Predictive Power Map Generation and Control Systems

Abstract

One or more information maps are obtained by an agricultural machine. The one or more information maps map one or more agricultural characteristic values ​​at different geographic locations in a field. Field sensors on the agricultural machine sense the agricultural characteristics as the agricultural machine moves through the field. A prediction map generator generates a prediction map based on the relationship between the values ​​in the one or more information maps and the agricultural characteristics sensed by the field sensors. The prediction map predicts the agricultural characteristics at different locations in the field. The prediction map can be output and used for automatic machine control.

Description

Technical Field

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

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

[0003] Agricultural harvesters typically include an engine or other power source that generates a limited amount of power that is provided to a variety of different subsystems of the agricultural harvester. Maintaining efficient power distribution from the limited amount of power to the various subsystems across changing field conditions is difficult.

[0004] The above discussion is provided as general background information only and is not intended to be used as an aid in determining the scope of the claimed subject matter. Summary of the Invention

[0005] One or more information maps are obtained by an agricultural machine. The one or more information maps map one or more agricultural characteristic values ​​at different geographic locations in a field. As the agricultural machine moves through the field, field sensors on the agricultural machine sense the agricultural characteristics. A prediction map generator generates a prediction map that predicts the agricultural characteristics at different locations in the field based on the relationship between the values ​​in the one or more information maps and the agricultural characteristics sensed by the field sensors. The prediction map can be output and used for automatic machine control.

[0006] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to examples that address any or all of the shortcomings noted in the background. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 is a partially illustrative, partially schematic illustration of an example of a combine harvester.

[0008] Figure 2 is a block diagram illustrating some portions of an agricultural harvester in greater detail according to some examples of the present disclosure.

[0009] Figures 3A to 3B A flow chart illustrating an example of the operation of an agricultural harvester in generating a map is shown.

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

[0011] Figure 5 is a flow chart illustrating an example of the operation of an agricultural harvester in receiving vegetation index, crop moisture, soil properties, topography, yield, weed or biomass maps, detecting power characteristics, and generating a functional predicted power map for use in controlling the agricultural harvester during harvesting operations.

[0012] Figure 6 is a block diagram illustrating one example of an agricultural harvester in communication with a remote server environment.

[0013] Figures 7 to 9 An example of a mobile device that may be used with an agricultural harvester is shown.

[0014] Figure 10 is a block diagram illustrating one example of a computing environment that may be used with an agricultural harvester and the architecture shown in the preceding figures. DETAILED DESCRIPTION

[0015] To facilitate understanding of the principles of the present disclosure, reference will now be made to the examples shown in the accompanying drawings, and specific language will be used to describe them. However, it will be understood that this is not intended to limit the scope of the present disclosure. Any changes and further modifications to the described devices, systems, methods, and any further applications of the principles of the present disclosure are fully contemplated, as would be generally contemplated by one skilled in the art. In particular, it is fully contemplated that the features, components, and / or steps described with respect to one example may be combined with the features, components, and / or steps described with respect to other examples of the present disclosure.

[0016] This specification relates to using field data acquired concurrently with agricultural operations in combination with predicted or a priori data to generate predictive maps, and more specifically, predictive power maps. In some examples, the predictive power maps can be used to control agricultural machines (e.g., agricultural harvesters). As discussed above, the power generation of a harvester has a finite limit, and overall performance may deteriorate when one or more subsystems have increased power demands.

[0017] The performance of a harvester can be adversely affected based on a variety of different criteria. For example, areas with dense crop plants, weeds, or a combination thereof can have a detrimental effect on the operation of the harvester because the subsystems require more power to handle larger amounts of material, including crop plants and weeds. A vegetation index can indicate areas where dense crop plants, weeds, or a combination thereof may be present. Or, for example, crop plants or weeds with higher moisture content also require more power to handle. Or, for example, soil properties (such as type or moisture) can affect the power usage of the steering and propulsion systems. For example, wet clay-type soils can cause additional slip compared to dry soils, which can reduce the efficiency of the drivetrain. Or, for example, the topography of a field can change the power characteristics of an agricultural harvester. For example, when a harvester climbs a slope, some power needs to be transferred to the propulsion system to maintain a constant speed. Or, for example, areas of a field with higher grain yields may need to transfer more power to the crop handling subsystem. Or, for example, areas of a field containing large amounts of biomass may need to transfer more power to the crop handling subsystem.

[0018] Some current systems provide vegetation index maps. The vegetation index map illustratively maps vegetation index values ​​(which can indicate vegetation growth) across different geographic locations in a field of interest. One example of a vegetation index includes the Normalized Difference Vegetation Index (NDVI). Many other vegetation indices exist within the scope of the present disclosure. In some examples, the vegetation index can be derived from sensor readings of one or more bands of electromagnetic radiation reflected by vegetation. Without limitation, these bands of electromagnetic radiation can be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum.

[0019] Vegetation index map can be used to identify the existence and position of vegetation. In some examples, these maps make it possible to identify and georeference weeds in the presence of bare soil, crop residues or other plants including crops or other weeds. For example, at the end of the growing season, when crops mature, crop plants may show relatively low levels of living and growing vegetation. However, weeds still remain in a growing state after the crops mature usually. Therefore, if a vegetation index map is generated at a relatively late stage in the growing season, the vegetation index map can indicate the position of weeds in the field.

[0020] Some current systems provide crop moisture maps. The crop moisture map illustratively maps crop moisture at different geographic locations across a field of interest. In one example, crop moisture can be sensed prior to harvesting operations using an unmanned aerial vehicle (UAV) equipped with a moisture sensor. As the UAV travels across the field, the crop moisture readings are geolocated to create a crop moisture map. This is merely an example, and crop moisture maps can also be created in other ways. For example, crop moisture can be predicted across the entire field based on precipitation, soil moisture, or a combination thereof.

[0021] Some current systems provide topographic maps. Topographic maps illustratively map the height or other topographical features of the ground across different geographical locations in a field of interest. Since the slope of the ground indicates a change in height, having two or more height values ​​allows the slope to be calculated across an area with known height values. Slopes of greater granularity can be achieved by having more areas with known height values. As an agricultural harvester travels across the terrain in a known direction, the pitch and roll of the agricultural harvester can be determined based on the slope of the ground (i.e., the area of ​​height change). The topographical features mentioned below may include, but are not limited to, height, slope (e.g., including the machine orientation relative to the slope), and ground contour (e.g., roughness).

[0022] The soil property map illustratively maps soil property values ​​(which can indicate soil type, soil moisture, soil cover, soil structure, and a variety of other soil properties) for different geographical locations across the field of interest. Therefore, the soil property map provides a geo-referenced soil property across the field of interest. Soil type can refer to a classification unit in soil science, wherein each soil type includes a limited shared property set. Soil type can include, for example, sandy soil, clay soil, silt soil, peat soil, chalky soil, loamy soil, and a variety of other soil types. Soil moisture can refer to the amount of water retained or otherwise included in the soil. Soil moisture can also be referred to as soil wetness. Soil cover can refer to the amount of items or materials covering the soil, including vegetation material, such as crop residues or cover crops, residues, and a variety of other items or materials. Typically, in agricultural terms, soil cover includes the measurement of remaining crop residues (for example, the remaining amount of plant stems) and the measurement of cover crops. Soil structure can refer to the arrangement of the solid parts of the soil and the pore spaces between the solid parts of the soil. Soil structure can include the way in which individual particles (such as individual particles of sand, silt, and clay) are arranged. Soil structure can be described in terms of grade (degree of aggregation), category (average size of aggregates), and form (type of aggregates), as well as a variety of other descriptions. These are merely examples. A variety of other characteristics and properties of soil can be mapped as soil property values ​​on a soil property map.

[0023] These soil property maps can be generated based on data collected during another operation corresponding to the field of interest, for example, a previous agricultural operation in the same season (such as a planting operation or a spraying operation) and a previous agricultural operation performed in a past season (e.g., a previous harvesting operation). The agricultural machine performing those agricultural operations can have onboard sensors that detect characteristics indicative of soil properties, such as characteristics indicative of soil type, soil moisture, soil cover, soil structure, and a variety of other characteristics indicative of a variety of other soil properties. In addition, operating characteristics or machine settings of the agricultural machine during the previous operation can be used to generate the soil property map along with other data. For example, header height data indicating the height of the header of an agricultural harvester during the previous harvesting operation at different geographical locations across the field of interest, together with weather data indicating weather conditions (e.g., precipitation data or wind data during an intermittent period (e.g., a period starting from the previous harvesting operation and the time when the soil property map was generated)) can be used to generate the soil moisture map. For example, by knowing the height of the header, the amount of remaining plant residue (eg, crop straw) can be known or estimated, and together with precipitation data, soil moisture levels can be predicted. This is merely an example.

[0024] This discussion also includes predictive maps that predict characteristics based on information maps and relationships with field sensors. Two of these maps include predicted yield maps and predicted biomass maps. In one example, a predicted yield map is generated by receiving a priori vegetation index map and sensing yield during a harvesting operation, and determining the relationship between the priori vegetation index map and the yield sensor signal, and using the relationship to generate a predicted yield map based on the relationship and the priori vegetation index map. In one example, a predicted biomass map is generated by receiving a priori vegetation index map and sensing biomass, and determining the relationship between the priori vegetation index map and the biomass sensor signal, and using the relationship to generate a predicted biomass map based on the relationship and the priori vegetation index map. Predicted yield maps and predicted biomass maps can also be created or otherwise generated based on other information maps. For example, predicted yield maps and predicted biomass maps can be generated based on satellite data or growth models.

[0025] Therefore, this discussion is conducted with respect to an example in which a system receives one or more of a vegetation index map, a weed map, a crop moisture map, a soil property map, a topographic map, a predicted yield map, or a predicted biomass map, and also uses field sensors to detect variables indicative of crop status during a harvesting operation. The system generates a model that models the relationship between the vegetation index values, crop moisture values, soil property values, predicted yield values, or predicted biomass values ​​from the map and field data from the field sensors. The model is used to generate a functional predicted power map that predicts the expected power characteristics of an agricultural harvester in the field. The functional predicted power characteristics map generated during the harvesting operation can be presented to an operator or other user and / or used to automatically control the agricultural harvester during the harvesting operation.

[0026] Figure 1 is a partially illustrative, partially schematic illustration of a self-propelled agricultural harvester 100. In the example shown, the agricultural harvester 100 is a combine harvester. Furthermore, although a combine harvester is provided as an example throughout this disclosure, it will be understood that this specification is also applicable to other types of harvesters, such as cotton harvesters, sugarcane harvesters, self-propelled forage harvesters, reapers, or other agricultural work machines. Therefore, the present disclosure is intended to cover the various types of harvesters described and is therefore not limited to combine harvesters. Furthermore, the present disclosure relates to other types of work machines, such as agricultural seeders and sprayers, construction equipment, forestry equipment, and turf management equipment to which the generation of prediction maps is applicable. Therefore, the present disclosure is intended to cover these various types of harvesters and other work machines and is therefore not limited to combine harvesters.

[0027] like Figure 1As shown, the agricultural harvester 100 illustratively includes an operator cabin 101 that can have a variety of 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 cutterhead, generally indicated at 104. The agricultural harvester 100 also includes a feeder housing 106, a feed accelerator 108, and a thresher, generally indicated at 110. The feeder housing 106 and the feed accelerator 108 form part of a material handling subsystem 125. The header 102 is pivotally coupled to a frame 103 of the agricultural harvester 100 along a pivot axis 105. One or more actuators 107 drive the header 102 to move about the axis 105 in a direction generally indicated by arrow 109. Thus, the vertical position (header height) of the header 102 above the ground 111 on which the header 102 travels can be controlled by actuating the actuator 107. Figure 1 Although not shown, the agricultural harvester 100 may also include one or more actuators operable to apply a pitch angle, a roll angle, or both to the header 102 or portions of the header 102. Pitch refers to the angle at which the cutterheads 104 engage the crop. For example, the pitch angle can be increased by controlling the header 102 so that the distal edge 113 of the cutterheads 104 points more toward the ground. The pitch angle can be decreased by controlling the header 102 so that the distal edge 113 of the cutterheads 104 points further away from the ground. The roll angle refers to the orientation of the header 102 about the front-to-back longitudinal axis of the agricultural harvester 100.

[0028] The thresher 110 illustratively includes a threshing drum 112 and a set of concave plates 114. Furthermore, the agricultural harvester 100 includes a separator 116. The agricultural harvester 100 also includes a grain cleaning subsystem or a grain cleaning chamber 118 (collectively, the grain cleaning subsystem 118), which includes a grain cleaning fan 120, a chaff screen 122, and a screen 124. The material handling subsystem 125 also includes a discharge agitator 126, a debris elevator 128, a clean grain elevator 130, and an unloading auger 134 and a spout 136. The clean grain elevator moves clean grain into a clean grain bin 132. The agricultural harvester 100 also includes a residue subsystem 138, which may include a chopper 140 and a spreader 142. The agricultural harvester 100 also includes a propulsion subsystem, which includes an engine that drives a ground engaging assembly 144 (e.g., wheels or tracks). In some examples, a combine harvester within the scope of the present disclosure may have more than one of any of the subsystems described above. In some examples, the agricultural harvester 100 may have Figure 1 The left and right grain cleaning subsystems, separators, etc. are not shown.

[0029] In operation, as an overview, the agricultural harvester 100 moves across a field, illustratively in the direction indicated by arrow 147. As the agricultural harvester 100 moves, the header 102 (and associated reel 164) engages the crop to be harvested and gathers the crop toward the cutter 104. The operator of the agricultural harvester 100 can be a local human operator, a remote human operator, or an automated system. The operator of the agricultural harvester 100 can determine one or more of a height setting, a pitch setting, or a roll setting for the header 102. For example, the operator inputs one or more settings to a control system (described in more detail below) that controls the actuators 107. The control system can also receive settings from the operator for establishing the pitch and roll angles of the header 102 and implement the input settings by controlling associated actuators (not shown) that operate to change the pitch and roll angles of the header 102. Actuator 107 maintains header 102 at a height above ground 111 based on the height setting, and, where applicable, at a desired pitch and roll angle. Each of the height setting, roll setting, and pitch setting can be implemented independently of the other settings. The control system responds to header errors (e.g., the difference between the height setting and the measured height of header 104 above ground 111, and in some cases, pitch and roll angle errors) with a responsiveness determined based on the selected sensitivity level. If the sensitivity level is set to a higher sensitivity level, the control system responds to smaller header position errors and attempts to reduce the detected errors more quickly than if the sensitivity level is set to a lower sensitivity level.

[0030] Returning to the description of the operation of agricultural harvester 100, after the crop is cut by cutter 104, the cut crop material is moved in feeder housing 106 via a conveyor toward feed accelerator 108, which accelerates the crop material into thresher 110. The crop is threshed by rotating drum 112, which forces the crop material against concave plate 114. Separator drums move the threshed crop in separator 116, where discharge agitator 126 moves a portion of the residue toward residue subsystem 138. The portion of residue delivered to residue subsystem 138 is chopped by residue chopper 140 and spread over the field by spreader 142. In other configurations, the residue is discharged from agricultural harvester 100 in a pile. In other examples, residue subsystem 138 may include a seed rejector (not shown), such as a seed bagger or other seed collector, or a seed crusher or other seed crusher.

[0031] The grain falls into the cleaning subsystem 118. A chaff screen 122 separates some larger material from the grain, and a screen 124 separates some fine material from the clean grain. The clean grain falls onto an auger that moves the grain to the inlet end of a clean grain elevator 130. The clean grain elevator 130 moves the clean grain upward, depositing it in a clean grain bin 132. The airflow generated by the cleaning fan 120 removes residue from the cleaning subsystem 118. The cleaning fan 120 directs air upward along an airflow path through the screen and chaff screen. The airflow transports the residue backward within the agricultural harvester 100 toward the residue handling subsystem 138.

[0032] The debris elevator 128 returns the debris to the threshing machine 110 where it is re-threshed. Alternatively, the debris can also be transferred by the debris elevator or another transport device to a separate re-threshing mechanism where it is also re-threshed.

[0033] Figure 1 Also shown, in one example, the agricultural harvester 100 includes a ground speed sensor 146, one or more separator loss sensors 148, a clean grain camera 150, a forward-looking image capture mechanism 151 (which can be in the form of a stereo camera or a monocular camera), and one or more loss sensors 152 disposed in the grain cleaning subsystem 118.

[0034] The ground speed sensor 146 senses the speed at which the agricultural harvester 100 is traveling over the ground. The ground speed sensor 146 can sense the speed of travel of the agricultural harvester 100 by sensing the rotational speed of a ground engaging component (e.g., a wheel or track), a drive shaft, an axle, or other component. In some cases, travel speed can be sensed using a positioning system, such as a global positioning system (GPS), a dead reckoning system, a long-range navigation (LORAN) system, or various other systems or sensors that provide an indication of travel speed.

[0035] Loss sensors 152 illustratively provide output signals indicating the amount of grain loss occurring in both the right and left sides of cleaning subsystem 118. In some examples, sensors 152 are impact sensors that count grain impacts per unit time or per unit distance traveled to provide an indication of grain loss occurring in cleaning subsystem 118. The impact sensors on the right and left sides of cleaning subsystem 118 can provide separate signals or a combined or aggregated signal. In some examples, rather than providing separate sensors for each cleaning subsystem 118, sensor 152 can comprise a single sensor.

[0036] The splitter loss sensor 148 provides an indication of the left and right splitters ( Figure 1The separator loss sensor 148 may be associated with the left and right separators and may provide separate grain loss signals or a combined or aggregated signal. In some cases, various types of sensors may be used to sense grain loss in the separators.

[0037] The agricultural harvester 100 may also include other sensors and measurement 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 the oscillation or bouncing (and amplitude) of the agricultural harvester 100; a residue setting sensor that is configured to sense whether the agricultural harvester 100 is configured to chop residue, pile, etc.; a cleaning chamber fan speed sensor that senses the speed of the fan 120; a concave plate gap sensor that senses the gap between the drum 112 and the concave plate 114; a threshing drum speed sensor that senses the roller speed of the drum 112 speed; a chaff screen gap sensor that senses the size of the opening in the chaff screen 122; a screen gap sensor that senses the size of the opening in the screen 124; a material other than grain (MOG) moisture sensor that senses the moisture level of the MOG passing through the agricultural harvester 100; one or more machine setting sensors that are configured to sense various configurable settings of the agricultural harvester 100; a machine orientation sensor that senses the orientation of the agricultural harvester 100; and a crop property sensor that senses various types of crop properties, such as crop type, crop moisture, and other crop properties. When the agricultural harvester 100 is processing crop material, the crop property sensor can also be configured to sense characteristics of the cut crop material. For example, in some cases, the crop property sensor can sense: grain quality, such as broken grain, MOG level; grain composition, such as starch and protein; and grain feed rate as the grain passes through the feeder housing 106, the clean grain elevator 130, or elsewhere in the agricultural harvester 100. The crop property sensor may also sense the feed rate of biomass through the feeder housing 106, separator 116, or elsewhere in the agricultural harvester 100. The crop property sensor may also sense the feed rate as a mass flow rate of grain through the elevator 130 or through other portions of the agricultural harvester 100, or provide other output signals indicative of other sensed variables.

[0038] Examples of sensors used to detect or sense power characteristics include, but are not limited to, voltage sensors, current sensors, torque sensors, fluid pressure sensors, fluid flow sensors, force sensors, bearing load sensors, and rotation sensors. Power characteristics can be measured at varying levels of granularity. For example, power usage can be sensed machine-wide, subsystem-wide, or by individual components of a subsystem.

[0039] Before describing how the agricultural harvester 100 generates a functional predicted power map and uses the functional predicted power map for control, a brief description of some items on the agricultural harvester 100 and their operation will first be described. Figure 2 、 Figure 3A and Figure 3B The mapping method described herein receives a general type of information map and combines the information from the information map with georeferenced sensor signals generated by field sensors, where the sensor signals indicate characteristics of a field, such as the power characteristics of an agricultural harvester. The field characteristics may include, but are not limited to: characteristics of the field, such as slope, weed density, weed type, soil moisture, and surface quality; characteristics of crop properties, such as crop height, crop moisture, crop density, and crop state; characteristics of grain properties, such as grain moisture, grain size, and grain test weight; and characteristics of machine performance, such as loss level, work quality, fuel consumption, and power utilization. A relationship between characteristic values ​​obtained from the field sensor signals and the information map values ​​is identified and used to generate a new functional prediction map. The functional prediction map predicts values ​​at different geographic locations in the field, and one or more of those values ​​can be used to control a machine, such as one or more subsystems of an agricultural harvester. In some cases, the functional prediction map can be presented to a user, such as an operator of an agricultural work machine, which may be an agricultural harvester. The functional prediction map can be presented to the user visually (e.g., via a display), tactilely, or aurally. The user can interact with the functional prediction map to perform editing operations and other user interface operations. In some cases, the functional prediction map can be used to control an agricultural machine (e.g., an agricultural harvester), presented to an operator or other user, and presented to the operator or user for operator or user interaction.

[0040] In reference Figure 2 、 Figure 3A and Figure 3B After describing the general method, refer to Figure 4 and Figure 5 A more specific method is described for generating a functional predictive power map that can be presented to an operator or user and / or used to control the agricultural harvester 100. Also, while this discussion is directed to agricultural harvesters, and specifically, combine harvesters, the scope of this disclosure encompasses other types of agricultural harvesters or other agricultural working machines.

[0041] Figure 2 is a block diagram illustrating portions of an example agricultural harvester 100 . Figure 2The agricultural harvester 100 is shown as illustratively 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 for sensing one or more agricultural characteristics of a field concurrently with harvesting operations. Agricultural characteristics may include any characteristics that may have an impact on harvesting operations. Some examples of agricultural characteristics include characteristics of the harvesting machine, the field, the plants on the field, and the weather. Other types of agricultural characteristics are also included. Agricultural characteristics may include any characteristics that may have an impact on harvesting operations. Some examples of agricultural characteristics include characteristics of the harvesting machine, the field, the plants on the field, the weather, etc. The field sensors 208 generate values ​​corresponding to the sensed characteristics. The agricultural harvester 100 also includes a prediction model or relationship generator (hereinafter collectively referred to as the "prediction model generator 210"), a prediction map generator 212, a control zone generator 213, a control system 214, one or more controllable subsystems 216, and an operator interface mechanism 218. The agricultural harvester 100 may also include various other agricultural harvester functions 220. For example, the field sensors 208 include onboard sensors 222, remote sensors 224, and other sensors 226 that sense characteristics of the field during agricultural operations. The predictive model generator 210 illustratively includes an information variable to field variable model generator 228, and the predictive model generator 210 may include other items 230. The control system 214 includes a communication system controller 229, an operator interface controller 231, a settings controller 232, a path planning controller 234, a feed rate controller 236, a header and reel controller 238, a draper belt controller 240, a cover position controller 242, a residue system controller 244, a machine cleaning controller 245, a zone controller 247, and the system 214 may include other items 246. Controllable subsystems 216 include machine and header actuators 248 , a propulsion subsystem 250 , a steering subsystem 252 , a residue subsystem 138 , a machine cleanup subsystem 254 , and subsystems 216 may include various other subsystems 256 .

[0042] Figure 2 The agricultural harvester 100 is also shown to receive an information map 258. As described below, the information map 258 may include, for example, a vegetation index map or a vegetation map from a previous or prior operation. However, the prior information map 258 may also include other types of data obtained prior to the harvesting operation or maps from a prior or previous operation. Figure 2Also shown is an operator 260 that can operate the agricultural harvester 100. The operator 260 interacts with the operator interface mechanism 218. In some examples, the operator interface mechanism 218 may include a rocker, a joystick, a steering wheel, a connecting rod, a pedal, a button, a dial, a keypad, a user-actuated element (e.g., an icon, a button, etc.) on a user interface display device, a microphone and a speaker (wherein voice recognition and voice synthesis are provided), and various other types of control devices. In the case of a touch-sensitive display system, the operator 260 can utilize touch gestures to interact with the operator interface mechanism 218. These examples are provided as illustrative examples and are not intended to limit the scope of the present disclosure. Therefore, other types of operator interface mechanisms 218 may also be used and are within the scope of the present disclosure.

[0043] The 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 can be a cellular communication system, a system that communicates via a wide area network or a local area network, a system that communicates via a near field communication network, or a communication system configured to communicate via any of a variety of other networks or a combination of networks. The communication system 206 can also include a system that facilitates downloading or transferring information to or from a secure digital (SD) card or a universal serial bus (USB) card, or both.

[0044] The geolocation sensor 204 illustratively senses or detects the geographic location or orientation of the agricultural harvester 100. The geolocation sensor 204 may include, but is not limited to, a global navigation satellite system (GNSS) receiver that receives signals from GNSS satellite transmitters. The geolocation sensor 204 may also include a real-time kinematic (RTK) component configured to enhance the accuracy of position data derived from the GNSS signals. The geolocation sensor 204 may include a dead reckoning system, a cellular triangulation system, or any of a variety of other geolocation sensors.

[0045] The field sensor 208 may be the one referenced above. Figure 1 Any sensor described. Field sensors 208 include onboard sensors 222 mounted on the agricultural harvester 100. For example, these sensors may include perception sensors (e.g., forward-looking monocular or stereo camera systems and image processing systems), image sensors within the agricultural harvester 100 (e.g., clean grain cameras, or cameras mounted to identify weed seeds exiting the agricultural harvester 100 through the residue subsystem or from the grain cleaning subsystem). Field sensors 208 also include remote field sensors 224 that capture field information. Field data includes data acquired from sensors on the harvester or data acquired by any sensor that detects data during harvesting operations.

[0046] Predictive model generator 210 generates a model indicating the relationship between values ​​sensed by field sensors 208 and metrics mapped to the field via information graph 258. For example, if information graph 258 maps vegetation index values ​​to different locations in the field, and field sensors 208 are sensing values ​​indicating header power usage, prior information variable pair field variable model generator 228 generates a predictive power model that models the relationship between vegetation index values ​​and header power usage values. A predictive power model may also be generated based on vegetation index values ​​from information graph 258 and multiple field data values ​​generated by field sensors 208. Based on information graph 258, predictive graph generator 212 then uses the predictive power model generated by predictive model generator 210 to generate a functional predictive power graph that predicts the values ​​of power characteristics (e.g., subsystem power usage) sensed by field sensors 208 at different locations in the field.

[0047] In some examples, the type of values ​​in functional prediction graph 263 may be the same as the type of field data sensed by field sensor 208. In some cases, the type of values ​​in functional prediction graph 263 may have different units than the data sensed by field sensor 208. In some examples, the type of values ​​in functional prediction graph 263 may be different from the type of data sensed by field sensor 208, but may have a relationship to the type of data sensed by field sensor 208. For example, in some examples, the type of data sensed by field sensor 208 may indicate the type of values ​​in functional prediction graph 263. In some examples, the type of data in functional prediction graph 263 may be different from the type of data in information graph 258. In some cases, the type of data in functional prediction graph 263 may have different units than the data in information graph 258. In some examples, the type of data in functional prediction graph 263 may be different from the type of data in information graph 258, but may have a relationship to the type of data in information graph 258. For example, in some examples, the type of data in information graph 258 may indicate the type of data in functional prediction graph 263. In some examples, the type of data in the functional prediction graph 263 is different from one or both of the field data type sensed by the field sensor 208 and the data type in the 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 information graph 258. In some 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 information graph 258 and different from the other.

[0048] Continuing with the aforementioned example, where information map 258 is a vegetation index map and field sensor 208 senses values ​​indicative of header power usage, prediction map generator 212 can use the vegetation index values ​​in information map 258 and the model generated by prediction model generator 210 to generate a functional prediction map 263 that predicts header power usage at different locations in the field. Prediction map generator 212 thus outputs prediction map 264.

[0049] like Figure 2 As shown, prediction graph 264 predicts the value of a characteristic sensed by field sensor 208, or a characteristic related to the sensed characteristic, at a plurality of locations across the field based on the information values ​​at those locations in information graph 258 and the prediction model. For example, if prediction model generator 210 has generated a prediction model indicating a relationship between vegetation index values ​​and header power usage, then prediction graph generator 212 generates prediction graph 264 that predicts the value of header power usage at the various locations across the field, given the vegetation index values ​​at the various locations across the field. Prediction graph 264 is generated using the vegetation index values ​​at those locations obtained from the vegetation index graph and the relationship between the vegetation index values ​​and header power usage obtained from the prediction model.

[0050] Some variations in the types of data mapped in the information graph 258, the types of data sensed by the field sensors 208, and the types of data predicted on the prediction graph 264 will now be described.

[0051] In some examples, the data type in information graph 258 is different from the data type sensed by field sensors 208, while the data type in prediction graph 264 is the same as the data type sensed by field sensors 208. For example, information graph 258 may be a vegetation index graph, and the variable sensed by field sensors 208 may be yield. Thus, prediction graph 264 may be a predicted yield graph that maps predicted yield values ​​to different geographic locations in a field. In another example, information graph 258 may be a vegetation index graph, and the variable sensed by field sensors 208 may be crop height. Then, prediction graph 264 may be a predicted crop height graph that maps predicted crop height values ​​to different geographic locations in a field.

[0052] Additionally, in some examples, the data type in information graph 258 differs from the data type sensed by field sensors 208, and the data type in prediction graph 264 differs from both the data type in information graph 258 and the data type sensed by field sensors 208. For example, information graph 258 may be a vegetation index graph, and the variable sensed by field sensors 208 may be crop height. Thus, prediction graph 264 may be a predicted biomass graph that maps predicted biomass values ​​to different geographic locations within a field. In another example, information graph 258 may be a vegetation index graph, and the variable sensed by field sensors 208 may be yield. Prediction graph 264 may be a predicted speed graph that maps predicted harvester speed values ​​to different geographic locations within a field.

[0053] In some examples, the information graph 258 is from or was previously passed through the field during or during previous operations, and the data type is different from the data type sensed by the field sensors 208, while the data type in the prediction graph 264 is the same as the data type sensed by the field sensors 208. For example, the information graph 258 can be a seed population graph generated during planting, and the variable sensed by the field sensors 208 can be stalk size. Thus, the prediction graph 264 can be a predicted stalk size graph that maps predicted stalk size values ​​to different geographical locations in the field. In another example, the information graph 258 can be a sowing hybrid graph, and the variable sensed by the field sensors 208 can be crop state (such as upright crops or lodged crops). Thus, the prediction graph 264 can be a predicted crop state graph that maps predicted crop state values ​​to different geographical locations in the field.

[0054] In some examples, infographic 258 is derived from a walk through the field during or prior operations, and the data type is the same as the data type sensed by field sensors 208, and the data type in forecast map 264 is also the same as the data type sensed by field sensors 208. For example, infographic 258 may be a yield map generated in the previous year, and the variable sensed by field sensors 208 may be yield. Thus, forecast map 264 may be a predicted yield map that maps predicted yield values ​​to different geographic locations in the field. In this example, forecast model generator 210 may use the relative yield differences in georeferenced infographic 258 from the previous year to generate a forecast model that models the relationship between the relative yield differences on infographic 258 and the yield values ​​sensed by field sensors 208 during the current harvesting operation. Forecast map generator 210 then uses the forecast model to generate a predicted yield map.

[0055] In another example, the information graph 258 may be a threshing / separating subsystem power usage graph generated during a previous or a priori operation, and the variable sensed by the field sensor 208 may be threshing / separating subsystem power usage. Thus, the prediction graph 264 may be a predicted threshing / separating subsystem power usage graph that maps predicted threshing / separating subsystem power usage values ​​to different geographic locations in the field.

[0056] In some examples, prediction map 264 may be provided to control zone generator 213. Control zone generator 213 groups adjacent portions of an area into one or more control zones based on the data values ​​associated with those adjacent portions of the area in prediction map 264. A control zone may include two or more contiguous portions of an area (e.g., a field) for which the control parameters corresponding to the control zone, used to control the controllable subsystems, are constant. For example, the response time to changing a setting of controllable subsystem 216 may not be sufficient to satisfactorily respond to changes in values ​​contained in a map such as prediction map 264. In such cases, control zone generator 213 parses the map and identifies control zones with defined dimensions to accommodate the response time of controllable subsystem 216. In another example, control zones may be resized to reduce wear caused by excessive actuator movement resulting from continuous adjustments. In some examples, different groups of control zones may exist for individual controllable subsystems 216 or groups of controllable subsystems 216. Control zones may be added to prediction map 264 to obtain predicted control zone map 265. Therefore, except that the prediction control area map 265 includes the control area information that defines the control area, the prediction control area map 265 may be similar to the prediction map 264. Therefore, as described herein, the functional prediction map 263 may include or may not include the control area. Both the prediction map 264 and the prediction control area map 265 are functional prediction maps 263. In one example, the functional prediction map 263 does not include the control area (e.g., the prediction map 264). In another example, the functional prediction map 263 does include the control area (e.g., the prediction control area map 265). In some examples, if an intercropping production system is implemented, multiple crops may exist in the field at the same time. In this case, the prediction map generator 212 and the control area generator 213 are able to identify the position and characteristics of two or more crops, and then generate the prediction map 264 and the prediction map 265 with the control area accordingly.

[0057] It will also be understood that the control zone generator 213 can cluster the values ​​to generate the control zones, and the control zones can be added to the predicted control zone graph 265 or added to a separate graph that only displays the generated control zones. In some examples, the control zones can be used to control and / or calibrate the agricultural harvester 100. In other examples, the control zones can be presented to the operator 260 and used to control or calibrate the agricultural harvester 100, and in other examples, the control zones can be presented to the operator 260 or another user, or stored for later use.

[0058] The prediction map 264 or the prediction control area map 265, or both, is provided to the control system 214, which generates control signals based on the prediction map 264 or the prediction control area map 265, or both. In some examples, the communication system controller 229 controls the communication system 206 to communicate the prediction map 264 or the prediction control area map 265, or control signals based on the prediction map 264 or the prediction control area map 265, to other agricultural harvesters harvesting in the same field. In some examples, the communication system controller 229 controls the communication system 206 to transmit the prediction map 264, the prediction control area map 265, or both to other remote systems.

[0059] The operator interface controller 231 is operable to generate control signals to control the operator interface mechanism 218. The operator interface controller 231 is also operable to present a prediction map 264 or a prediction control zone map 265, or other information derived from or based on the prediction map 264, the prediction control zone map 265, or both, to the operator 260. The operator 260 may be a local operator or a remote operator. As an example, the controller 231 generates control signals to control a display mechanism to display one or both of the prediction map 264 and the prediction control zone map 265 to the operator 260. The controller 231 may generate operator-actuable mechanisms that are displayed and actuable by the operator to interact with the displayed map. The operator may edit the map by, for example, correcting the dynamic characteristics displayed on the map based on the operator's observations. The setting controller 232 may generate control signals to control various settings on the agricultural harvester 100 based on the prediction map 264, the prediction control zone map 265, or both. For example, the settings controller 232 can generate control signals to control the machine and header actuators 248. In response to the generated control signals, the machine and header actuators 248 operate to control, for example, screen and chaff screen settings, concave plate gap, drum settings, cleaning fan speed settings, header height, header function, reel speed, reel position, draper function (where the agricultural harvester 100 is coupled to a draper header), grain header function, internal distribution control, and one or more of other actuators 248 that affect other functions of the agricultural harvester 100. The path planning controller 234 illustratively generates control signals to control the steering subsystem 252 to steer the agricultural harvester 100 according to a desired path. The path planning controller 234 can control the path planning system to generate a path for the agricultural harvester 100 and can control the propulsion subsystem 250 and the steering subsystem 252 to steer the agricultural harvester 100 along the path. The feed rate controller 236 can control various subsystems, such as the propulsion subsystem 250 and the machine actuators 248, to control the feed rate based on the predictive map 264, the predictive control zone map 265, or both. For example, as the agricultural harvester 100 approaches an area with predicted subsystem power usage values ​​above a selected threshold, the feed rate controller 236 can reduce the speed of the agricultural harvester 100 to maintain a power distribution for the predicted power usage requirements of the one or more subsystems. The header and reel controller 238 can generate control signals to control the header or reel or other header functions. The draper belt controller 240 can generate control signals to control the draper belt or other draper functions based on the predictive map 264, the predictive control zone map 265, or both.The cover position controller 242 can generate control signals based on the prediction map 264 or the prediction control zone map 265, or both, to control the position of the cover included on the header, and the residue system controller 244 can generate control signals based on the prediction map 264 or the prediction control zone map 265, or both, to control the residue subsystem 138. The machine cleaning controller 245 can generate control signals to control the machine cleaning subsystem 254. Other controllers included on the agricultural harvester 100 can also control other subsystems based on the prediction map 264 or the prediction control zone map 265, or both.

[0060] Figure 3A and Figure 3B A flow chart is shown illustrating one example of the operation of the agricultural harvester 100 when generating a prediction map 264 and a prediction control zone map 265 based on the information map 258 .

[0061] At block 280, the agricultural harvester 100 receives an information map 258. An example of the information map 258 or receiving the information map 258 is discussed with reference to blocks 281, 282, 284, and 286. As described above, the information map 258 maps the values ​​of a variable corresponding to a first characteristic to different locations in the field, as indicated by block 282. As indicated by block 281, receiving the information map 258 may involve selecting one or more of a plurality of possible information maps available. For example, one information map may be a vegetation index map generated from aerial imagery. Another information map may be a map generated during a previous pass through the field, which may have been performed by a different machine (e.g., a sprayer or other machine) performing a prior or prior operation in the field. The process of selecting one or more information maps may be manual, semi-automatic, or automatic. The information map 258 is based on data collected prior to the current harvesting operation. This is indicated by block 284. For example, the data may be collected based on aerial imagery acquired in the previous year, earlier in the current growing season, or at another time. As indicated by block 285, the information map may be a predictive map that predicts characteristics based on the information map and a relationship with field sensors. Figure 5 The process of generating a prediction map is presented in FIG. This process can also be performed with other sensors and other maps to generate, for example, a predicted yield map or a predicted biomass map. These prediction maps can be used as maps in other prediction processes, as indicated by box 285. The data can be based on data detected in other ways besides using aerial imagery. For example, data for the information map 258 can be sent to the agricultural harvester 100 using the communication system 206 and stored in the data storage device 202. The data for the information map 258 can also be provided to the agricultural harvester 100 in other ways using the communication system 206, which is indicated by the box 285. Figure 3A In some examples, the information graph 258 may be received by the communication system 206 .

[0062] At the start of a harvesting operation, the field sensor 208 generates a sensor signal indicative of one or more field data values ​​indicative of characteristics, such as power characteristics (e.g., the amount of power used by one or more subsystems), as indicated by block 288. Examples of the field sensor 288 are discussed with reference to blocks 222, 290, and 226. As described above, the field sensor 208 includes: an onboard sensor 222; a remote field sensor 224, such as a UAV-based sensor that is flown once to collect field data (shown in block 290); or other types of field sensors designated by the field sensor 226. In some examples, the data from the onboard sensor is georeferenced using position, heading, or velocity data from the geolocation sensor 204.

[0063] The predictive model generator 210 controls the information variable to field variable model generator 228 to generate a model that models the relationship between the mapped values ​​contained in the information graph 258 and the field values ​​sensed by the field sensors 208, as indicated by block 292. The characteristics or data types represented by the mapped values ​​in the information graph 258 and the field values ​​sensed by the field sensors 208 may be the same characteristics or data types, or different characteristics or data types.

[0064] The relationship or model generated by the predictive model generator 210 is provided to the predictive map generator 212. The predictive map generator 212 uses the predictive model and the information map 258 to generate a predictive map 264 that predicts the values ​​of different characteristics sensed by or related to the characteristics sensed by the field sensors 208 at different geographic locations in the field being harvested, as indicated by block 294.

[0065] It should be noted that in some examples, the information graph 258 may include two or more different graphs, or two or more different layers of a single graph. Each layer may represent a data type that is different from the data type of another layer, or a layer may have the same data type obtained at different times. Each graph in the two or more different graphs, or each layer in two or more different layers of a graph, maps different types of variables to geographic locations in a field. In such an example, the predictive model generator 210 generates a predictive model that models the relationship between the field data and the different variables mapped by the two or more different graphs or two or more different layers. Similarly, the field sensors 208 may include two or more sensors that each sense a different type of variable. Therefore, the predictive model generator 210 generates a predictive model that models the relationship between the different types of variables mapped by the information graph 258 and the different types of variables sensed by the field sensors 208. The prediction map generator 212 can use the prediction model and the various maps or layers in the information map 258 to generate a functional prediction map 263, which predicts the value of each sensed characteristic (or characteristic related to the sensed characteristic) sensed by the field sensor 208 at different locations in the field being harvested.

[0066] The predictive map generator 212 configures the predictive map 264 so that the predictive map 264 can be manipulated (or used) by the control system 214. The predictive map generator 212 can provide the predictive map 264 to the control system 214 or the control zone generator 213, or both. Some examples of different ways in which the predictive map 264 can be configured or output will be described with reference to blocks 296, 295, 299, and 297. For example, the predictive map generator 212 configures the predictive map 264 so that the predictive map 264 includes values ​​that can be read by the control system 214 and used as a basis for generating control signals for one or more different controllable subsystems of the agricultural harvester 100, as indicated by block 296.

[0067] Control zone generator 213 can divide prediction map 264 into control zones based on the values ​​on prediction map 264. Geographically contiguous values ​​within a threshold of each other can be grouped into a control zone. The threshold can be a default threshold, or it can be set based on operator input, input from an automated system, or other criteria. The size of the zone can be based on the responsiveness of control system 214, controllable subsystems 216, wear considerations, or other criteria, as indicated by block 295. Predictive map generator 212 configures prediction map 264 for presentation to an operator or other user. Control zone generator 213 can configure predicted control zone map 265 for presentation to an operator or other user. This is indicated by block 299. When presented to an operator or other user, the presentation of the forecast map 264 or the forecast control zone map 265, or both, may include one or more of the forecast values ​​associated with the geographic location on the forecast map 264, the control zones associated with the geographic location on the forecast control zone map 265, and setpoints or control parameters to be used based on the forecast values ​​on the map 264 or the zones on the forecast control zone map 265. In another example, the presentation may include more abstract information or more detailed information. The presentation may also include a confidence level indicating the accuracy with which the forecast values ​​on the forecast map 264 or the zones on the forecast control zone map 265 conform to measurements that can be measured by sensors on the agricultural harvester 100 as the agricultural harvester 100 moves through the field. Furthermore, where information is presented to more than one location, a verification and authorization system may be provided to facilitate the verification 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, an onboard display device may display the map locally on the machine in near real time, or the map may be generated at one or more remote locations, or both. In some examples, each physical display device at each location may be associated with a person or user permission level. User permission levels can be used to determine which display markers are visible on the physical display device and which values ​​the corresponding person can change. As an example, the local operator of the agricultural harvester 100 may not be able to see the information corresponding to the forecast map 264 or to make any changes to the machine operation. However, a supervisor (e.g., a supervisor at a remote location) may be able to see the forecast map 264 on the display, but is prevented from making any changes. A manager who may be at a separate remote location may be able to see all elements on the forecast map 264 and also be able to change the forecast map 264. In some cases, the forecast map 264 that can be accessed and changed by a manager at a remote location can be used for machine control. This is an example of an achievable authorization hierarchy. The forecast map 264 or the forecast control area map 265 or both may also be configured in other ways, as indicated by box 297.

[0068] At block 298, input is received by the control system from the geolocation sensor 204 and other field sensors 208. In particular, at block 300, the control system 214 detects input from the geolocation sensor 204 identifying the geographic location of the agricultural harvester 100. Block 302 represents the control system 214 receiving sensor input indicating the track or heading of the agricultural harvester 100, and block 304 represents the control system 214 receiving the speed of the agricultural harvester 100. Block 306 represents the control system 214 receiving other information from the various field sensors 208.

[0069] At block 308, the control system 214 generates control signals to control the controllable subsystems 216 based on the forecast map 264 or the forecast control zone map 265, or both, and inputs from the geolocation sensor 204 and any other field sensors 208. At block 310, the control system 214 applies the control signals to the controllable subsystems. It will be appreciated that the specific control signals generated and the specific controllable subsystems 216 controlled can vary based on one or more different factors. For example, the control signals generated and the controllable subsystems 216 controlled can be based on the type of forecast map 264 or the forecast control zone map 265, or both, being used. Similarly, the control signals generated, the controllable subsystems 216 controlled, and the timing of the control signals can be based on various delays in the crop flow through the agricultural harvester 100 and the responsiveness of the controllable subsystems 216.

[0070] As an example, the generated prediction map 264 in the form of a predicted power map can be used to control one or more subsystems 216. For example, the predicted power map can include power usage demand values ​​georeferenced to locations within a field being harvested. The power usage demand values ​​from the predicted power map can be extracted and used to control the steering subsystem 252 and the propulsion subsystem 250. By controlling the steering subsystem 252 and the propulsion subsystem 250, the feed rate of material moving through the agricultural harvester 100 can be controlled. Similarly, the header height can be controlled to introduce more or less material, and thus the header height can also be controlled to control the feed rate of material through the agricultural harvester 100. In other examples, if the prediction map 264 maps the predicted header power usage to locations in the field, power distribution to the header can be implemented. For example, if the values ​​present in the predicted power map indicate that one or more areas have a higher power demand on the header subsystem, the header and reel controller 238 can allocate more power from the engine to the header subsystem, which may require allocating less power to other subsystems, such as by reducing speed and reducing power to the propulsion subsystem. The above examples of using the predicted power map in connection with header control are provided as examples only. Thus, values ​​obtained from the predicted power map or other types of predictive maps can be used to generate a variety of other control signals to control one or more controllable subsystems 216.

[0071] At block 312, a determination is made as to whether the harvesting operation has been completed. If harvesting is not complete, processing proceeds to block 314 where field sensor data from the geolocation sensor 204 and the field sensor 208 (and possibly other sensors) is continuously read.

[0072] 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 prediction map 264, the prediction control zone map 265, the model generated by the prediction model generator 210, the zones generated by the control zone generator 213, one or more control algorithms implemented by the controller in the control system 214, and other triggered learning.

[0073] The learning trigger criteria may include any of a variety of different criteria. Some examples of detecting trigger criteria are discussed with reference to blocks 318, 320, 321, 322, and 324. For example, in some examples, triggered learning may involve recreating the relationships used to generate the prediction model when a threshold amount of field sensor data is obtained from the field sensor 208. In these examples, receiving an amount of field sensor data exceeding a threshold from the field sensor 208 triggers or causes the prediction model generator 210 to generate a new prediction model used by the prediction map generator 212. Thus, as the agricultural harvester 100 continues harvesting operations, receiving a threshold amount of field sensor data from the field sensor 208 triggers the creation of a new relationship represented by the prediction model generated by the prediction model generator 210. In addition, a new prediction map 264, a prediction control area map 265, or both may be regenerated using the new prediction model. Block 318 represents detecting a threshold amount of field sensor data used to trigger the creation of a new prediction model.

[0074] In other examples, the learning trigger criteria may be based on how much the field sensor data from the field sensor 208 has changed, for example, over time or compared to a previous value. For example, if the change in the field sensor data (or the relationship between the field sensor data and the information in the information graph 258) is within a selected range, or is less than a defined amount, or is below a threshold, the predictive model generator 210 does not generate a new predictive model. As a result, the predictive map generator 212 does not generate a new predictive map 264 and / or predictive control zone map 265. However, if, for example, the change in the field sensor data is outside a selected range, or is greater than a defined amount, or is above a threshold, the predictive model generator 210 generates a new predictive model using all or part of the newly received field sensor data that the predictive map generator 212 used to generate the new predictive map 264. At block 320, the change in the field sensor data (e.g., the magnitude of the amount by which the data exceeds the selected range, or the magnitude of the change in the relationship between the field sensor data and the information in the information graph 258) may serve as a trigger for causing the generation of a new predictive model and predictive map. Continuing with the examples described above, the thresholds, ranges, and defined amounts may be set as default values, set by an operator or user through user interface interaction, set by an automated system, or set in other ways.

[0075] Other learning triggering criteria may also be used. For example, if the prediction model generator 210 switches to a different information map (different from the initially selected information map 258), switching to the different information map may trigger the prediction model generator 210, the prediction map generator 212, the control zone generator 213, the control system 214, or other items to relearn. In another example, the agricultural harvester 100 transitioning to different terrain or a different control zone may also be used as a learning triggering criterion.

[0076] In some cases, operator 260 may also edit prediction map 264 or prediction control zone map 265, or both. Such edits may change the values ​​on prediction map 264, change the size, shape, location, or presence of control zones on prediction control zone map 265, or both. Block 321 illustrates that the edited information may be used as a learning trigger criterion.

[0077] In some cases, the operator 260 may also observe that the automatic control of the controllable subsystem is not what the operator desired. In these cases, the operator 260 may provide manual adjustments to the controllable subsystem, reflecting the operator 260's desire for the controllable subsystem to operate in a manner different from that commanded by the control system 214. Thus, the operator 260 manually changing the settings may cause one or more of the following to occur based on the adjustments made by the operator 260 (as shown in block 322): causing the predictive model generator 210 to relearn the model, causing the predictive map generator 212 to regenerate the map 264, causing the control zone generator 213 to regenerate one or more control zones on the predictive control zone map 265, and causing the control system 214 to relearn the control algorithm or perform machine learning on one or more of the controller components 232 to 246 in the control system 214. Block 324 represents the use of other triggered learning criteria.

[0078] In other examples, relearning may be performed periodically or intermittently, as indicated by block 326 , based on, for example, selected time intervals (eg, discrete time intervals or variable time intervals).

[0079] As indicated by block 326, if relearning is triggered (whether based on a learning triggering criterion or based on an elapsed time interval), one or more of the predictive model generator 210, the predictive map generator 212, the control zone generator 213, and the control system 214 perform machine learning to generate a new predictive model, a new predictive map, a new control zone, and a new control algorithm, respectively, based on the learning triggering criterion. The new predictive model, new predictive map, and new control algorithm are generated using any additional data collected since the last learning operation was performed. Execution of the relearning is indicated by block 328.

[0080] If the harvesting operation is complete, then the operation moves from block 312 to block 330 where one or more of the prediction map 264, the prediction control area map 265, and the prediction model generated by the prediction model generator 210 are stored. The prediction map 264, the prediction control area map 265, and the prediction model may be stored locally on the data storage device 202 or may be sent to a remote system using the communication system 206 for subsequent use.

[0081] It will be noted that although some examples herein describe the predictive model generator 210 and the predictive map generator 212 as receiving information maps when generating a predictive model and a functional predictive map, respectively, in other examples, the predictive model generator 210 and the predictive map generator 212 may receive other types of maps, including predictive maps, such as functional predictive maps generated during harvesting operations, when generating a predictive model and a functional predictive map, respectively.

[0082] Figure 4 yes Figure 11 is a block diagram of a portion of an agricultural harvester 100. Specifically, among other things, Figure 4 Examples of the prediction model generator 210 and the prediction map generator 212 are shown in more detail. Figure 4 The information flow between the various components shown is also illustrated. Predictive model generator 210 receives one or more of vegetation index map 332, crop moisture map 335, topography map 337, soil property map 339, predicted yield map 341, or predicted biomass map 343 as information maps. Vegetation index map 332 includes georeferenced vegetation index values. Crop moisture map 335 includes georeferenced crop moisture values. Topography map 337 includes georeferenced terrain property values. Soil property map 339 includes georeferenced soil property values.

[0083] The predicted yield map 341 includes georeferenced predicted yield values. Figure 2 3 to generate the predicted yield map 341, wherein the information map includes a vegetation index map or a historical yield map, and the field sensor includes a yield sensor. The predicted yield map 341 can also be generated in other ways.

[0084] The predicted biomass map 343 includes georeferenced predicted biomass values. Figure 2 3 , wherein the information map includes a vegetation index map and the field sensor includes a roller drive pressure sensor or an optical sensor that generates a sensor signal indicative of biomass. The predicted biomass map 343 may also be generated in other ways.

[0085] The predictive model generator 210 also receives a geographic location 334 or an indication of a geographic location from the geographic location sensor 204. The field sensors 208 illustratively include a power characteristic sensor (e.g., a power sensor 336) and a processing system 338. The power sensor 336 senses the power characteristics of one or more components of the agricultural harvester 100. In some cases, the power sensor 336 can be located on the agricultural harvester 100. The processing system 338 processes the sensor data generated from the power sensor 336 to generate processed data, some examples of which are described below. The power sensor 336 can include, but is not limited to, one or more of a voltage sensor, a current sensor, a torque sensor, a fluid pressure sensor, a fluid flow sensor, a force sensor, a bearing load sensor, and a rotation sensor. The output of one or more of these sensors or other sensors can be combined to determine one or more power characteristics.

[0086] This discussion is made with respect to examples where the power sensor 336 is one or more of the sensors listed above. It should be understood that these are merely examples and that other examples of the power sensor 336 are also contemplated herein. Figure 4 As shown, the example prediction model generator 210 includes one or more of a vegetation index to dynamic characteristics model generator 342, a crop moisture to dynamic characteristics model generator 343, a terrain to dynamic characteristics model generator 344, a soil property to dynamic characteristics model generator 345, a yield to dynamic characteristics model generator 346, and a biomass to dynamic characteristics model generator 347. In other examples, the prediction model generator 210 may include a Figure 4 Thus, in some examples, the predictive model generator 210 may also include other items 348, which may include other types of predictive model generators to generate other types of dynamic models.

[0087] The model generator 342 determines a relationship between the dynamic characteristic at the geographic location corresponding to the location where the dynamic characteristic was sensed by the dynamic sensor 336 and the vegetation index value from the vegetation index map 332 corresponding to the same location in the field where the dynamic characteristic was sensed. Based on this relationship established by the model generator 342, the model generator 342 generates a predicted dynamic model 350. The predicted map generator 212 uses the predicted dynamic model 350 to predict the dynamic characteristic at the same location in the field based on the georeferenced vegetation index values ​​at different locations in the field contained in the vegetation index map 332.

[0088] The model generator 343 determines a relationship between the dynamic characteristic at the geographic location corresponding to the location where the dynamic characteristic was sensed by the dynamic sensor 336 and the crop moisture value corresponding to the same location in the field where the dynamic characteristic was sensed from the crop moisture map 335. Based on this relationship established by the model generator 343, the model generator 343 generates a predictive dynamic model 350. The predictive map generator 212 uses the predictive dynamic model 350 to predict the dynamic characteristic at the same location in the field based on the georeferenced crop moisture values ​​at different locations in the field contained in the crop moisture map 335.

[0089] The model generator 344 determines a relationship between a power characteristic at a geographic location corresponding to the location where the power characteristic was sensed by the power sensor 336 and a terrain characteristic value from the terrain map 337 corresponding to the same location in the field where the power characteristic was sensed. Based on this relationship established by the model generator 344, the model generator 344 generates a predicted power model 350. The predicted map generator 212 uses the predicted power model 350 to predict the power characteristic at the same location in the field based on the georeferenced terrain characteristic values ​​at different locations in the field contained in the terrain map 337.

[0090] The model generator 345 determines a relationship between the dynamic characteristic at the geographic location corresponding to the location where the dynamic characteristic was sensed by the dynamic sensor 336 and the soil property value corresponding to the same location in the field where the dynamic characteristic was sensed, from the soil property map 339. Based on this relationship established by the model generator 345, the model generator 345 generates a predicted dynamic model 350. The predicted map generator 212 uses the predicted dynamic model 350 to predict the dynamic characteristic at the same location in the field based on the soil property values ​​at different locations in the field contained in the soil property map 339.

[0091] The model generator 346 determines a relationship between the power characteristic at the geographic location corresponding to the location where the power characteristic was sensed by the power sensor 336 and the yield value from the yield map 341 corresponding to the same location in the field where the power characteristic was sensed. Based on the relationship established by the model generator 346, the model generator 346 generates a predictive power model 350. The predictive map generator 212 uses the predictive power model 350 to predict the power characteristic at the same location in the field based on the yield values ​​at different locations in the field contained in the yield map 341.

[0092] The model generator 347 determines a relationship between the power characteristic at the geographic location corresponding to the location where the power characteristic was sensed by the power sensor 336 and the biomass value corresponding to the same location in the field where the power characteristic was sensed, from the biomass map 343. Based on this relationship established by the model generator 347, the model generator 347 generates a predictive power model 350. The predictive map generator 212 uses the predictive power model 350 to predict the power characteristic at the same location in the field based on the biomass values ​​at different locations in the field contained in the biomass map 343.

[0093] In view of the above, the prediction model generator 210 is operable to generate a plurality of prediction dynamic models, such as one or more prediction dynamic models generated by model generators 342, 343, 344, 345, 346, and 347. In another example, two or more of the above prediction dynamic models may be combined into a single prediction dynamic model that predicts two or more dynamic characteristics based on different values ​​at different locations in the field. Any one of these dynamic models or a combination thereof may be combined into a single prediction dynamic model. Figure 4 The dynamic model 350 in is represented uniformly.

[0094] The prediction dynamics model 350 is provided to the prediction map generator 212. Figure 4 In the example shown, the predictive map generator 212 includes a crop engagement assembly map generator 351, a header power map generator 352, a feeder power map generator 353, a residue handling power map generator 356, and a propulsion power map generator 357. In other examples, the predictive map generator 212 may include more, fewer, or different map generators. Thus, in some examples, the predictive map generator 212 may include other items 358, which may include other types of map generators to generate power maps for other types of power characteristics.

[0095] The crop engagement assembly map generator 351 receives the predicted power model 350 (which predicts power characteristics based on values ​​in one or more of the vegetation index map 332, the crop moisture map 335, the topography map 337, the soil property map 339, the predicted yield map 341, or the predicted biomass map 343) and generates a prediction map predicting the power characteristics of the crop engagement assembly at different locations in the field. For example, the crop engagement assembly may include a cutter bar and a reel, and the crop engagement assembly map generator 351 generates a map of estimated power usage of the reel and cutter bar based on the predicted power model 350 that defines the relationship between crop moisture and power usage of the reel and cutter bar.

[0096] The header power map generator 352 receives a predicted power model 350 that predicts power characteristics based on values ​​in one or more of the vegetation index map 332, the crop moisture map 335, the terrain map 337, the soil property map 339, the predicted yield map 341, or the predicted biomass map 343, and generates a predicted map that predicts power characteristics of the header at different locations in the field. For example, the crop engagement assembly may include one or more of a cutterbar, a reel, a draper belt, an auger, a collection assembly, a stalk handling assembly, and a header positioning actuator; and the header power map generator 352 generates a map of estimated power usage of one or more of the reel, cutterbar, draper belt, auger, collection assembly, stalk handling assembly, and header positioning actuator based on the predicted power model 350, wherein the predicted power model 350 defines a relationship between crop moisture and terrain and power usage of one or more of the reel, cutterbar, draper belt, auger, and header positioning actuator.

[0097] The feeder dynamic map generator 353 receives the predicted dynamic model 350 (which predicts dynamic characteristics based on values ​​in one or more of the vegetation index map 332, the crop moisture map 335, the topography map 337, the soil property map 339, the predicted yield map 341, or the predicted biomass map 343) and generates a predicted map predicting the dynamic characteristics of the feeder at different locations in the field.

[0098] The threshing power map generator 354 receives the predicted power model 350 (which predicts power characteristics based on values ​​in one or more of the vegetation index map 332, the crop moisture map 335, the topography map 337, the soil property map 339, the predicted yield map 341, or the predicted biomass map 343) and generates a predicted map that predicts the power characteristics of the threshing system at different locations in the field. For example, the threshing system may include one or more threshing drums, concave plate adjustment actuators, and beaters, and the threshing power map generator 352 generates a map of estimated power usage of the one or more threshing drums, concave plate adjustment actuators, and beaters based on the predicted power model 350, wherein the predicted power model 350 defines a relationship between the vegetation index and the power usage of the one or more threshing drums, concave plate adjustment actuators, and beaters. Alternatively, for example, the threshing system may include a threshing drum and a set of concave plates at a given gap, and the threshing power map generator 352 generates a map of estimated power usage of the threshing drum with the set of concave plates at the given gap based on the predictive power model 350, wherein the predictive power model 350 defines a relationship between predicted biomass and power usage of the threshing drum with the set of concave plates at the given gap. Alternatively, for example, the threshing system may include one or more beaters in a given configuration, and the threshing power map generator 352 generates a map of estimated power usage of the one or more beaters in the given configuration based on the predictive power model 350, wherein the predictive power model 350 defines a relationship between predicted biomass and power usage of the one or more beaters in the given configuration.

[0099] The separator power map generator 355 receives the predicted power model 350 (which predicts power characteristics based on values ​​in one or more of the vegetation index map 332, the crop moisture map 335, the topography map 337, the soil property map 339, the predicted yield map 341, or the predicted biomass map 343) and generates a prediction map predicting the power characteristics of the separator subsystem at different locations in the field. For example, the separator subsystem may include one or more fans, screens, chaff screens, and straw walkers, and the separator power map generator 355 generates a map of estimated power usage of the one or more fans, screens, chaff screens, and straw walkers based on the predicted power model 350, wherein the predicted power model 350 defines a relationship between predicted yield values ​​and power usage of the one or more fans, screens, chaff screens, and straw walkers. For example, the separator subsystem may include one or more fans operating at a given speed, and a screen, chaff screen, and straw shaker in a given configuration, and the separator power map generator 355 generates a map of estimated power usage of the one or more fans operating at a given speed, and the screen, chaff screen, and straw shaker in a given configuration based on the predictive power model 350, wherein the predictive power model 350 defines a relationship between a predicted yield value and the power usage of the one or more fans operating at a given speed, and the screen, chaff screen, and straw shaker in a given configuration.

[0100] The residue handling power map generator 356 receives the predicted power model 350 (which predicts power characteristics based on values ​​in one or more of the vegetation index map 332, the crop moisture map 335, the topography map 337, the soil property map 339, the predicted yield map 341, or the predicted biomass map 343) and generates a predicted map predicting the power characteristics of the residue handling subsystem at different locations in the field. For example, the residue handling power map generator 356 generates a map of estimated power usage for a residue spreader based on the predicted power model 350, wherein the predicted power model 350 defines a relationship between predicted biomass values ​​and the power usage of the residue spreader. For example, the residue handling power map generator 356 generates a map of estimated power usage for a residue chopper based on the predicted power model 350, wherein the predicted power model 350 defines a relationship between predicted yield values ​​and the power usage of the residue chopper.

[0101] The propulsion power map generator 357 receives the predicted power model 350 (which predicts power characteristics based on values ​​in one or more of the vegetation index map 332, the crop moisture map 335, the topography map 337, the soil property map 339, the predicted yield map 341, or the predicted biomass map 343) and generates a prediction map that predicts power characteristics of the propulsion subsystem at different locations in the field. For example, the propulsion power map generator 357 generates a map of estimated power usage of the propulsion subsystem based on the predicted power model 350, wherein the predicted power model 350 defines a relationship between topography map values ​​and power usage of the propulsion system.

[0102] The prediction map generator 212 outputs one or more predicted power maps 360 that predict one or more power characteristics. Each predicted power map 360 predicts a corresponding power characteristic at a different location in the field. Each generated predicted power map 360 can be provided to the control zone generator 213, the control system 214, or both. The control zone generator 213 generates control zones and incorporates those control zones into a functional prediction map (i.e., prediction map 360) to generate a predicted control zone map 265. One or both of the prediction map 264 and the predicted control zone map 265 can be provided to the control system 214, which generates control signals based on the prediction map 264 and / or the predicted control zone map 265 to control one or more controllable subsystems 216.

[0103] Figure 5 3 is a flow chart illustrating an example of the operation of the prediction model generator 210 and the prediction map generator 212 in generating a prediction power model 350 and a prediction power map 360. At block 362, the prediction model generator 210 and the prediction map generator 212 receive one or more of the a priori vegetation index map 332, the crop moisture map 335, the topographic map 337, the soil property map 339, the predicted yield map 341, the predicted biomass map 343, or some other map 363. At block 364, the processing system 338 receives one or more sensor signals from the power sensors 336. As discussed above, the power sensors 336 may include, but are not limited to, one or more of a voltage sensor 371, a current sensor 373, a torque sensor 375, a fluid pressure sensor 377, a fluid flow sensor 379, a force sensor 381, a bearing load sensor 383, a rotation sensor 385, and other types of power sensors 370.

[0104] At block 372, processing system 338 processes the one or more received sensor signals to generate data indicative of power characteristics. As shown in block 374, the power characteristics are identified at a machine-wide level, e.g., overall power usage for the entire agricultural harvester. This power usage at this level can be used to calculate fuel consumption, efficiency, and the like. As shown in block 376, the power characteristics are identified at a subsystem level. For example, this level of characteristics can be used to allocate power among subsystems. As shown in block 378, the power characteristics are identified at a component level. The sensor data may also include other data at other levels, as shown in block 380.

[0105] At block 382 , the predictive model generator 210 also obtains a geographic location corresponding to the sensor data. For example, the predictive model generator 210 may obtain a geographic location from the geographic location sensor 204 and determine the precise geographic location where the sensor data 340 was captured or derived based on machine latency, machine speed, etc.

[0106] At block 384, the predictive model generator 210 generates one or more predictive power models, such as the power model 350, that model the relationship between vegetation index values, crop moisture values, soil property values, predicted yield values, or predicted biomass values ​​obtained from an information map (e.g., the information map 258) and power characteristics or related characteristics being sensed by the field sensors 208. For example, the predictive model generator 210 may generate a predictive power model that models the relationship between vegetation index values ​​and sensed characteristics, including power usage, indicated by sensor data obtained from the field sensors 208.

[0107] At block 386, a predictive power model, such as the predictive power model 350, is provided to the predictive map generator 212, which generates a predictive power map 360 based on the vegetation index map, the crop moisture map, the soil property map, the predicted yield map, or the predicted biomass map and the predictive power model 350. The predictive power map 360 maps predicted power characteristics. For example, in some examples, the predictive power map 360 predicts power usage / demand for a variety of different subsystems. Furthermore, the predictive power map 360 can be generated during the course of an agricultural operation. Thus, as the agricultural harvester moves across a field to perform an agricultural operation, the predictive power map 360 is generated while the agricultural operation is being performed.

[0108] At block 394, the predictive map generator 212 outputs the predicted power map 360. At block 391, the predictive map generator 212 outputs the predicted power map 360 for presentation to and possible interaction with the operator 260. As shown at block 393, the predictive map generator 212 may configure the map for use by the control system 214. At block 395, the predictive map generator 212 may also provide the map 360 to the control zone generator 213 for control zone generation. At block 397, the predictive map generator 212 may further configure the map 360. The predicted power map 360 (with or without control zones) is provided to the control system 214. At block 396, the control system 214 generates control signals based on the predicted power map 360 to control the controllable subsystems 216.

[0109] As can be seen, the system uses an information map that maps characteristics such as vegetation index values, crop moisture values, soil property values, predicted yield values ​​or predicted biomass values, or information from previous or a priori operations to different locations in the field. The system also uses one or more field sensors that sense field sensor data indicating power characteristics (such as power usage, power demand, power efficiency or power loss), and generates a model that models the relationship between the characteristics or related characteristics sensed using the field sensors and the characteristics mapped in the information map. Therefore, the system uses the model, field data and information map to generate a functional prediction map, and the generated functional prediction map can be configured for use by the control system, presented to a local operator or a remote operator or other user, or both. For example, the control system can use the map to control one or more systems of a combine harvester.

[0110] This discussion has mentioned processors and servers. In some examples, processors and servers include computer processors with associated memory and timing circuits (not separately shown). Processors and servers are functional parts of the systems or devices to which they belong and are activated by and facilitate the functions of other components or items in these systems.

[0111] Moreover, many user interface displays have been discussed. The display can take various forms and can have various user-actuated operator interface mechanisms disposed on the display. For example, the user-actuated operator interface mechanism can be a text box, a check box, an icon, a link, a drop-down menu, a search box, etc. The user-actuated operator interface mechanism can also be actuated in various ways. For example, an operator interface mechanism (such as a pointing device (such as a trackball or mouse, a hardware button, a switch, a joystick or a keyboard, a thumb switch or thumb pad, etc.), a virtual keyboard or other virtual actuator) can be used to actuate the user-actuated operator interface mechanism. In addition, in the case where the screen on which the user-actuated operator interface mechanism is displayed is a touch-sensitive screen, a touch gesture can be used to actuate the user-actuated operator interface mechanism. Furthermore, a voice recognition function can be used to actuate the user-actuated operator interface mechanism using voice commands. Voice recognition can be implemented using voice detection equipment (such as a microphone) and software for recognizing the detected voice and executing commands based on the received voice.

[0112] A number of data storage devices are also discussed. It should be noted that each data storage device can be divided into multiple data storage devices. In some examples, one or more of the data storage devices can be local to the system accessing the data storage device, one or more of the data storage devices can all be located remote from the system utilizing the data storage device, or one or more data storage devices can be local while others are remote. All of these configurations are contemplated by this disclosure.

[0113] In addition, the figures illustrate multiple blocks, with functionality attributed to each block. It should be noted that fewer blocks may be used to illustrate that functionality attributed to multiple different blocks is performed by fewer components. Furthermore, more blocks may be used to illustrate that functionality may be distributed across more components. In various examples, some functionality may be added, and some functionality may be deleted.

[0114] It should be noted that the above discussion has described various 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 projects, such as processors, memories or other processing components (some of which are described below), which perform functions associated with those systems, components, logic or interactions. In addition, any or all of the systems, components, logic and interactions can be implemented by software that is loaded into the memory and subsequently executed by a processor or server or other computing component, as described below. Any or all of the systems, components, logic and interactions can also be implemented by different combinations of hardware, software, firmware, etc., some examples of which are described below. These are some examples of different structures that can be used to implement any or all of the systems, components, logic and interactions described above. Other structures can also be used.

[0115] Figure 6 is a block diagram of an agricultural harvester 600, which may be similar to Figure 2 1. The agricultural harvester 100 shown in FIG. The agricultural harvester 600 communicates with the elements in the remote server architecture 500. In some examples, the remote server architecture 500 can provide computing, software, data access and storage services that do not require the end user to understand the physical location or configuration of the system delivering the services. In various examples, the remote server can use appropriate protocols to deliver the services over a wide area network (such as the Internet). For example, the remote server can deliver applications over a wide area network and can be accessed through a web browser or any other computing component. Figure 2 The software or components shown in the and data associated therewith can be stored on a server at a remote location. The computing resources in a remote server environment can be consolidated at a remote data center location, or the computing resources can be dispersed across multiple remote data centers. The remote server infrastructure can deliver services through a shared data center, even if the service appears as a single access point to the user. Therefore, the components and functionality described herein can be provided from a remote server at a remote location using a remote server architecture. Alternatively, the components and functionality can be provided from a server, or the components and functionality can be installed directly or otherwise on a client device.

[0116] exist Figure 6 In the example shown, some items are similar to Figure 2 , and these items are numbered similarly. Figure 6 It is specifically shown that the prediction model generator 210 or the prediction map generator 212 or both can be located at a server location 502 remote from the agricultural harvester 600. Figure 6In the example shown in , agricultural harvester 600 accesses the system through remote server location 502.

[0117] Figure 6 Another example of a remote server architecture is also depicted. Figure 6 Shown Figure 2 Some elements of the data storage device 202 may be arranged at a remote server location 502, while other elements may be located elsewhere. As an example, the data storage device 202 may be arranged at a location separated from the location 502 and accessed via a remote server at the location 502. Regardless of where these elements are located, they can be directly accessed by the agricultural harvester 600 through a network (such as a wide area network or a local area network); these elements can be hosted at a remote site by a service; or these elements can be provided as a service or accessed by a connection service residing at a remote location. In addition, data can be stored at any location, and the stored data can be accessed or forwarded to an operator, user or system by an operator, user or system. For example, a physical carrier can be used instead of an electromagnetic wave carrier, or a physical carrier can be used in addition to an electromagnetic wave carrier. In some examples, in the case where wireless telecommunications service coverage is poor or non-existent, 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 is close to a machine (such as a fuel cart) 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 arrives at a location where wireless telecommunications service coverage or other wireless coverage is available, the collected information can be forwarded to another network. For example, when the fuel cart travels to a location to refuel other machines or at a main fuel storage location, the fuel cart can enter an area with wireless communication coverage. All of these architectures are considered herein. In addition, the 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 send the information to another network.

[0118] It will also be noted that Figure 2 The components or parts thereof can be arranged on a variety of different devices. One or more of these devices may include an onboard computer, an electronic control unit, a display unit, a server, a desktop computer, a laptop computer, a tablet computer or other mobile devices, such as a palmtop computer, a cellular phone, a smart phone, a multimedia player, a personal digital assistant, etc.

[0119] In some examples, the remote server architecture 500 may include network security measures. These measures include, without limitation, encryption of data on storage devices, encryption of data sent between network nodes, authentication of persons or processes accessing data, and the use of a ledger to record metadata, data, data transfers, data accesses, and data transformations. In some examples, the ledger may be distributed and immutable (e.g., implemented as a blockchain).

[0120] Figure 7 1 is a simplified block diagram of one illustrative example of a handheld computing device or mobile computing device that can be used as a user's or client's handheld device 16, in which the present system (or a portion thereof) can be deployed. For example, the mobile device can be deployed in an operator's cabin of an agricultural harvester 100 for use in generating, processing, or displaying the diagrams discussed above. Figures 8 and 9 are examples of handheld or mobile devices.

[0121] Figure 7 Provides a general block diagram of the components of a client device 16 that can run Figure 2 Some components shown in FIG, the client device 16 can be used with Figure 2 In the device 16, a communication link 13 is provided that allows the handheld device to communicate with other computing devices, and in some examples, provides a channel for automatically receiving information (e.g., by scanning). Examples of the communication link 13 include allowing 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.

[0122] In other examples, the application may be received on a removable Secure Digital (SD) card connected to the interface 15. The interface 15 and the communication link 13 communicate with a processor 17 (which may also be embodied as a processor or server from other figures) along a bus 19, which is also connected to a memory 21 and input / output (I / O) components 23, as well as a clock 25 and a positioning system 27.

[0123] In one example, I / O components 23 are provided to facilitate input and output operations. Various examples of I / O components 23 for device 16 may include input components (such as buttons, touch sensors, optical sensors, microphones, touch screens, proximity sensors, accelerometers, orientation sensors) and output components (such as display devices, speakers, and / or printer ports). Other I / O components 23 may also be used.

[0124] The clock 25 illustratively includes a real-time clock component that outputs time and date. Schematically, the clock 25 can also provide a timing function for the processor 17.

[0125] The positioning system 27 illustratively includes components that output the current geographic location of the device 16. The positioning system 27 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. The positioning system 27 may also include, for example, mapping software or navigation software that generates desired maps, navigation routes, and other geographic functions.

[0126] Memory 21 stores operating system 29, network settings 31, applications 33, application configuration settings 35, data storage 37, communication drivers 39, and communication configuration settings 41. Memory 21 may include all types of tangible, volatile, and non-volatile computer-readable memory 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 in accordance with the instructions. Processor 17 may also be activated by other components to facilitate the functions of those components.

[0127] Figure 8 An example is shown where device 16 is a tablet computer 600. Figure 8 In FIG, computer 600 is shown having a user interface display screen 602. Screen 602 may be a touch screen or a pen-supported interface that receives input from a pen or stylus. Tablet computer 600 may also utilize an on-screen virtual keyboard. Of course, computer 600 may also be attached to a keyboard or other user input device, for example, via a suitable attachment mechanism (such as a wireless link or a USB port). Computer 600 may also schematically receive voice input.

[0128] Figure 9 Similar to Figure 8 , except that the device is a smartphone 71. Smartphone 71 has a touch-sensitive display 73 that displays icons or tiles or other user input mechanisms 75. Mechanisms 75 can be used by the user to run applications, make calls, perform data transfer operations, etc. Generally speaking, smartphone 71 is built on a mobile operating system and provides more advanced computing power and connectivity than feature phones.

[0129] Note that other forms of device 16 are possible.

[0130] Figure 10 It can be deployed Figure 2 An example of a computing environment for elements of Figure 10, an 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 the processors or servers from the previous figures), a system memory 830, and a system bus 821 that couples various system components including the system memory to the processing unit 820. The system bus 821 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. Figure 2 The memory and program described can be deployed in Figure 10 in the corresponding part of .

[0131] Computer 810 typically includes a variety of computer-readable media. Computer-readable media can be any available media that can be accessed by computer 810, and includes 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 is distinct from, and does not include, modulated data signals or carrier waves. Computer-readable media include hardware storage media, including volatile and non-volatile, removable and non-removable media implemented in any method or technology 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 technology, CD-ROM, digital versatile disks (DVDs) or other optical disk storage devices, magnetic cassettes, magnetic tape, magnetic disk storage devices or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by 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 media. The term "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.

[0132] The system memory 830 includes computer storage media in the form of volatile and / or nonvolatile memory, or both, such as read-only memory (ROM) 831 and random access memory (RAM) 832. A basic input / output system 833 (BIOS), containing the basic routines that help to transfer information between elements within the computer 810, such as during startup, is typically stored in ROM 831. RAM 832 typically contains data and / or program modules, or both, that are immediately accessible to and / or currently being operated on by the processing unit 820. By way of example, and not limitation, Figure 10Operating system 834 , application programs 835 , other program modules 836 , and program data 837 are shown.

[0133] The computer 810 may also include other removable / non-removable volatile / non-volatile computer storage media. For example only, Figure 10 Shown is a hard disk drive 841 that reads from and writes to a non-removable nonvolatile magnetic medium, an optical disk drive 855, and a nonvolatile optical disk 856. The hard disk drive 841 is typically connected to the system bus 821 through a non-removable storage interface, such as interface 840, and the optical disk drive 855 is typically connected to the system bus 821 through a removable storage interface, such as interface 850.

[0134] Alternatively or additionally, the functions described herein may be at least partially performed by one or more hardware logic components. For example, but not limited to, exemplary 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), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0135] discussed above and in Figure 10 The drives and their associated computer storage media shown in FIG. 8 provide storage of computer readable instructions, data structures, program modules and other data for the computer 810. For example, in FIG. Figure 10 845, other program modules 846, and program data 847. Note that these components can be the same as or different from operating system 834, application programs 835, other program modules 836, and program data 837.

[0136] A user can enter commands and information into the computer 810 through input devices such as a keyboard 862, a microphone 863, and a pointing device 861, such as a mouse, trackball, or touch pad. Other input devices (not shown) may include a joystick, a game controller, a satellite dish, a scanner, or the like. These and other input devices are typically connected to the processing unit 820 through a user input interface 860 that is coupled to the system bus, but may be connected through other interface and bus structures. A visual display 891 or other type of display device is also connected to the system bus 821 through an interface, such as a video interface 890. In addition to the monitor, the computer may also include other peripheral output devices, such as speakers 897 and printer 896, which may be connected through a peripheral output interface 895.

[0137] Computer 810 operates in a networking environment using logical connections, such as a Controller Area Network (CAN), a Local Area Network (LAN), or a Wide Area Network (WAN), to one or more remote computers, such as remote computer 880 .

[0138] When used in a LAN networking environment, the computer 810 is connected to the LAN 871 through a network interface or adapter 870. When used in a WAN networking environment, the computer 810 typically includes a modem 872 or other means for establishing communications over the WAN 873 (such as the Internet). In a networking environment, program modules may be stored in the remote memory storage device. For example, Figure 10 Remote application programs 885 are shown as residing on remote computer 880 .

[0139] It should also be noted that the different examples described herein can be combined in different ways. That is, parts of one or more examples can be combined with parts of one or more other examples. All of these aspects are considered in this article.

[0140] Example 1 is an agricultural machine comprising:

[0141] a communication system that receives an information map including values ​​of a first agricultural characteristic corresponding to different geographic locations in a field;

[0142] a geographic location sensor, the geographic location sensor detecting a geographic location of the agricultural machine;

[0143] a field sensor configured to detect a value of a power characteristic of the agricultural machine corresponding to the geographical location, the power characteristic of the agricultural machine serving as a second agricultural characteristic;

[0144] a prediction model generator that generates a prediction agricultural model based on the value of the first agricultural characteristic in the information map at the geographical location and the value of the second agricultural characteristic sensed by the field sensor at the geographical location, the prediction agricultural model models a relationship between the first agricultural characteristic and the second agricultural characteristic; and

[0145] A prediction map generator that generates a functional prediction agricultural map of the field based on the value of the first agricultural characteristic in the information map and based on the prediction agricultural model, wherein the functional prediction agricultural map maps the predicted value of the second agricultural characteristic to the different geographical locations in the field.

[0146] Example 2 is an agricultural working machine of any or all of the preceding examples, wherein the predictive map generator configures the functional predictive agricultural map for use by a control system, and the control system generates control signals based on the functional predictive agricultural map to control controllable subsystems on the agricultural working machine.

[0147] Example 3 is an agricultural work machine of any or all of the preceding examples, wherein the field sensor on the agricultural work machine is configured to detect power usage of one or more subsystems corresponding to the geographic location as the value of the second agricultural characteristic.

[0148] Example 4 is an agricultural working machine of any or all of the preceding examples, wherein the field sensor comprises one or more of a voltage sensor, a current sensor, a torque sensor, a fluid pressure sensor, a fluid flow sensor, a force sensor, a bearing load sensor, and a rotation sensor.

[0149] Example 5 is the agricultural work machine of any or all of the preceding examples, wherein the information map includes a vegetation index map that maps vegetation index values ​​as the first agricultural characteristic to the different geographic locations in the field.

[0150] Example 6 is an agricultural working machine of any or all of the preceding examples, wherein the predictive model generator is configured to determine a relationship between the power characteristic and the vegetation index based on the power characteristic value detected at the geographic location and the vegetation index value at the geographic location in the vegetation index map, and the predictive agricultural model is configured to receive an input vegetation index value as a model input and generate a predicted power characteristic value as a model output based on the determined relationship.

[0151] Example 7 is the agricultural work machine of any or all of the preceding examples, wherein the information map includes a crop moisture map that maps crop moisture values ​​as the first agricultural characteristic to the different geographic locations in the field.

[0152] Example 8 is an agricultural working machine of any or all of the preceding examples, wherein the predictive model generator is configured to determine a relationship between the power characteristic and the crop moisture based on the power characteristic value detected at the geographic location and the crop moisture value at the geographic location in the crop moisture map, and the predictive agricultural model is configured to receive an input crop moisture value as a model input and generate a predicted power characteristic value as a model output based on the determined relationship.

[0153] Example 9 is an agricultural working machine of any or all of the foregoing examples, wherein the information map includes a predicted yield map, which maps the predicted yield values ​​of the first agricultural characteristic to the different geographic locations in the field; and wherein the prediction model generator is configured to determine the relationship between the predicted yield and the power characteristic based on the power characteristic value detected at the geographic location and the yield value at the geographic location in the predicted yield map, and the prediction agricultural model is configured to receive the input predicted yield value as a model input and generate a predicted power characteristic value as a model output based on the determined relationship.

[0154] Example 10 is an agricultural working machine of any or all of the foregoing examples, wherein the information map includes a predicted biomass map, which maps the predicted biomass values ​​as the first agricultural characteristic to the different geographical locations in the field; and wherein the predictive model generator is configured to determine the relationship between the predicted biomass and the dynamic characteristic based on the dynamic characteristic values ​​detected at the geographical locations and the biomass values ​​at the geographical locations in the predicted biomass map, and the predictive agricultural model is configured to receive the input predicted biomass values ​​as model input and generate predicted dynamic characteristic values ​​as model output based on the determined relationship.

[0155] Example 11 is an agricultural working machine of any or all of the foregoing examples, wherein the information map includes a topographic map, which maps the terrain characteristic values ​​as the first agricultural characteristic to the different geographic locations in the field; and wherein the predictive model generator is configured to determine the relationship between the terrain characteristic and the power characteristic based on the power characteristic values ​​detected at the geographic locations and the terrain values ​​at the geographic locations in the topographic map, and the predictive agricultural model is configured to receive the input terrain characteristic values ​​as model input and generate predicted power characteristic values ​​as model output based on the determined relationship.

[0156] Example 12 is a computer-implemented method for generating a functional predictive agricultural map, comprising:

[0157] receiving, at an agricultural work machine, an information map indicating values ​​of a first agricultural characteristic corresponding to different geographic locations in a field;

[0158] detecting a geographic location of the agricultural machine;

[0159] detecting a power characteristic value corresponding to the geographical location as a second agricultural characteristic using an on-site sensor;

[0160] generating a predictive agricultural model that models a relationship between the first agricultural characteristic and the second agricultural characteristic; and

[0161] A prediction map generator is controlled to generate the functional prediction agricultural map of the field based on the value of the first agricultural characteristic in the information map and the prediction agricultural model, the functional prediction agricultural map mapping the predicted values ​​of the second agricultural characteristic to the different geographical locations in the field.

[0162] Example 13 is the computer-implemented method of any or all of the preceding examples, further comprising:

[0163] The functional predictive agricultural map is configured for use in a control system that generates control signals based on the functional predictive agricultural map to control controllable subsystems on the agricultural work machine.

[0164] Example 14 is a computer-implemented method of any or all of the preceding examples, wherein detecting a power characteristic value as the second agricultural characteristic with a field sensor includes detecting a power usage demand of a subsystem of the agricultural work machine corresponding to the geographic location.

[0165] Example 15 is the computer-implemented method of any or all of the preceding examples, wherein detecting a power characteristic value as the second agricultural characteristic with a field sensor includes detecting a power usage demand of a component of the subsystem corresponding to the geographic location.

[0166] Example 16 is a computer-implemented method of any or all of the preceding examples, wherein receiving the infographic comprises:

[0167] An information map generated based on previous operations performed in the field is received.

[0168] Example 17 is the computer-implemented method of any or all of the preceding examples, wherein the first agricultural characteristic comprises one of a vegetation index, crop moisture, terrain characteristics, soil properties, predicted yield, and predicted biomass.

[0169] Example 18 is the computer-implemented method of any or all of the preceding examples, further comprising:

[0170] An operator interface mechanism is controlled to present the predictive agricultural map.

[0171] Embodiment 19 is an agricultural machine, comprising:

[0172] a communication system that receives an information map indicating agricultural characteristic values ​​corresponding to different geographic locations in a field;

[0173] a geographic location sensor, the geographic location sensor detecting a geographic location of the agricultural machine;

[0174] a field sensor, the field sensor detecting a power characteristic value corresponding to the geographical location;

[0175] a prediction model generator that generates a prediction power model based on the agricultural characteristic value at the geographical location in the information map and the power characteristic value at the geographical location sensed by the field sensor, the prediction power model models a relationship between the agricultural characteristic value and the power characteristic; and

[0176] A prediction map generator generates a functional prediction power map of the field based on the agricultural characteristic values ​​in the information map and based on the prediction power model, the functional prediction power map mapping the predicted power characteristic values ​​to the different geographical locations in the field.

[0177] Example 20 is the agricultural working machine of any or all of the preceding examples, wherein the information map indicates an agricultural characteristic, the agricultural characteristic indicating one or more of vegetation index, crop moisture, terrain characteristics, soil properties, predicted yield, and predicted biomass.

[0178] Although the subject matter has been described in language specific to structural features or methodological acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts are disclosed as example forms of the claims.

Claims

1. An agricultural system comprising: a communication system (206) that receives an information map (258) including values ​​of a first agricultural characteristic corresponding to different geographic locations in a field; a geographic location sensor (204) for detecting a geographic location of the agricultural machine; a field sensor (208) for detecting a value of a power characteristic of the agricultural machine (100) corresponding to the geographical location, the power characteristic serving as a second agricultural characteristic; a prediction model generator (210) for generating a prediction agricultural model based on the value of the first agricultural characteristic in the information map (258) at the geographic location and the value of the second agricultural characteristic sensed by the field sensor (208) at the geographic location, the prediction agricultural model modeling a relationship between the first agricultural characteristic and the second agricultural characteristic; and A prediction map generator (212) generates a functional prediction agricultural map of the field based on the value of the first agricultural characteristic in the information map (258) and based on the prediction agricultural model, the functional prediction agricultural map mapping the predicted values ​​of the second agricultural characteristic to the different geographical locations in the field.

2. The agricultural system according to claim 1, wherein: The predictive map generator configures the functional predictive agricultural map for use by a control system that generates control signals to control controllable subsystems on the agricultural work machine based on the functional predictive agricultural map.

3. The agricultural system according to claim 1, wherein: The field sensor is configured to detect power usage of one or more subsystems corresponding to the geographic location as a value of the second agricultural characteristic.

4. The agricultural system according to claim 3, wherein: The field sensor includes one or more of a voltage sensor, a current sensor, a torque sensor, a fluid pressure sensor, a fluid flow sensor, a force sensor, a bearing load sensor, and a rotation sensor.

5. The agricultural system according to claim 1, wherein: The information map includes a vegetation index map that maps vegetation index values ​​as the first agricultural characteristic to the different geographical locations in the field.

6. The agricultural system according to claim 5, wherein: The prediction model generator is configured to determine the relationship between the dynamic characteristic and the vegetation index based on the dynamic characteristic value detected at the geographical location and the vegetation index value at the geographical location in the vegetation index map, and the prediction agricultural model is configured to receive the input vegetation index value as a model input and generate a predicted dynamic characteristic value as a model output based on the determined relationship.

7. The agricultural system according to claim 1, wherein: The information map includes a crop moisture map that maps crop moisture values ​​as the first agricultural characteristic to the different geographical locations in the field.

8. The agricultural system according to claim 7, wherein: The prediction model generator is configured to determine the relationship between the dynamic characteristic and the crop moisture based on the dynamic characteristic value detected at the geographic location and the crop moisture value at the geographic location in the crop moisture map, and the prediction agricultural model is configured to receive the input crop moisture value as a model input and generate a predicted dynamic characteristic value as a model output based on the determined relationship.

9. A computer-implemented method for generating a functional predictive agricultural map, comprising: receiving an information map (258) indicating values ​​of a first agricultural characteristic corresponding to different geographical locations in a field; Detecting the geographical location of the agricultural machine (100); detecting a power characteristic value corresponding to the geographic location as a second agricultural characteristic using a field sensor (258); generating a predictive agricultural model that models a relationship between the first agricultural characteristic and the second agricultural characteristic; and Controlling a prediction map generator (212) to generate the functional prediction agricultural map of the field based on the value of the first agricultural characteristic in the information map (258) and the prediction agricultural model, the functional prediction agricultural map mapping the predicted value of the second agricultural characteristic to the different geographical locations in the field.

10. An agricultural system comprising: a communication system (206) that receives an information map (258) indicating agricultural characteristic values ​​corresponding to different geographic locations in a field; a geographic location sensor (204) for detecting a geographic location of the agricultural machine; a field sensor (208) that detects a power characteristic value of a power characteristic corresponding to the geographic location; a prediction model generator (210) for generating a prediction power model based on the agricultural characteristic value at the geographical location in the information map (258) and the power characteristic value at the geographical location sensed by the field sensor (208), the prediction power model modeling a relationship between the agricultural characteristic value and the power characteristic; and A prediction map generator that generates a functional prediction power map for the field based on the agricultural characteristic values ​​in the information map (258) and based on the prediction power model, the functional prediction power map mapping the predicted power characteristic values ​​to the different geographical locations in the field.

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