522 obtains the dynamic zone definition standard of the selected WMA or WMA group. Box 556 indicates an example where the dynamic zone definition standard is based on manual input from operator 260 or another user. Box 558 shows an example where the dynamic zone definition standard is based on crop humidity (including detected crop humidity, predicted crop humidity, or historical crop humidity). Box 559 shows an example where the dynamic zone definition standard is based on vegetation index characteristics. Box 560 shows an example where the dynamic zone definition standard is based on topographic features. Box 561 shows an example where the dynamic zone definition standard is based on soil properties. Box 564 indicates an example where the dynamic zone definition standard is also or includes other standards.

[0161] In box 566, the control zone boundary definition component 496 generates the boundary of the control zone on the graph in the analysis based on the control zone criteria. The dynamic zone boundary definition component 524 generates the boundary of the dynamic zone on the graph in the analysis based on the dynamic zone criteria. Box 568 indicates an example in which the boundary of the control zone and the dynamic zone is identified. Box 570 shows that the target setting identifier component 498 identifies the target settings for each in the control zone. The control zone and the dynamic zone can also be generated in other ways, and this is indicated by box 572.

[0162] In box 574, the set parser identifier component 526 identifies the set parser for the selected WMA in each dynamic region defined by the dynamic region boundary definition component 524. As discussed above, the dynamic region parser can be a human parser 576, an artificial intelligence or machine learning system parser 578, a parser based on the predicted quality or historical quality of each competing objective setting 580, a rule-based parser 582, a performance criterion-based parser 584, or another parser 586.

[0163] In box 588, WMA selector 486 determines if there are more WMAs or WMA groups to process. If there are additional WMAs or WMA groups to process, processing returns to box 436, where the next WMA or WMA group for which a control area and dynamic area are defined is selected. When no additional WMAs or WMA groups remain for which a control area or dynamic area is to be generated, processing moves to box 590, where control area generator 213 generates a graph for each output in each WMA or WMA group, with a control area, target settings, dynamic area, and settings resolver. As discussed above, the output graph can be presented to operator 260 or another user; the output graph can be provided to control system 214; or the output graph can be output in other ways.

[0164] Figure 10An example is shown of a control system 214 controlling the operation of an agricultural harvester 100 based on a map output by a control zone generator 213. Thus, in box 592, the control system 214 receives a map of the work site. In some cases, this map may be a functional prediction map that includes control zones and dynamic zones (as shown in box 594). In some cases, the received map may be a functional prediction map that excludes control zones and dynamic zones. Box 596 indicates an example where the received work site map may be a priori information map with control zones and dynamic zones identified thereon. Box 598 indicates an example where the received map may include multiple different maps or multiple different layers. Box 610 indicates an example where the received map may also take other forms.

[0165] In block 612, control system 214 receives sensor signals from geolocation sensor 204. Sensor signals from geolocation sensor 204 may include data indicating the geolocation 614 of harvester 100, the speed 616 of harvester 100, the heading 618 of harvester 100, or other information 620. In block 622, area controller 247 selects a dynamic area, and in block 624, area controller 247 selects a control area on a map based on the geolocation sensor signals. In block 626, area controller 247 selects a WMA or WMA group to be controlled. In block 628, area controller 247 obtains one or more target settings for the selected WMA or WMA group. The target settings obtained for the selected WMA or WMA group can come from a variety of different sources. For example, block 630 shows an example where one or more of the target settings for the selected WMA or WMA group are based on input from a control area on a map from the work site. Block 632 shows an example where one or more of the target settings are obtained from manual input from operator 260 or another user. Box 634 illustrates an example where the target settings are obtained from field sensor 208. Box 636 illustrates an example where one or more target settings are obtained from one or more sensors on other machines operating simultaneously with the agricultural harvester 100 in the same field, or from one or more sensors on machines that have previously operated in the same field. Box 638 illustrates an example where the target settings are also obtained from other sources.

[0166] In box 640, the zone controller 247 accesses the settings resolver of the selected dynamic zone and controls the settings resolver to parse the competing target settings into a resolved target setting. As discussed above, in some cases, the settings resolver may be a human resolver, in which case the zone controller 247 controls the operator interface mechanism 218 to present the competing target settings to the operator 260 or another user for resolution. In some cases, the settings resolver may be a neural network or other artificial intelligence or machine learning system, and the zone controller 247 submits the competing target settings to the neural network, artificial intelligence, or machine learning system for selection. In some cases, the settings resolver may be based on predicted or historical quality metrics, based on threshold rules, or based on logic components. In any of these later examples, the zone controller 247 executes the settings resolver to obtain the resolved target setting based on predicted or historical quality metrics, based on threshold rules, or when using logic components.

[0167] At block 642, if the zone controller 247 has identified the resolved target setting, it provides the resolved target setting to other controllers in the control system 214. These controllers generate control signals based on the resolved target setting and apply the control signals to the selected WMA or WMA group. For example, if the selected WMA is a machine or header actuator 248, the 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 an additional WMA or additional WMA group is to be controlled at the current geographic location of the harvester 100 (as detected in 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 harvester 100 have been resolved. If no additional WMA or WMA group remains to be controlled at the current geographic location of the harvester 100, the process proceeds to box 646, where the area controller 247 determines whether an additional control area to be considered exists in the selected dynamic area. If an additional control area to be considered exists, the process returns to box 624, where the next control area is selected. If no additional control area needs to be considered, the process proceeds to box 648, where it is determined whether there is an additional dynamic area to be considered. The area controller 247 determines whether there is an additional dynamic area to be considered. If there is an additional dynamic area to be considered, the process returns to box 622, where the next dynamic area is selected.

[0168] In box 650, the zone controller 247 determines whether the operation being performed by the harvester 100 has been completed. If not, the zone controller 247 determines whether control zone criteria have been met to continue processing, as shown in box 652. For example, as mentioned above, control zone definition criteria may include criteria defining when the harvester 100 can cross the control zone boundary. For example, whether the harvester 100 can cross the control zone boundary may be defined by a selected time period, meaning that the harvester 100 is prevented from crossing the zone boundary until the selected amount of time has elapsed. In this case, in box 652, the zone controller 247 determines whether the selected time period has elapsed. Additionally, the zone controller 247 can perform processing continuously. Therefore, the zone controller 247 does not wait for any specific time period before continuing to determine whether the operation of the harvester 100 has been completed. In box 652, the zone controller 247 determines that it is time to continue processing, and then processing continues in box 612, where the zone controller 247 again receives input from the geolocation sensor 204. It should also be understood that the zone controller 247 can use a multiple-input multiple-output controller to control the WMA and WMA group simultaneously, rather than controlling the WMA and WMA group sequentially.

[0169] Figure 11 This is a block diagram illustrating an example of an operator interface controller 231. In the illustrated example, the operator interface controller 231 includes an operator input command processing system 654, other controller interaction systems 656, a voice processing system 658, and a motion signal generator 660. The operator input command processing system 654 includes a voice processing system 662, a touch gesture processing system 664, and other items 666. The other controller interaction system 656 includes a controller input processing system 668 and a controller output generator 670. The voice processing system 658 includes a trigger detector 672, a recognition unit 674, a synthesis unit 676, a natural language understanding system 678, a dialogue management system 680, and other items 682. The motion signal generator 660 includes a visual control signal generator 684, an audio control signal generator 686, a tactile control signal generator 688, and other items 690. Figure 11 Before processing various operator interface actions, the example operator interface controller 231 shown in the figure first provides a brief description of some of the items in the operator interface controller 231 and their associated operations.

[0170] The operator input command processing system 654 detects operator input on the operator interface mechanism 218 and processes these command inputs. The voice processing system 662 detects voice input and processes interaction with the voice processing system 658 to process voice command inputs. The touch gesture processing system 664 detects touch gestures on touch-sensitive elements in the operator interface mechanism 218 and processes these command inputs.

[0171] Other controller interaction system 656 handles interactions with other controllers in control system 214. Controller input processing system 668 detects and processes inputs from other controllers in control system 214, and controller output generator 670 generates outputs and provides these outputs to other controllers in control system 214. Voice processing system 658 recognizes voice input, determines the meaning of these inputs, and provides outputs indicating the meaning of the spoken input. For example, voice processing system 658 can recognize voice input from operator 260 as a setting change command, where operator 260 is instructing control system 214 to change the settings of controllable subsystem 216. In such an example, voice processing system 658 recognizes the content of the spoken command, identifies the meaning of the command as a setting change command, and provides the meaning of the input back to voice processing system 662. Voice processing system 662 then interacts with controller output generator 670 to provide command outputs to the appropriate controllers in control system 214 to complete the spoken setting change command.

[0172] The voice processing system 658 can be invoked in various ways. For example, in one example, the voice processing system 662 continuously provides input from a microphone (as part of the operator interface mechanism 218) to the voice processing system 658. The microphone detects speech from the operator 260, and the voice processing system 662 provides the detected speech to the voice processing system 658. A trigger detector 672 detects a trigger indicating that the voice processing system 658 has been invoked. In some cases, when the voice processing system 658 receives continuous voice input from the voice processing system 662, the voice recognition unit 674 performs continuous speech recognition on all speech uttered by the operator 260. In some cases, the voice processing system 658 is configured to be invoked using a wake-up word. That is, in some cases, the operation of the voice processing system 658 can be initiated based on the recognition of a selected spoken word (referred to as a wake-up word). In such an example, when the recognition unit 674 recognizes the wake-up word, the recognition unit 674 provides an indication to the trigger detector 672 that the wake-up word has been recognized. Trigger detector 672 detects that voice processing system 658 has been invoked or triggered by a wake word. In another example, voice processing system 658 may be invoked by operator 260 actuating an actuator on the user interface mechanism, such as by touching an actuator on a touch-sensitive display, by pressing a button, or by providing another trigger input. In such an example, trigger detector 672 can detect that voice processing system 658 has been invoked when a trigger input is detected via the user interface mechanism. Trigger detector 672 may also detect that voice processing system 658 has been invoked in other ways.

[0173] Once the speech processing system 658 is invoked, speech input from operator 260 is provided to the speech recognition unit 674. The speech recognition unit 674 recognizes linguistic elements in the speech input, such as words, phrases, or other linguistic units. The natural language understanding system 678 identifies the meaning of the recognized speech. This meaning can be any of the following: natural language output, command output identifying a command reflected in the recognized speech, value output identifying a value in the recognized speech, or a variety of other outputs reflecting an understanding of the recognized speech. For example, more generally, the natural language understanding system 678 and the speech processing system 568 can understand the meaning of speech recognized in the environment of the agricultural harvester 100.

[0174] In some examples, the voice processing system 658 can also generate output for user-guided navigation of the operator 260 based on voice input. For instance, the dialogue management system 680 can generate and manage dialogues with the user to identify what the user wants to do. This dialog box can disambiguate user commands, identify one or more specific values ​​required to execute the user command, or obtain other information from or provide other information to the user, or both. The synthesis component 676 can generate speech synthesis, which can be presented to the user through an audio operator interface mechanism such as a speaker. Therefore, dialogues managed by the dialogue management system 680 can be exclusively verbal dialogues or a combination of visual and verbal dialogues.

[0175] Motion signal generator 660 generates motion signals to control operator interface mechanism 218 based on outputs from one or more of operator input command processing system 654, other controller interaction system 656, and voice processing system 658. Visual control signal generator 684 generates control signals to control visual items in operator interface mechanism 218. Visual items may be lights, displays, warning indicators, or other visual items. Audio control signal generator 686 generates outputs to control audio elements of operator interface mechanism 218. Audio elements include speakers, audible alarm mechanisms, horns, or other audible elements. Tactile control signal generator 688 generates control signals that are output to control tactile elements of operator interface mechanism 218. Tactile elements include vibratory elements that can be used to make vibrations, such as an operator's seat, steering wheel, pedals, or joystick used by the operator. Tactile elements may include tactile feedback or force feedback elements that provide tactile or force feedback to the operator through the operator interface mechanism. Tactile elements may also include a variety of other tactile elements.

[0176] Figure 12 This is a flowchart illustrating an example of the operation of the operator interface controller 231 when generating an operator interface display on an operator interface mechanism 218 that may include a touch-sensitive display screen. Figure 12 An example of how the operator interface controller 231 can detect and process operator interactions with the touch-sensitive display is also shown.

[0177] In box 692, the operator interface controller 231 receives a graph. Box 694 indicates that the graph is an example of a functional prediction graph, while box 696 indicates that the graph is an example of another type of graph. In box 698, the operator interface controller 231 receives input from the geolocation sensor 204 identifying the geolocation of the harvester 100. As shown in box 700, the input from the geolocation sensor 204 may include the heading and position of the harvester 100. Box 702 indicates an example where the input from the geolocation sensor 204 includes the speed of the harvester 100, and box 704 indicates an example where the input from the geolocation sensor 204 includes other items.

[0178] In box 706, the vision control signal generator 684 in the operator interface controller 231 controls the touch-sensitive display in the operator interface mechanism 218 to generate a display showing all or part of the field represented by the received map. Box 708 indicates that the displayed field may include a current position marker showing the current position of the harvester 100 relative to the field. Box 710 indicates an example where the displayed field includes a next work unit marker identifying the next work unit (or area on the field) in which the harvester 100 will operate. Box 712 indicates an example where the displayed field includes an upcoming area display showing areas not yet processed by the harvester 100, while box 714 indicates an example where the displayed field includes a previously visited display showing areas of the field already processed by the harvester 100. Box 716 indicates an example where the displayed field shows various features of the field having a geographic reference location on the map. For example, if the received map is a crop moisture map (e.g., a predicted crop moisture map 360), the displayed fields can show different crop moisture values ​​present in georeferenced fields within the displayed fields. Mapped features can be shown in previously visited areas (as shown in box 714), upcoming areas (as shown in box 712), and the next work unit (as shown in box 710). Box 718 indicates that the fields displayed also include examples of other items.

[0179] Figure 13 This illustration shows an example of a user interface display 720 that can be generated on a touch-sensitive display screen. In other embodiments, the user interface display 720 can be generated on other types of displays. The touch-sensitive display screen can be installed in the operator's cab of an agricultural harvester 100, on a mobile device, or elsewhere. (Continuing the description...) Figure 12 Before the flowchart shown, the user interface display 720 will be described.

[0180] exist Figure 13In the example shown, the user interface display 720 illustrates a touch-sensitive display including display features for operating a microphone 722 and a speaker 724. Therefore, the touch-sensitive display can be communicatively coupled to the microphone 722 and the speaker 724. Box 726 indicates that the touch-sensitive display may include various user interface control actuators, such as buttons, keypads, soft keypads, links, icons, switches, etc. Operator 260 can actuate the user interface control actuators to perform various functions.

[0181] exist Figure 14 In the example shown, the user interface display 720 includes a field display portion 728 that displays at least a portion of the field in which the harvester 100 is operating. The field display portion 728 is shown as having a current position marker 708 corresponding to the current position of the harvester 100 within the portion of the field shown in the field display portion 728. In one example, the operator can control the touch-sensitive display to zoom in on a portion of the field display portion 728, or pan or scroll the field display portion 728 to display different portions of the field. The next work unit 730 is shown as the field area directly in front of the current position marker 708 of the harvester 100. The current position marker 708 can also be configured to identify the direction of travel of the harvester 100, the speed of travel of the harvester 100, or both. Figure 14 In the image, the shape of the current position marker 708 provides an indication of the orientation of the agricultural harvester 100 in the field, which can be used as an indication of the direction of travel of the agricultural harvester 100.

[0182] The size of the next work unit 730 marked on the field display section 728 can vary based on a variety of different criteria. For example, the size of the next work unit 730 can vary based on the travel speed of the harvester 100. Therefore, the area of ​​the next work unit 730 may be larger when the harvester 100 is traveling faster compared to when the harvester 100 is traveling slower. In another example, the size of the next work unit 730 can vary based on the size of the harvester 100 (including the equipment on the harvester 100, such as the header 102). For example, the width of the next work unit 730 can vary based on the width of the header 102. The field display section 728 is also shown displaying previously visited areas 714 and upcoming areas 712. The previously visited area 714 represents areas that have already been harvested, while the upcoming area 712 represents areas that still need to be harvested. The field display section 728 is also shown displaying different features of the field. Figure 13In the example shown, the plot being displayed is a crop moisture plot (e.g., a functional predictive crop moisture plot 360). Therefore, multiple crop moisture markers are displayed on the field display section 728. A set of crop moisture display markers 732 is shown in the already visited area 714. Another set of crop moisture display markers 732 is shown in the upcoming area 712, and a further set of crop moisture display markers 732 is shown in the next work unit 730. Figure 14 The crop humidity display mark 732 is shown to consist of different symbols indicating areas with similar crop humidity. Figure 14 In the example shown, the ! symbol indicates an area of ​​high crop humidity; the * symbol indicates an area of ​​ideal crop humidity; and the # symbol indicates an area of ​​low crop humidity. Therefore, the field display section 728 displays different measured or predicted values ​​(or features indicated by values) located in different areas of the field and represents those measured or predicted values ​​(or features indicated by values ​​or features derived from values) with various display markers 732. 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 13 The example shown is crop moisture display mark 732. In some cases, each location in the field may have a display mark associated with it. Therefore, in some cases, display marks may be provided at each location of the field display section 728 to identify the nature of a characteristic mapped to each particular location of the field. Therefore, this disclosure includes providing display marks at one or more locations on the field display section 728, for example (as in...). Figure 13 (In the context of this example) Crop moisture display mark 732 is used to identify the nature, degree, etc., of the feature being displayed, thereby identifying the feature at the corresponding location in the field being displayed. As previously mentioned, display mark 732 can consist of different symbols, and as described below, the symbols can be any display feature, such as different colors, shapes, patterns, intensities, text, icons, or other display features. In some cases, each location in the field can have a display mark associated with it. Therefore, in some cases, display marks can be provided at each location of the field display section 728 to identify the nature of the feature mapped to each particular location in the field. Therefore, this disclosure includes providing display marks at one or more locations on the field display section 728, for example (as in... Figure 11 In the context of this example, loss level display marker 732 is used to identify the nature, degree, etc. of the feature being displayed, thereby identifying the feature at the corresponding location in the field being displayed.

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

[0184] exist Figure 13 In the example, the user interface display 720 also has a control display section 738. The control display section 738 allows the operator to view information and interact with the user interface display 720 in various ways.

[0185] The actuators and display markers in part 738 can be displayed as, for example, individual items, fixed lists, scrollable lists, drop-down menus, or drop-down lists. Figure 13 In the example shown, display section 738 displays three different stem diameter categories corresponding to the three symbols mentioned above. Display section 738 also includes a set of touch-sensitive actuators that operator 260 can interact with by touch. For example, operator 260 can touch a touch-sensitive actuator with a finger to activate the corresponding actuator. As shown, display section 238 also includes multiple interactive tabs, such as crop humidity tab 762 and other tabs 770. The values ​​displayed in sections 728 and 738 for an active tab can be modified. For example, as shown, crop humidity tab 762 is activated and therefore mapped onto section 728, and the value displayed in section 738 corresponds to a crop humidity value. When operator 260 touches tab 770, touch gesture processing system 664 updates sections 728 and 738 to display other agricultural features, such as operator commands.

[0186] like Figure 14As shown, display portion 738 includes an interactive marker display portion, generally denoted as 741. The interactive marker display portion 741 includes a marker column 739 that displays markers that have been automatically or manually set. Marker actuator 740 allows operator 260 to mark locations, such as the current location of the harvester or another location on the field specified by the operator, and adds information indicating features (e.g., crop moisture) found at the current location. For example, when operator 260 actuates marker actuator 740 by touching it, touch gesture processing system 664 in operator interface controller 231 identifies the current location as a location where the harvester 100 encounters high crop moisture. When operator 260 touches button 742, touch gesture processing system 664 identifies the current location as a location where the harvester 100 encounters ideal crop moisture. When operator 260 touches button 744, touch gesture processing system 664 identifies the current location as a location where the harvester 100 encounters low crop moisture. When one of the actuation sign actuators 740, 742, or 744 is activated, the touch gesture processing system 664 can control the visual control signal generator 684 to add a symbol corresponding to the identified feature on the field display portion 728 at the user-identified location. In this way, areas of the field where the predicted value cannot accurately represent the actual value can be marked for later analysis or for machine learning. In other examples, the operator can specify an area in front of or around the harvester 100 by actuating one of the actuation sign actuators 740, 742, or 744, allowing control of the harvester 100 based on values ​​specified by the operator 260.

[0187] Display section 738 also includes an interactive marker display section, generally denoted as 743. The interactive marker display section 743 includes a symbol column 746, which displays values ​​or features tracked on the field display section 728 (in...). Figure 13 In the case of crop humidity), each category has a symbol. The display section 738 also includes an interactive indicator display section, generally denoted by 745. The interactive indicator display section 745 includes a column of identifiers 748 that displays an identifier value or characteristic (in the context of crop humidity). Figure 13 In the case of crop moisture), the category of the designator (which may be a text designator or other designator). Without limitation, the symbols in the symbol column 746 and the designators in the designator column 748 may include any display features, such as different colors, shapes, patterns, intensities, text, icons or other display features, and may be customized through the interaction of the operator of the agricultural harvester 100.

[0188] Display section 738 also includes an interactive value display section, generally denoted as 747. The interactive value display section 747 includes a value display column 750 that displays the selected value. The selected value corresponds to a feature or value that is being tracked or displayed, or both, on the field display section 728. The selected value can be selected by the operator of the agricultural harvester 100. The selected value in the value display column 750 defines a range of values ​​or other values ​​(such as predicted values) by which they are categorized. Therefore, in Figure 13 In the examples, predicted or measured crop moisture levels of 16% or higher are categorized as "high crop moisture," while predicted or measured crop moisture levels of 12% or lower are categorized as "low crop moisture." In some examples, the selected values ​​may include a range, such that predicted or measured values ​​within the selected range will be categorized under the appropriate indicator. For example... Figure 13 As shown, "ideal crop humidity" includes a range of 13%-15%, so that measured or predicted crop humidity values ​​falling within this range are classified as "ideal crop humidity." The selected value in column 750 can be adjusted by the operator of the agricultural harvester 100. In one example, operator 260 can select a specific portion of the field display section 728, and the value in column 750 will be displayed for that specific portion. Therefore, the value in column 750 can correspond to the value in display sections 712, 714, or 730.

[0189] Display section 738 also includes an interactive threshold display section, generally denoted as 749. The interactive threshold display section 749 includes a threshold display column 752 that displays action thresholds. The action threshold in column 752 can be a threshold corresponding to a selected value in value display column 750. If a predicted or measured value of a feature being tracked, displayed, or both meets the corresponding action threshold in threshold display column 752, control system 214 takes one or more actions identified in column 754. In some cases, a measured or predicted value can satisfy a corresponding action threshold by meeting or exceeding it. In one example, operator 260 can select a threshold, for example, to change the threshold by touching a threshold in threshold display column 752. Once selected, operator 260 can change the threshold. The threshold in column 752 can be configured such that a specified action is performed when the measured or predicted value 750 of the feature exceeds, equals, or is less than the threshold. In some cases, a threshold may represent a range of values, or a range of deviations from the selected values ​​in value display column 750, such that predicted or measured characteristic values ​​that meet or fall within that range satisfy the threshold. For example, in the crop moisture example, a predicted crop moisture falling within the 5% to 16% moisture range would satisfy the corresponding action threshold (within the 5% to 16% moisture range) and action, such as adjusting the speed of an agricultural harvester or adjusting the header by control system 214. In other examples, the thresholds in threshold display column 752 are separated from the selected values ​​in value display column 750, such that the values ​​in value display column 750 define the classification and display of predicted or measured values, while the action threshold defines when an action is taken based on the measured or predicted value. For example, for classification and display purposes, a predicted or measured crop moisture of 16% could be designated as “high crop moisture,” and the action threshold could be 17%, such that no action is taken until the crop moisture meets the threshold. In other examples, the thresholds in threshold display column 752 may include distance or time. For example, in the distance example, the threshold could be a threshold distance from a field area to a measured or predicted value that is georeferenced, where the harvester 100 must be within that field area before taking action. For example, a 5-foot threshold distance value means that the harvester will take action when it is 5 feet or less from the measured or predicted value of the georeferenced field area. In the example where the threshold is time, the threshold could be a threshold time for the harvester 100 to reach the measured or predicted value of the georeferenced field area. For example, a 5-second threshold would mean that the harvester 100 will take action when it is 5 seconds away from the measured or predicted value of the georeferenced field area. In such examples, the harvester's current location and travel speed can be considered.

[0190] Display section 738 also includes an interactive action display section, generally denoted as 751. The interactive action display section 751 includes an action display column 754 that displays action identifiers indicating the action to be taken when a predicted or measured value meets an action threshold in threshold display column 752. Operator 260 can touch the action identifiers in column 754 to change the action to be taken. An action can be taken when the threshold is met. For example, at the bottom of column 754, actions such as raising the header, lowering the header, increasing speed, and decreasing speed are identified as actions to be taken when the measured or predicted value meets the threshold in column 752. In some examples, multiple actions can be taken when the threshold is met. For example, the position of the header (e.g., height, pitch, or roll) and the speed of the crop can be adjusted. These are just some examples.

[0191] The actions set in column 754 can be any of a variety of different types of actions. For example, these actions can include prohibition actions that, when executed, prevent the combine harvester 100 from harvesting further in the area. These actions can include speed-changing actions that, when executed, change the speed at which the combine harvester 100 travels through the field. These actions can include setting-changing actions for changing the settings of an internal actuator or another WMA or WMA group, or setting-changing actions for implementing changes in settings (such as header position settings (e.g., header height setting, header pitch setting, or header roll setting) and various other settings). These are merely examples, and a wide variety of other actions are considered herein.

[0192] The items displayed on the user interface display 720 can be visually controlled. Visual control of the interface display 720 can be performed to capture the attention of the operator 260. For example, the intensity, color, or pattern of the displayed item can be modified. Additionally, the blinking of the item can be controlled. Changes to the visual appearance of the item are provided as examples. Therefore, other aspects of the visual appearance of the item can be changed. Thus, items can be modified in a desired manner in various situations to, for example, capture the attention of the operator 260. Furthermore, while a specific number of items are displayed on the user interface display 720, this is not always the case. In other examples, more or fewer items (including more or fewer specific items) can be included on the user interface display 720.

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

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

[0195] In box 790, operator interface controller 231 receives a signal indicating an alarm condition. For example, box 792 indicates a signal that can be received by controller input processing system 668, indicating that a detected or predicted value satisfies a threshold condition present in column 752. As previously explained, a threshold condition can include values ​​below, above, or below a threshold. Box 794 shows that action signal generator 660 can, in response to receiving an alarm condition, generate a visual alarm using visual control signal generator 684, an audio alarm using audio control signal generator 686, a tactile alarm using tactile control signal generator 688, or any combination thereof to alert operator 260. Similarly, as shown in box 796, controller output generator 670 can generate outputs to other controllers in control system 214, causing these controllers to perform the corresponding actions identified in column 754. Box 798 shows that operator interface controller 231 can also detect and process alarm conditions in other ways.

[0196] Box 900 illustrates that the voice processing system 662 can detect and process input that invokes the voice processing system 658. Box 902 illustrates that performing voice processing may include using the dialogue management system 680 to converse with the operator 260. Box 904 illustrates that voice processing may include providing a signal to the controller output generator 670 to automatically perform control operations based on the voice input.

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

[0198] Table 1

[0199] Operator: "Johnny, tell me about the current crop moisture level."

[0200] Operator interface controller: "Current crop humidity is very high."

[0201] Operator: "Johnny, what should I do about the crop's humidity?"

[0202] Operator interface controller: "Reduce travel speed by 1 mph to achieve the desired feed rate."

[0203] Table 2 illustrates such an example where the speech synthesis unit 676 provides output to the audio control signal generator 686 to provide auditory updates intermittently or periodically. The interval between updates can be based on time (such as every five minutes), or on coverage or distance (such as every five acres), or on anomalies (such as when a measured value exceeds a threshold).

[0204] Table 2

[0205] Operator interface controller: "The crop humidity has been very high for the past 10 minutes."

[0206] Operator interface controller: "The next 1 acre is predicted to have moderate crop moisture."

[0207] Operator interface controller: "Warning: Crop moisture is about to change, travel speed will increase by 1 mph".

[0208] The examples shown in Table 3 illustrate some actuators or user input mechanisms on the touch-sensitive display 720 that can be supplemented by voice dialogue. The examples in Table 3 also show that the motion signal generator 660 can generate motion signals to automatically mark crop moisture zones in a field being harvested.

[0209] Table 3

[0210] Human: "Johnny, mark areas with high crop humidity."

[0211] Operator interface controller: "High crop humidity area marked".

[0212] The example shown in Table 4 illustrates that the motion signal generator 660 can communicate with the operator 260 to start and stop marking crop moisture zones.

[0213] Table 4

[0214] Human: "Johnny, start marking areas with high crop humidity."

[0215] Operator interface controller: "Mark areas with high crop humidity".

[0216] Human: "Johnny, stop marking high crop humidity areas."

[0217] Operator interface controller: "Stop marking high crop humidity areas".

[0218] The examples shown in Table 5 illustrate that the motion signal generator 160 can generate signals marking crop humidity zones in a manner different from that shown in Tables 3 and 4.

[0219] Table 5

[0220] Human: "Johnny, mark the next 100 feet as a low crop moisture zone."

[0221] Operator interface controller: "The next 100 feet is marked as a low crop humidity zone."

[0222] Return again Figure 12 Box 906 illustrates that the operator interface controller 231 can also detect and process situations for outputting messages or other information in other ways. For example, the other controller interaction system 656 can detect input from other controllers indicating alarms or output messages that should be presented to the operator 260. Box 908 illustrates that the output can be an audio message. Box 910 illustrates that the output can be a visual message, and Box 912 illustrates that the output can be a tactile message. Until the operator interface controller 231 determines that the current harvesting operation is complete (as shown in Box 914), processing returns to Box 698, where the geographical location of the harvester 100 is updated, and processing continues as described above to update the user interface display 720.

[0223] Once the operation is complete, any desired values ​​displayed or already displayed on the user interface display 720 can be saved. These values ​​can also be used in machine learning to improve different parts of the predictive model generator 210, predictive map generator 212, control area generator 213, control algorithm, or other components. The saved desired values ​​are indicated by box 916. These values ​​can be saved locally on the agricultural harvester 100, or they can be saved at a remote server location or sent to another remote system.

[0224] Therefore, one or more maps are obtained by an agricultural harvester, showing agricultural characteristic values ​​(such as predicted or historical crop moisture values, topographic features, vegetation index values, or soil properties) at different geographic locations in the field being harvested. Field sensors on the harvester sense features with values ​​indicating agricultural characteristics as the harvester moves through the field. A prediction map generator produces a prediction map that predicts control values ​​for different locations in the field based on the agricultural characteristic values ​​in the map and the agricultural characteristics sensed by the field sensors. The control system controls the controllable subsystems based on the control values ​​in the prediction map.

[0225] A control value is a value upon which an action can be based. As described herein, a control value can include any value (or a characteristic indicated by or derived from that value) that can be used to control the agricultural harvester 100. A control value can be any value indicating an agricultural characteristic. A control value can be a predicted value, a measured value, or a detected value. A control value can include any value provided by a graph (such as any of the graphs described herein), for example, a control value can be a value provided by an infographic, a value provided by a priori infographic, or a value provided by a predictive graph, such as a functional prediction graph. A control value can also include any characteristic indicated by or derived from a value detected by any of the sensors described herein. In other examples, control values ​​can be provided by the operator of the agricultural machine, such as commands entered by the operator of the agricultural machine.

[0226] The current discussion has already mentioned processors and servers. In some examples, processors and servers include computer processors with associated memory and timing circuitry (not shown separately). Processors and servers are functional parts of the system or device to which they belong, and are activated and facilitated by other components or items in these systems.

[0227] Furthermore, numerous user interface displays have been discussed. Displays can take various forms and can have various user-actuable operator interface structures set on them. For example, user-actuable operator interface mechanisms can be text boxes, checkboxes, icons, links, drop-down menus, search boxes, etc. User-actuable operator interface mechanisms can also be actuated in various ways. For example, user-actuable operator interface mechanisms can be actuated using operator interface mechanisms such as click devices (e.g., trackballs or mice, hardware buttons, switches, joysticks or keyboards, thumb switches or thumb pads, etc.), virtual keyboards, or other virtual actuators). Furthermore, if the screen displaying the user-actuable operator interface mechanisms is a touch-sensitive screen, touch gestures can be used to actuate the user-actuable operator interface mechanisms. Moreover, voice recognition functionality can be used to actuate user-actuable operator interface mechanisms using voice commands. Voice recognition can be implemented using voice detection devices (such as microphones) and software for recognizing the detected voice and executing commands based on the received voice.

[0228] Many data storage devices are also discussed. It should be noted that data storage can be divided into multiple data storage devices. In some examples, one or more of the data storage devices may be local to the system accessing the data storage devices; one or more of the data storage devices may all be located remotely from the system utilizing the data storage devices; or one or more data storage devices may be local while others are remote. This disclosure considers all of these configurations.

[0229] Furthermore, the accompanying diagram shows multiple boxes, with each function belonging to a specific box. It should be noted that fewer boxes can be used to illustrate that functions attributed to multiple different boxes are performed by fewer components. Moreover, more boxes can be used to show that the function can be distributed across more components. In different examples, some functions can be added, and some functions can be removed.

[0230] It should be noted that the foregoing discussion has described various 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, memory, or other processing units, including but not limited to artificial intelligence units (such as neural networks, some of which are described below) that perform functions associated with those systems, components, logic, or interactions. Furthermore, any or all of the systems, components, logic, and interactions can be implemented by software loaded into memory and subsequently executed by a processor, server, or other computing unit, 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 may also be used.

[0231] Figure 14 This is a block diagram of the agricultural harvester 600, which can be similar to... Figure 2 The agricultural harvester 100 is shown in the diagram. The agricultural harvester 600 communicates with components in a remote server architecture 500. In some examples, the remote server architecture 500 can provide computing, software, data access, and storage services without requiring end users to know the physical location or configuration of the system delivering the services. In various examples, the remote server can deliver services over a wide area network (such as the Internet) using appropriate protocols. For example, the remote server can deliver applications over a wide area network and can be accessed via a web browser or any other computing component. Figure 2 The software or components shown herein, along with their associated data, can be stored on servers at remote locations. Computing resources in a remote server environment can be consolidated at a remote data center location, or they can be distributed across multiple remote data centers. Remote server infrastructure can deliver services through shared data centers, even if it appears as a single access point for a user. Therefore, the components and functions described herein can be provided from remote servers at remote locations using a remote server architecture. Alternatively, components and functions can be provided from servers, or they can be installed directly or otherwise on client devices.

[0232] exist Figure 14 In the examples shown, some items are similar to Figure 2 The items shown are numbered similarly. Figure 14 Specifically, it is shown that the prediction model generator 210 or the prediction graph generator 212, or both, can be located at a server location 502, away from the agricultural harvester 600. Therefore, in Figure 14 In the example shown, the agricultural harvester 600 accesses the system via a remote server location 502.

[0233] Figure 14 Another example of a remote server architecture is also described. Figure 14 It shows Figure 2 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 placed at a location separate from location 502 and accessed via a remote server at location 502. Regardless of their location, these components can be directly accessed by the combine harvester 600 via a network (such as a wide area network or local area network), hosted by a service at a remote site, provided as a service, or accessed by a connection service residing at a remote location. Furthermore, data can be stored anywhere, and the stored data can be accessed or forwarded to 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 absent, another machine (such as a fuel truck or other mobile machine or vehicle) can have an automatic, semi-automatic, or manual information collection system. When the combine harvester 600 approaches the machine (such as a fuel truck) containing the information collection system before refueling, the information collection system collects information from the combine harvester 600 using any type of temporary dedicated wireless connection. Then, when the machine containing the received information reaches a location with wireless telecommunications service coverage or other available wireless coverage, the collected information can be forwarded to another network. For example, when a fuel truck travels to a location to refuel other machines or to a main fuel storage location, the fuel truck can enter an area with wireless communication coverage. All these architectures are considered in this paper. Furthermore, information can be stored on the agricultural harvester 600 until the agricultural harvester 600 enters an area with wireless communication coverage. The agricultural harvester 600 itself can transmit the information to another network.

[0234] It will also be noted that Figure 2 The components or parts thereof can be mounted on a variety of different devices. One or more of these devices may include airborne computers, electronic control units, display units, servers, desktop computers, laptop computers, tablet computers, or other mobile devices such as PDAs, cellular phones, smartphones, multimedia players, personal digital assistants, etc.

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

[0236] Figure 15 This is a simplified block diagram illustrating a schematic example of a handheld or mobile computing device 16 that can be used as a user's or customer's handheld device 16, which may be deployed in this system (or as part thereof). For example, a mobile device may be deployed in the operator's cab of an agricultural harvester 100 for use in generating, processing, or displaying the diagrams discussed above. Figures 16 to 17 Examples are handheld or mobile devices.

[0237] Figure 15 A general block diagram of the components of client device 16, which can operate... Figure 2 The device 16 includes some of the components shown, interacts with them, or both. In device 16, a communication link 13 is provided that allows the handheld device to communicate with other computing devices, and in some examples, a channel is provided for automatically receiving information (e.g., by scanning). Examples of communication link 13 include those that allow communication via one or more communication protocols, such as wireless services for providing cellular access to a network, and protocols for providing local wireless connectivity to a network.

[0238] In other examples, the application can receive data on a removable Secure Digital (SD) card connected to interface 15. Interface 15 and communication link 13 communicate along bus 19 with processor 17 (which may also be a processor or server from another diagram), which is also connected to memory 21 and input / output (I / O) components 23, as well as clock 25 and position system 27.

[0239] In one example, I / O components 23 are provided to facilitate input and output operations. Various examples of I / O components 23 in device 16 may include input components (such as buttons, touch sensors, optical sensors, microphones, touchscreens, proximity sensors, accelerometers, orientation sensors) and output components (such as display devices, speaker and / or printer ports). Other I / O components 23 may also be used.

[0240] Clock 25 schematically includes a real-time clock component that outputs the time and date. Schematically, it may also provide timing functions for processor 17.

[0241] Location system 27 schematically includes components that output the current geographic location of device 16. This may include, for example, a Global Positioning System (GPS) receiver, a LoRAN system, a dead reckoning system, a cellular triangulation system, or other positioning systems. Location system 27 may also include, for example, mapping or navigation software that generates desired maps, navigation routes, and other geographic functions.

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

[0243] Figure 16 The illustration shows an example where device 16 is a tablet computer 600. Figure 16 In the diagram, computer 601 is shown as having a user interface display screen 602. Screen 602 can be a touchscreen or a pen-enabled interface that receives input from a pen or stylus. Tablet 600 can also utilize an on-screen virtual keyboard. Of course, computer 601 can also be attached to a keyboard or other user input device, for example, via a suitable attachment structure (such as a wireless link or USB port). Computer 601 can also schematically receive voice input.

[0244] Figure 17 Similar to Figure 16 The device is a smartphone 71. The smartphone 71 has a touch-sensitive display 73 that shows icons, blocks, or other user input mechanisms 75. Users can use the mechanisms 75 to run applications, make calls, perform data transfer operations, etc. Generally, smartphones 71 are built on a mobile operating system and offer more advanced computing power and connectivity than feature phones.

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

[0246] Figure 18 It is one of the deployable ones Figure 2 An example of a computing environment for the components. Reference Figure 18An example system for implementing some embodiments includes a computing device in the form of a computer 810 programmed to operate as discussed above. Components of the computer 810 may include, but are not limited to, a processing unit 820 (which may include a processor or server from the previous figures), system memory 830, and a system bus 821 that couples various system components, including the system memory, to the processing unit 820. The system bus 821 may be any of several types of bus architectures, including a memory bus or memory controller, a peripheral bus, and a local bus using any of various bus architectures. Regarding... Figure 2 The described memory and program can be deployed in Figure 18 In the corresponding part.

[0247] Computer 810 typically includes a variety of computer-readable media. Computer-readable media can be any available medium accessible to computer 810, and includes volatile and non-volatile media, removable and non-removable media. By way of example and not limitation, computer-readable media can include computer storage media and communication media. Computer storage media are distinct from and do not include modulated data signals or carriers. Computer-readable media include hardware storage media, including volatile and non-volatile, removable and non-removable media implemented in any way or by any technique for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical disc storage devices, magnetic tape, magnetic tape, disk storage devices or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to computer 810. Communication media can implement computer-readable instructions, data structures, program modules, or other data in a transmission mechanism, and includes any information delivery medium. The term "modulated data signal" refers to a signal that has one or more of its characteristics set or changed in a manner that encodes information in the signal.

[0248] System memory 830 includes computer storage media in the form of volatile and / or non-volatile memory or both, such as read-only memory (ROM) 831 and random access memory (RAM) 832. The basic input / output system 833 (BIOS) (which contains basic routines such as those that help transfer information between components within computer 810 during startup) is typically stored in ROM 831. RAM 832 typically contains data and / or program modules, or both, that are readily accessible to and / or currently being operated by processing unit 820. This is by way of example and not limitation. Figure 18 The operating system 834, application program 835, other program modules 836, and program data 837 are shown.

[0249] Computer 810 may also include other removable / non-removable volatile / non-volatile computer storage media. This is just one example. Figure 18 A hard disk drive 841 is shown that reads from or writes to a non-removable, non-volatile magnetic medium, an optical disk drive 855, and a non-volatile optical disk 856. The hard disk drive 841 is typically connected to the system bus 821 via a non-removable memory interface (such as interface 840), and the optical disk drive 855 is typically connected to the system bus 821 via a removable memory interface (such as interface 850).

[0250] Alternatively or additionally, the functions described herein may be performed at least in part by one or more hardware logic components. For example, but not limited to, illustrative types of hardware logic components that may be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (e.g., ASSPs), system-on-a-chip systems (SOCs), complex programmable logic devices (CPLDs), and the like.

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

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

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

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

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

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

[0257] A communication system that receives a map including crop humidity values ​​corresponding to different geographical locations in a field;

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

[0259] A field sensor that detects values ​​of agricultural characteristics corresponding to the geographical location;

[0260] A prediction map generator generates a functional prediction map of the field based on the crop humidity value in the map and the value of the agricultural feature, the functional prediction map mapping the predicted control values ​​to different geographical locations in the field;

[0261] Controllable subsystem; and

[0262] A control system that generates control signals to control the controllable subsystem based on the geographical location of the agricultural machinery and control values ​​in a functional predictive agricultural map.

[0263] Example 2 is any or all of the agricultural operating machines of the foregoing examples, wherein the map is a predicted crop moisture map generated based on values ​​from a priori maps and crop moisture values ​​detected in the field.

[0264] Example 3 is any or all of the agricultural operating machines of the foregoing examples, wherein the prediction map generator includes:

[0265] A predictive agricultural feature map generator generates a functional predictive agricultural feature map as the functional predictive agricultural map, which maps the predicted values ​​of agricultural features to different geographical locations in the field.

[0266] Example 4 is any or all of the agricultural operating machines of the foregoing examples, wherein the field sensors detect the value of an operator command that instructs the agricultural operating machine to perform a command action, the value of the operator command being used as a value of an agricultural characteristic.

[0267] Example 5 is any or all of the agricultural operating machines of the foregoing examples, wherein the prediction map generator includes:

[0268] The predictive operator command map generates a functional predictive operator command map as a functional predictive agriculture map, which maps predictive operator command values ​​to different geographical locations in the field.

[0269] Example 6 is any or all of the agricultural operating machines of the foregoing examples, wherein the control system includes:

[0270] The controller is configured to generate operator command control signals that indicate operator commands based on the detected geographical location and the function prediction operator command map, and to control the controllable subsystem to execute the operator commands based on the operator command control signals.

[0271] Example 7 is any or all of the agricultural operating machines of the foregoing examples, wherein the operator command control signal controls the control subsystem to adjust the feed rate of material through the agricultural operating machine.

[0272] Example 8 is any or all of the agricultural operating machines of the foregoing examples, and also includes:

[0273] A prediction model generator generates a predictive agriculture model that models the relationship between crop humidity and agricultural features based on crop humidity values ​​in the map at the geographic location and agricultural feature values ​​detected by the field sensors corresponding to the geographic location. The prediction map generator generates the functional predictive agriculture map based on crop humidity values ​​in the map and the predictive agriculture model.

[0274] Example 9 is any or all of the agricultural operating machines of the foregoing examples, wherein the control system further includes:

[0275] An operator interface controller generates a user interface representation of a functional predictive agriculture map, the user interface representation including a field portion having one or more indicators indicating predictive control values ​​at one or more geographic locations on the field portion.

[0276] Example 10 is any or all of the agricultural operating machines of the foregoing examples, wherein the operator interface controller generates a user interface diagram representation to include an interactive display portion that displays a value display portion indicating a selected value, an interactive threshold display portion that indicates an action threshold, and an interactive action display portion that indicates a control action to be taken when one of the predicted control values ​​satisfies an action threshold related to the selected value, and the control system generates control signals based on the control actions to control a controllable subsystem.

[0277] Example 11 is a computer-implemented method for controlling agricultural machinery, comprising:

[0278] Obtain a graph that includes crop moisture values ​​corresponding to different geographical locations in the field;

[0279] Detecting the geographical location of agricultural machinery;

[0280] Utilize field sensors to detect agricultural characteristics corresponding to geographical locations;

[0281] Based on crop moisture values ​​in the map and agricultural feature values, a functional predictive agriculture map of the field is generated, mapping predicted control values ​​to different geographical locations within the field; and

[0282] The controllable subsystem is controlled based on the geographical location of agricultural machinery and the control values ​​in the functional predictive agricultural map.

[0283] Example 12 is a computer-implemented method of any or all of the foregoing examples, wherein obtaining the graph includes:

[0284] A predicted crop humidity map is obtained, which includes predicted values ​​of crop humidity corresponding to different geographical locations in the field, and these predicted values ​​are used as the values ​​of crop humidity.

[0285] Example 13 is a computer-implemented method of any or all of the foregoing examples, wherein generating the functional predictive agricultural map includes:

[0286] A functional predictive agricultural feature map is generated, which maps predicted agricultural feature values ​​as predictive control values ​​to different geographical locations in the field.

[0287] Example 14 is a computer-implemented method of any or all of the foregoing examples, wherein detecting values ​​of agricultural features using field sensors includes:

[0288] Operator commands that indicate the actions of agricultural machinery are detected using field sensors, and these operator commands are used as values ​​of agricultural characteristics.

[0289] Example 15 is a computer-implemented method of any or all of the foregoing examples, wherein generating the functional predictive agricultural map includes:

[0290] A function prediction operator command map is generated, which maps the prediction operator command values ​​as prediction control values ​​to different geographical locations in the field.

[0291] Example 16 is a computer-implemented method of any or all of the foregoing examples, wherein controlling the controllable subsystem includes:

[0292] Based on the detected geographic location and the predicted operator command map, an operator command control signal is generated to instruct the operator's commands; and

[0293] The controllable subsystem is controlled based on operator command control signals to execute operator commands.

[0294] Example 17 is a computer-implemented method of any or all of the foregoing examples, wherein controlling a controllable subsystem to execute operator commands based on operator command control signals includes:

[0295] Controllable subsystems are used to adjust the feed rate of materials through agricultural machinery.

[0296] Example 18 is a computer-implemented method of any or all of the foregoing examples, and also includes:

[0297] A predictive agriculture model is generated based on the crop humidity value in the map at a geographic location and the values ​​of agricultural features corresponding to that geographic location detected by field sensors. The generation of the functional predictive agriculture map includes generating the functional predictive agriculture map based on the crop humidity value in the map and the predictive agriculture model.

[0298] Example 19 is an agricultural operating machine, comprising:

[0299] A communication system that receives a map including crop humidity values ​​corresponding to different geographical locations in a field;

[0300] A geolocation sensor that detects values ​​of agricultural features corresponding to a geographical location;

[0301] A predictive model generator generates a predictive agricultural model that models the relationship between crop humidity and agricultural features based on crop humidity values ​​in the map at the geographic location and agricultural feature values ​​detected by the field sensors corresponding to the geographic location.

[0302] A prediction map generator generates a functional predictive agriculture map of the field that maps predicted control values ​​to different geographical locations in the field, based on crop humidity values ​​in the map and the predictive agriculture model.

[0303] Controllable subsystem; and

[0304] A control system that generates control signals to control the controllable subsystem based on the geographical location of the agricultural machinery and control values ​​in the functional prediction agricultural map.

[0305] Example 20 is any or all of the agricultural operating machines of the foregoing examples, wherein the control system includes at least one of the following:

[0306] A feed rate controller generates a feed rate control signal based on the detected geographic location and functional predictive agricultural map, and controls a controllable subsystem based on the feed rate control signal to control the feed rate of material through the agricultural operation machine.

[0307] The system configures a controller that generates speed control signals based on detected geographic locations and functional predictive agricultural maps, and controls a controllable subsystem based on these signals to control the speed of agricultural machinery.

[0308] A header controller that generates header control signals based on detected geographic location and functional predictive agricultural maps, and controls a controllable subsystem based on these signals to control the header on the agricultural machinery; and

[0309] The controller is configured to generate operator command control signals that indicate operator commands based on the detected geographic location and functional predictive agricultural map, and to control the controllable subsystem to execute the operator commands based on the operator command control signals.

[0310] Although the subject matter has been described in language specific to structural features or methodological actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are disclosed as examples of the claims.

Claims

1. An agricultural operating machine (100), comprising: A communication system (206) receives a crop humidity map as a first map, the crop humidity map including a map of crop humidity values ​​corresponding to different geographical locations in the field; A geolocation sensor (204) detects the geolocation of the agricultural machinery; A field sensor (208) detects values ​​of agricultural features corresponding to a first geographical location; A prediction map generator (212) generates a functional prediction agricultural map of the field as a second map based on a first value of crop humidity in an agricultural humidity map at a first geographic location, a detection value of agricultural features corresponding to the first geographic location, and a second value of crop humidity in a crop humidity map at a second geographic location. The functional prediction agricultural map maps the predicted values ​​of agricultural features to the second geographic location in the field. Controllable subsystem (216); and The control system (214) generates control signals to control the controllable subsystem (216) based on the geographical location of the agricultural machine (100) and based on the predicted values ​​of agricultural features in the functional prediction agricultural map.

2. The agricultural machinery according to claim 1, wherein, The graph is a predicted crop humidity map generated based on values ​​from agricultural humidity maps and crop humidity values ​​detected on-site.

3. The agricultural machinery according to claim 1, wherein, The field sensors detect the value of the operator command corresponding to the command action of the agricultural machinery at the first geographical location, and the value of the operator command serves as the value of the agricultural characteristic.

4. The agricultural machinery according to claim 3, wherein, The prediction map generator includes: A predictive operator command map generator generates a functional predictive operator command map as a functional predictive agricultural map based on a first value of crop humidity in an agricultural humidity map at a first geographic location, a detection value of an operator command corresponding to the first geographic location, and a second value of crop humidity in a crop humidity map at a second geographic location. The functional predictive operator command map maps the predicted values ​​of operator commands, which are predicted values ​​of agricultural features, to the second geographic location.

5. The agricultural machinery according to claim 4, wherein, The control system includes: The controller is configured to generate operator command control signals that indicate operator commands based on the geographical location of the agricultural machinery and the predicted values ​​of operator commands in the function prediction operator command diagram, and to control the controllable subsystem to execute the operator commands based on the operator command control signals.

6. The agricultural machinery according to claim 5, wherein, The operator command control signal controls the controllable subsystem to adjust the feeding speed of material through the agricultural machine.

7. The agricultural machinery according to claim 1, further comprising: A prediction model generator generates a predictive agricultural model that models the relationship between crop humidity and agricultural features based on a first value of crop humidity in the crop humidity map at a first geographical location and a detection value of an agricultural feature corresponding to the first geographical location. The prediction map generator generates the functional predictive agricultural map based on a second value of crop humidity in the crop humidity map at a second geographical location and the predictive agricultural model.

8. A computer-implemented method for controlling agricultural machinery (100), comprising: Obtain a crop humidity map (258) that includes crop humidity values ​​corresponding to different geographical locations in the field; Detect the geographical location of the agricultural machinery (100); The values ​​of agricultural features corresponding to the first geographical location are detected using field sensors (208); Based on the first value of crop humidity in the crop humidity map at the first geographical location, the detected value of the agricultural feature corresponding to the first geographical location, and the second value of the operational humidity in the operational humidity map at the second geographical location, a functional predictive agricultural map of the field is generated, which maps the predicted value of the agricultural feature to the second geographical location in the field. as well as The controllable subsystem (216) is controlled based on the geographical location of the agricultural machine (100) and the functional prediction agricultural map.

9. An agricultural operating machine (100), comprising: A communication system (206) receives a crop humidity map including crop humidity values ​​corresponding to different geographical locations in the field, wherein the crop humidity values ​​represent the humidity content of crops in the field; A geolocation sensor (204) detects the geolocation of agricultural machinery in the field; A field sensor (208) detects values ​​of agricultural features corresponding to a geographical location; A prediction model generator (210) generates a prediction agricultural model based on the crop humidity value in the crop humidity map at the geographic location and the detection value of the agricultural feature corresponding to the geographic location. The prediction agricultural model models the relationship between the crop humidity and the agricultural feature. A prediction map generator (212) generates a functional predictive agriculture map of the field based on the crop humidity value in the map and the predictive agriculture model, the functional predictive agriculture map mapping the predictive control values ​​to different geographical locations in the field; Controllable subsystem (216); and A control system (214) generates control signals to control the controllable subsystem (216) based on the geographical location of the agricultural machine (100) and the control values ​​in the functional prediction agricultural map.

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