Soil property-based prediction map generation and control

Abstract

One or more information maps are obtained by an agricultural machinery. These maps map one or more agricultural characteristic values ​​to different geographical locations within the field. Field sensors on the agricultural machinery sense the agricultural characteristics as the machinery 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. This prediction map predicts the agricultural characteristics at different locations within the field. The prediction map can be output and used for automated machine control.

Description

Technical Field

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

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

[0003] Different types of agricultural machinery operate on fields with a variety of soil properties, such as topography, soil type, soil moisture, soil cover, soil structure, and many other different soil properties. Each of these different soil properties can vary throughout the field, such as varying topography, varying soil type, varying soil moisture levels, varying soil cover levels, varying soil structure, and many other variations. Maps indicating one or more soil properties throughout the field can be generated and used during the operation of agricultural machinery on the field.

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

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

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

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

[0008] Figure 2This is a block diagram showing some parts of an agricultural harvester in more detail, based on some examples of this disclosure.

[0009] Figures 3A to 3B A flowchart illustrating an example of the operation of an agricultural harvester when generating a diagram is shown.

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

[0011] Figure 4B This is a block diagram illustrating an exemplary field sensor.

[0012] Figure 5 This is a flowchart illustrating an example of how an agricultural harvester receives soil property maps, detects characteristics, and generates functional prediction maps for use in controlling the agricultural harvester during harvesting operations.

[0013] Figure 6 This is a block diagram illustrating an example of an agricultural harvester communicating with a remote server environment.

[0014] Figures 7 to 9 An example of a mobile device that can be used in agricultural harvesters is shown.

[0015] Figure 10 This is a block diagram illustrating an example of a computing environment that can be used in agricultural harvesters and the architecture shown in the aforementioned figures. Detailed Implementation

[0016] To facilitate understanding of the principles of this disclosure, reference will now be made to the examples shown in the accompanying drawings, and they will be described using specific language. However, it will be understood that this is not intended to limit the scope of this disclosure. Any changes and further modifications to the described apparatus, systems, and methods, as well as any further application of the principles of this disclosure, are fully contemplated, as would normally occur to those skilled in the art to which this disclosure pertains. Specifically, it is fully contemplated that features, components, steps, or combinations thereof described with respect to one example may be combined with features, components, steps, or combinations thereof described with respect to other examples of this disclosure.

[0017] This specification relates to generating predictive maps by combining field data acquired concurrently with agricultural operations with prior data, and more specifically, generating predictive maps that correlate field data with prior data to predict characteristics across the entire field as indicated by the field data. In some examples, predictive maps can be used to control agricultural machinery (e.g., agricultural harvesters). As discussed above, soil properties of a field can vary across the entire field; for example, soil type, soil moisture, soil cover, soil structure, and many other different soil properties can vary across the entire field. Other agricultural characteristics (such as non-machinery characteristics or machinery characteristics) may be influenced by or otherwise related to soil properties, making these agricultural characteristics predictable in different areas of a field with similar soil properties. For example, the crop yield or biomass in one area of ​​a field with known (or estimated) soil properties may be similar to the crop yield or biomass in another area of ​​the same field with similar known (or estimated) soil properties. The performance of agricultural machinery can be influenced by agricultural characteristics, and therefore, by predicting the agricultural characteristics across the field, control of the machinery can be implemented to optimize its operation under given agricultural characteristics. For example, by predicting the crop biomass across the field based on data from soil property maps and field data indicating biomass (such as crop height, crop density, crop volume, threshing drum drive force, and various other characteristics), the position of the harvester's header relative to the field surface or the forward speed of the harvester can be adjusted to control the throughput or feed rate of the plant material to be processed by the harvester. This is just one example.

[0018] The performance of an agricultural harvester can be affected by a variety of agricultural characteristics, such as non-machinery characteristics (e.g., characteristics of the field or the plants on the field) and various machine characteristics of the harvester (e.g., machine settings, operating characteristics, or machine performance characteristics). These agricultural characteristics can be detected in the field using sensors on the harvester, or by detecting values ​​indicating these agricultural characteristics, and the harvester can be controlled in a variety of different ways based on these agricultural characteristics.

[0019] Soil property maps graphically map soil property values ​​across different geographic locations in a field of interest (they can indicate soil type, soil moisture, soil cover, soil structure, and a variety of other soil properties). Therefore, soil property maps provide georeferenced soil properties across the field of interest. Soil type can refer to a taxonomic unit in soil science, where each soil type includes a defined set of shared properties. Soil types can include, for example, sandy soils, clay soils, silty soils, peat soils, chalky soils, loam soils, and a variety of other soil types. Soil moisture can refer to the amount of water held in or otherwise contained 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 materials such as crop residues or cover crops, debris, and a variety of other items or materials. Typically, in agricultural terminology, soil cover includes a measure of remaining crop residues (e.g., piles of remaining plant stalks) and a measure of cover crops. Soil structure can refer to the arrangement of the solid components of soil and the pore spaces between them. Soil structure can include the arrangement of individual particles (such as particles of sand, silt, and clay). Soil structure can be described by grade (degree of aggregation), category (average size of aggregates), and form (type of aggregates), as well as many other different descriptions. These are just examples. Many other properties and characteristics of soil can be mapped to soil property values ​​on a soil property map.

[0020] These soil property maps can be generated based on data collected during another operation corresponding to the field of interest, such as a previous agricultural operation (e.g., planting or spraying) in the same season and a previous agricultural operation performed in a past season (e.g., a previous harvesting). The agricultural machinery performing those operations can have onboard sensors that detect characteristics indicating soil properties, such as soil type, soil moisture, soil cover, soil structure, and various other characteristics indicating a variety of other soil properties. Furthermore, the operating characteristics or machine settings or performance characteristics of the agricultural machinery during the previous operation, along with other data, can be used to generate soil property maps. For example, data indicating the height of the harvester's header at different geographical locations throughout the field of interest during a previous harvesting operation, along with weather data indicating weather conditions (e.g., precipitation or wind data during intermittent periods (e.g., the period from the previous harvesting operation to the time when the soil property map was generated), can be used to generate a soil moisture map. For example, by knowing the height of the harvesting platform, the amount of remaining plant residue (e.g., crop stalks) can be known or estimated, and together with precipitation data, soil moisture levels can be predicted. This is just one example.

[0021] In other examples, surveys of the field of interest can be conducted by various machines equipped with sensors (e.g., imaging systems) or by humans. Data collected during these surveys can be used to generate soil property maps. For example, an aerial survey of the field of interest can be conducted, during which the field is imaged, and a soil property map can be generated based on the image data. In another example, a person can enter the field, with or without the assistance of devices such as sensors, to collect various data or samples, and a soil property map of the field can be generated based on these data or samples. For example, a person can collect core samples from different geographical locations throughout the field of interest. These core samples can be used to generate a soil property map of the field. In other examples, soil property maps can be based on input from a user or operator (e.g., input from a farm manager), which can provide various data collected or observed by the user or operator.

[0022] In addition, soil property maps can be obtained from remote sources, such as third-party service providers or government agencies, such as the USDA Natural Resources Defense Council (NRCS), the U.S. Geological Survey (USGS), and many other different remote sources.

[0023] In some examples, soil property maps can be derived from sensor readings of one or more electromagnetic radiation bands reflected by the surface of the soil (or field). These electromagnetic radiation bands can be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum, without limitation.

[0024] These are merely some examples of the ways in which soil property maps can be generated and provided in the current system. Those skilled in the art will understand that soil property maps can be generated in many different ways, and the scope of this disclosure is not limited to the examples provided herein.

[0025] Therefore, this discussion pertains to a system that receives a soil property map of a field or a map generated based on prior or previous operations, and also uses field sensors to detect variables indicating one or more characteristics during harvesting operations. These characteristics are, for example, agricultural characteristics, such as non-machinery characteristics (e.g., characteristics of the field or plants in the field) and machine characteristics (e.g., machine settings, operating characteristics, or machine performance data). However, it should be noted that field sensors can detect variables indicating any of a plurality of characteristics and are not limited to those described herein. Agricultural characteristics are any of a variety of characteristics that may affect agricultural operations (e.g., harvesting operations). The system generates a model that models the relationship between soil property values ​​on the soil property map or values ​​on the map generated from prior or previous operations and output values ​​from the field sensors. This model is used to generate a functional prediction map that predicts characteristics at different locations in the field as indicated by the output values ​​from the field sensors. The functional prediction map generated during harvesting operations can be presented to the operator or other users, and / or used to automatically control agricultural harvesters during harvesting operations.

[0026] Figure 1 This is a partial schematic diagram of a self-propelled agricultural harvester 100. In the example shown, the agricultural harvester 100 is a combine harvester. Furthermore, although combine harvesters are provided as examples throughout this disclosure, it will be understood that this specification also applies to other types of harvesters, such as cotton harvesters, sugarcane harvesters, self-propelled hay harvesters, slashers, or other agricultural operating machines. Therefore, this disclosure is intended to cover the various types of harvesters described, and is therefore not limited to combine harvesters. Additionally, this disclosure relates to other types of operating machines, such as agricultural seeders and sprayers to which predictive mapping can be applied, construction equipment, forestry equipment, and turf management equipment. Therefore, this disclosure is intended to cover these various types of harvesters and other operating machines, and is therefore not limited to combine harvesters.

[0027] like Figure 1As shown, the agricultural harvester 100 exemplarily includes an operator's cab 101, which may have various different operator interface mechanisms for controlling the agricultural harvester 100. The agricultural harvester 100 includes front-end equipment, such as a header 102 and a cutter generally indicated by 104. The agricultural harvester 100 also includes a feeder housing 106, a feed accelerator 108, and a thresher generally indicated by 110. The feeder housing 106 and the feed accelerator 108 form part of a material handling subsystem 125. The header 102 is pivotally coupled to the frame 103 of the agricultural harvester 100 along a pivot axis 105. One or more actuators 107 drive the header 102 to move about the axis 105 in a direction generally indicated by arrow 109. Therefore, the vertical position (cutting height) of the cutting table 102 above the ground 111 (where the cutting table 102 travels) can be controlled by actuating the actuator 107. Although Figure 1 As not shown, the agricultural harvester 100 may further include actuators operated to apply one or more of a tilt angle, a tumble angle, or both to the header 102 or a portion thereof. Tilt refers to the angle at which the cutter 104 engages with the crop. For example, the tilt angle is increased by controlling the header 102 to point the distal edge 113 of the cutter 104 more towards the ground. The tilt angle is decreased by controlling the header 102 to point the distal edge 113 of the cutter 104 further away from the ground. Tumble refers to the orientation of the header 102 about the longitudinal axis of the agricultural harvester 100.

[0028] The threshing machine 110 exemplarily includes a threshing drum 112 and a set of concave plates 114. Additionally, the agricultural harvester 100 includes a separator 116. The agricultural harvester 100 also includes a cleaning subsystem or cleaning chamber 118 (collectively referred to as cleaning subsystem 118), which includes a cleaning fan 120, a chaff screen 122, and a screen 124. The material handling subsystem 125 also includes a discharge agitator 126, a waste lifter 128, a clean grain lifter 130, and an unloading screw conveyor 134 and a nozzle 136. The clean grain lifter moves clean grain into a clean grain bin 132. The agricultural harvester 100 also includes a residue subsystem 138, which may include a shredder 140 and a spreader 142. The agricultural harvester 100 also includes a propulsion subsystem, which includes an engine driving a ground engagement assembly 144 (e.g., wheels or tracks). In some examples, the combine harvester within the scope of this disclosure may have more than one of any of the above subsystems. In some examples, the agricultural harvester 100 may have Figure 1 The left and right grain cleaning subsystems and separators are not shown in the diagram.

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

[0030] Returning to the description of the operation of the agricultural harvester 100, after the crop is cut by the cutter 104, the cut crop material is moved in the feeder housing 106 by a conveyor toward the feed accelerator 108, which accelerates the crop material into the thresher 110. The crop is threshed by a roller 112 that rotates against a concave plate 114. In a separator 116, a separator roller moves the threshed crop, while a discharge agitator 126 moves a portion of the residue toward the residue subsystem 138. The portion of residue conveyed to the residue subsystem 138 is shredded by the residue shredder 140 and spread across the field by a spreader 142. In other configurations, the residue is discharged from the agricultural harvester 100 in piles. In other examples, the residue subsystem 138 may include a seed eliminator (not shown), such as a seed bagger or other seed collector, or a seed shredder or other seed crusher.

[0031] The grain falls into the grain cleaning subsystem 118. A husk sieve 122 separates larger pieces of grain, while a screen 124 separates smaller pieces from the clean grain. The clean grain falls onto a screw conveyor that moves the grain to the inlet of a clean grain elevator 130, which then moves the clean grain upwards, causing it to settle in a clean grain bin 132. Airflow generated by a cleaning fan 120 removes residue from the grain cleaning subsystem 118. The cleaning fan 120 directs air upwards along an airflow path through the screen and husk sieve. The airflow then transports the residue backwards within the agricultural harvester 100 toward the residue handling subsystem 138.

[0032] The waste elevator 128 returns the waste to the threshing machine 110, where it is re-threshed. Alternatively, the waste may also be conveyed by the waste elevator or another conveying device to a separate re-threshing mechanism, where it is also re-threshed.

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

[0034] Ground speed sensor 146 senses the travel speed of the agricultural harvester 100 on the ground. Ground speed sensor 146 can sense the travel speed of the agricultural harvester 100 by sensing the rotational speed of ground-engaging components (e.g., wheels or tracks), drive shafts, axles, or other components. In some cases, a positioning system can be used to sense the travel speed, such as a Global Positioning System (GPS), a dead reckoning system, a LoRa (LoRandom Access Registry) system, or a variety of other systems or sensors that provide an indication of travel speed.

[0035] Loss sensor 152 exemplarily provides an output signal indicating the amount of grain loss occurring on both the right and left sides of the grain cleaning subsystem 118. In some examples, sensor 152 is an impact sensor that counts grain impacts per unit time or per unit distance traveled to provide an indication of grain loss occurring at the grain cleaning subsystem 118. The impact sensors on the right and left sides of the grain cleaning subsystem 118 can provide separate signals or combined or aggregated signals. In some examples, instead of providing separate sensors for each grain cleaning subsystem 118, sensor 152 may include a single sensor.

[0036] Separator loss sensor 148 provides indication of the left and right separators ( Figure 1(Not shown separately) The separator loss sensor 148 can be associated with the left and right separators and can provide individual grain loss signals or combined or aggregated signals. In some cases, various types of sensors may also be used to sense grain loss in the separator.

[0037] The agricultural harvester 100 may also include other sensors and measuring mechanisms. For example, the agricultural harvester 100 may include one or more of the following sensors: a header height sensor that senses the height of the header 102 above the ground 111; a stability sensor that senses the oscillation or jumping (and amplitude) of the agricultural harvester 100; a residue setting sensor configured to sense whether the agricultural harvester 100 is configured to chop residue, pile it, etc.; a cleaning chamber fan speed sensor that senses the speed of the cleaning 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 drum speed of the drum 112; and a force sensor that senses the force required to drive the threshing drum 112, such as sensing the fluid (e.g., hydraulic fluid) required to drive the threshing drum 112. The system includes a pressure sensor (such as an air pressure sensor) or a torque sensor that senses the torque required to drive the threshing drum 112; a husk sieve gap sensor that senses the opening size in the husk sieve 122; a sieve mesh gap sensor that senses the opening size in the sieve mesh 124; a material other than grain (MOG) moisture sensor that senses the moisture level of the MOG passing through the harvester 100; one or more machine setting sensors configured to sense various configurable settings of the harvester 100; a machine orientation sensor that senses the orientation of the harvester 100; and a crop property sensor that senses various types of crop properties, such as crop type, crop moisture, crop height, crop density, crop volume, and other crop properties. When the harvester 100 is processing crop material, the crop property sensor can also be configured to sense the characteristics of the cut crop material. For example, in some cases, crop property sensors can sense: grain quality, such as broken grain and MOG levels; grain composition, such as starch and protein; and the grain feed rate as the grain passes through feeder housing 106, clean grain elevator 130, or elsewhere in the harvester 100. Crop property sensors can also sense the feed rate of biomass through feeder housing 106, separator 116, or elsewhere in the harvester 100. Crop property sensors can also sense the feed rate as the mass flow rate of grain through elevator 130 or other parts of the harvester 100, or provide additional output signals indicating other sensed variables. Crop property sensors may include one or more yield sensors that sense the yield of the crop being harvested by the harvester.

[0038] Before describing how the agricultural harvester 100 generates a functional predictive characteristic map and uses that map for control, a brief description of some items on the agricultural harvester 100 and their operation will be provided first.

[0039] Figure 2 , Figure 3A and Figure 3B The drawing describes receiving a general type of prior information map and combining information from the prior information map with georegistered sensor signals generated by field sensors, wherein the sensor signals indicate one or more agricultural characteristics, such as one or more non-machine characteristics and / or one or more machine characteristics. Non-machine characteristics are any agricultural characteristics not related to a machine such as agricultural harvester 100. Non-machine characteristics can include a variety of characteristics, such as field characteristics. Field characteristics can include (but are not limited to): surface characteristics, such as topography, slope, surface quality, etc.; weed characteristics, such as weed density, weed type; soil properties, such as soil type, soil moisture, soil cover, soil structure, etc.; crop properties, such as crop height, crop volume, crop moisture, crop density, crop condition, etc.; grain properties, such as grain moisture, grain size, grain test weight, grain size, etc. Other non-machine characteristics are also within the scope of this disclosure. Machine characteristics are any agricultural characteristics related to a machine such as agricultural harvester 100. Machine characteristics can include a variety of different characteristics, such as machine setting or operating characteristics, such as ground speed, header height, header orientation, machine heading, threshing drum drive force, engine load, and a variety of other machine setting or operating characteristics. Machine characteristics can also include characteristics of various machine performance, such as loss level, working quality, fuel consumption, and power utilization, and a variety of other machine performance characteristics. Other machine characteristics are within the scope of this disclosure. A relationship is determined between characteristic values ​​obtained from field sensor signals and values ​​from a priori information map, and this relationship is used to generate a new functional prediction map. The functional prediction map predicts values ​​at different geographic locations in the field, and one or more of those values ​​can be used to control the machine, such as for controlling 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 operating machine (which could be an agricultural harvester). The functional prediction map can be presented to the user visually (e.g., via a display), tactilely, or audibly. The user can interact with the functional prediction map to perform editing operations and other user interface operations. In some cases, functional prediction maps can be used to control agricultural machinery (e.g., agricultural harvesters), to be presented to operators or other users, and to be presented to operators or users to facilitate operator or user interaction, or one or more of these functions.

[0040] In reference Figure 2 , Figure 3A and Figure 3B After describing the general method, refer to Figure 4 and... Figure 5 More specific methods are described for generating functional predictive agricultural characteristic maps that can be presented to operators or users and / or used to control agricultural harvesters 100. Similarly, although this discussion is directed toward agricultural harvesters (specifically, combine harvesters), the scope of this disclosure extends to other types of agricultural harvesters or other agricultural operating machines.

[0041] Figure 2 This is a block diagram showing some parts of an example agricultural harvester 100. Figure 2 The agricultural harvester 100, as illustrated, includes one or more processors or servers 201, a data storage device 202, a geolocation sensor 204, a communication system 206, and one or more field sensors 208 that sense one or more agricultural characteristics of the field simultaneously with the harvesting operation. Agricultural characteristics can include any characteristics that can influence the harvesting operation. Some examples of agricultural characteristics include characteristics of the harvesting machine, characteristics of the field, characteristics of the plants in the field, and characteristics of the weather. Other types of agricultural characteristics are also included. The field sensors 208 generate values ​​corresponding to the sensed characteristics. The agricultural harvester 100 also includes a prediction model or relation generator (collectively referred to as "prediction model generator 210"), a prediction map generator 212, a control area generator 213, a control system 214, one or more controllable subsystems 216, and an operator interface mechanism 218. The agricultural harvester 100 may also include various other agricultural harvester functions 220. For example, field sensors 208 include airborne sensors 222, remote sensors 224, and other sensors 226 that sense field characteristics during agricultural operations. Predictive model generator 210 exemplarily includes a prior information variable-to-field variable model generator 228, and may include other items 230. Control system 214 includes a communication system controller 229, an operator interface controller 231, a setting controller 232, a path planning controller 234, a feed rate controller 236, a header and reel controller 238, a belt conveyor controller 240, a cover position controller 242, a residue system controller 244, a machine cleaning controller 245, a zone controller 247, and may include other items 246. The controllable subsystem 216 includes machine and header actuator 248, propulsion subsystem 250, steering subsystem 252, residue subsystem 138, machine grain cleaning subsystem 254, and the controllable subsystem 216 may include various other subsystems 256.

[0042] Figure 2The agricultural harvester 100 is also shown to receive a priori information map 258. As described below, for example, the priori information map 258 includes, for example, a soil property map or a soil property map from a previous or prior operation. However, the priori information map 258 may also encompass other types of data obtained prior to the harvesting operation or maps from prior or previous operations. Figure 2 The diagram also shows an operator 260 capable of operating an agricultural harvester 100. The operator 260 interacts with an operator interface mechanism 218. In some examples, the operator interface mechanism 218 may include a joystick, joystick, steering wheel, linkage, pedals, buttons, dials, keypad, user-actuable elements on a user interface display (e.g., icons, buttons, etc.), microphone and speaker (where speech recognition and speech synthesis are provided), and various other types of control devices. Where a touch-sensitive display system is provided, the operator 260 may interact with the operator interface mechanism 218 using touch gestures. The examples provided above are exemplary and not intended to limit the scope of this disclosure. Therefore, other types of operator interface mechanisms 218 may also be used and are within the scope of this disclosure.

[0043] Using communication system 206 or other methods, prior information map 258 can be downloaded to agricultural harvester 100 and stored in data storage device 202. In some examples, communication system 206 may be a cellular communication system, a system communicating via a wide area network or local area network, a system communicating via a near-field communication network, or a communication system configured to communicate via any or a combination of various other networks. Communication system 206 may also include a system for facilitating the download or transfer of information to and from a Secure Digital (SD) card or a Universal Serial Bus (USB) card, or both.

[0044] The geolocation sensor 204 exemplarily senses or detects the geolocation or orientation of the agricultural harvester 100. The geolocation sensor 204 may include (but is not limited to) a Global Navigation Satellite System (GNSS) receiver that receives signals from a GNSS satellite transmitter. The geolocation sensor 204 may also include a Real-Time Kinematic (RTK) component configured to enhance the accuracy of position data derived from GNSS signals. The geolocation sensor 204 may include any of a dead reckoning system, a cellular triangulation system, or a variety of other geolocation sensors.

[0045] Field sensor 208 can be one of the above-mentioned... Figure 1Any sensor described. Field sensor 208 includes onboard sensor 222 mounted on the agricultural harvester 100, or field sensor 208 may also include remote field sensor 224 for capturing field information. Such sensors may include (but are not limited to): soil property sensors, crop moisture sensors, weed density sensors, weed location sensors, weed type sensors, yield sensors, biomass sensors, crop status sensors, power or dynamic characteristic sensors, speed sensors, machine orientation (pitch, roll, direction) sensors, waste sensors, grain quality sensors, internal material distribution sensors, stalk characteristic sensors, crop height sensors, residue sensors, etc. In some examples, field sensors may include (but are not limited to): sensing sensors (e.g., forward-looking monocular or stereo camera systems and image processing systems), and image sensors inside the agricultural harvester 100 (e.g., one or more clean grain cameras mounted to identify or determine the characteristics of vegetation traveling through the agricultural harvester 100). Field data includes data acquired from sensors on the agricultural harvester or, where data is detected during harvesting operations, data acquired by any sensor.

[0046] Predictive model generator 210 generates models indicating the relationship between values ​​sensed by field sensors 208 and values ​​mapped to the field via prior information map 258. For example, if prior information map 258 maps soil property values ​​to different locations in the field, and field sensors 208 are sensing values ​​indicating biomass, then prior information variable to field variable model generator 228 generates a predictive biomass model that models the relationship between soil property values ​​and biomass values. This is because a variety of different soil properties can indicate the growth of vegetation (including crop plants) in the field of interest. For example, soil moisture levels and the type of soil used as a growth medium can affect the growth of crops (and other vegetation) in the field and the resulting biomass. Soil properties and biomass are merely examples, and soil properties can involve other characteristics sensed by one or more field sensors 208 that predictive model generator 210 can use to generate models.

[0047] A predictive model can also be generated based on soil property values ​​from prior information map 258 and multiple field data values ​​generated by field sensors 208. Then, the predictive map generator 212 uses the predictive model generated by the predictive model generator 210, based on prior information map 258, to generate a functional predictive map 263, which predicts the values ​​of the properties sensed by field sensors 208 at different locations in the field.

[0048] In the example where prior information map 258 is a soil property map and field sensor 208 senses values ​​indicating characteristics, prediction map generator 212 can use the soil property values ​​in prior information map 258 and a model generated by prediction model generator 210 to generate a functional prediction map 263 that predicts characteristics at different locations in the field. Therefore, prediction map generator 212 outputs prediction map 264.

[0049] 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 a different unit 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 related 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 prior information graph 258. In some cases, the type of data in functional prediction graph 263 may have a different unit than the data in prior information graph 258. In some examples, the type of data in functional prediction graph 263 may be different from the type of data in prior information graph 258, but related to the type of data in prior information graph 258. For example, in some examples, the data type in the prior information graph 258 may indicate the type of data in the functional prediction graph 263. In some examples, the type of data in the functional prediction graph 263 differs from one or both of the field data type sensed by the field sensor 208 and the data type in the prior information graph 258. In some examples, the type of data in the functional prediction graph 263 is the same as one or both of the field data type sensed by the field sensor 208 and the data type in the prior information graph 258. In 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 prior information graph 258, and different from the other.

[0050] like Figure 2As shown, prediction graph 264 predicts the value of a sensed characteristic (sensed by field sensor 208) or a characteristic related to the sensed characteristic at multiple locations across the field, based on prior information values ​​in prior information graph 258 at multiple locations across the field and a prediction model. For example, if prediction model generator 210 has generated a prediction model indicating the relationship between soil property values ​​and yield, then given soil property values ​​at different locations across the field, prediction graph generator 212 generates prediction graph 264 predicting the yield values ​​at those different locations across the field. Prediction graph 264 is generated using the soil property values ​​at those locations obtained from the soil property map and the relationship between soil property values ​​and yield obtained from the prediction model. This is because a variety of different soil properties can indicate the growth or health of vegetation (including crop plants) in the field of interest. For example, soil moisture levels and the type of soil used as a growth medium can affect crop growth in the field and the resulting yield. Soil properties and yield are merely examples. Soil properties may involve a variety of other characteristics sensed by one or more field sensors 208, upon which the prediction model generator 210 can generate a model. The prediction model generator 210 can generate a prediction model indicating the relationship between soil property values ​​and any one of the multiple characteristics sensed by the field sensors 208 or any one of the multiple characteristics associated with the sensed characteristics, and the prediction map generator 212 can generate a prediction map 264 that predicts the values ​​of high characteristics at different locations in the field. The soil property values ​​at those locations obtained from the soil property map and the relationship between the soil property values ​​and characteristics obtained from the prediction model can be used to generate the prediction map 264.

[0051] The following will describe some changes to the data types mapped in prior information graph 258, the data types sensed by field sensor 208, and the data types predicted in prediction graph 264.

[0052] In some examples, the data type in the prior information map 258 differs from the data type sensed by the field sensor 208, while the data type in the prediction map 264 is the same as that sensed by the field sensor 208. For example, the prior information map 258 could be a soil property map, and the variable sensed by the field sensor 208 could be yield. Therefore, the prediction map 264 could be a predicted yield map mapping the predicted yield values ​​to different geographical locations in the field. In another example, the prior information map 258 could be a soil property map, and the variable sensed by the field sensor 208 could be crop height. Therefore, the prediction map 264 could be a predicted crop height map mapping the predicted crop height values ​​to different geographical locations in the field.

[0053] Furthermore, in some examples, the data type in the prior information map 258 differs from the data type sensed by the field sensor 208, and the data type in the prediction map 264 differs from both the data type in the prior information map 258 and the data type sensed by the field sensor 208. For example, the prior information map 258 may be a soil property map, and the variable sensed by the field sensor 208 may be crop height. Therefore, the prediction map 264 may be a predicted biomass map mapping predicted biomass values ​​to different geographical locations in the field. In another example, the prior information map 258 may be a soil property map, and the variable sensed by the field sensor 208 may be yield. Therefore, the prediction map 264 may be a predicted speed map mapping predicted agricultural harvester speed values ​​to different geographical locations in the field.

[0054] In some examples, the prior information map 258 is derived from previous passes through the field during prior or previous operations, and its data type differs from that sensed by the field sensor 208, while the data type in the prediction map 264 is the same as that sensed by the field sensor 208. For example, the prior information map 258 could be a soil property map, and the variable sensed by the field sensor 208 could be stem size. Thus, the prediction map 264 could be a predicted stem size map mapping the predicted stem size values ​​to different geographic locations in the field. In another example, the prior information map 258 could be a soil property map, and the variable sensed by the field sensor 208 could be crop state, such as upright or lodged crops. Thus, the prediction map 264 could be a predicted crop state map mapping the predicted crop state values ​​to different geographic locations in the field.

[0055] In some examples, the prior information map 258 is derived from previous passes through the field during a prior or previous operation, and the data type is the same as that sensed by the field sensor 208, and the data type in the prediction map 264 is also the same as that sensed by the field sensor 208. For example, the prior information map 258 could be a soil property map generated in the previous year, and the variable sensed by the field sensor 208 could be soil properties. Thus, the prediction map 264 could be a predicted soil property map that maps predicted soil property values ​​to different geographic locations in the field. In this example, the prediction model generator 210 can use the relative soil property differences in the georegistered prior information map 258 from the previous year to generate a prediction model that models the relationship between the relative soil property differences on the prior information map 258 and the soil property values ​​sensed by the field sensor 208 during the current harvest operation. The prediction map generator 210 then uses the prediction model to generate a predicted soil property map.

[0056] In another example, the prior information map 258 may be a soil property map generated during, for example, a previous or prior operation from a sprayer or seeder, and the variables sensed by the field sensor 208 may be soil properties. Thus, the prediction map 264 may be a predicted soil property map that maps predicted soil property values ​​to different geographic locations in the field. In such an example, a map of soil properties at the time of spraying or seeding is georegistered, recorded, and provided to the agricultural harvester 100 as a prior information map 258 of one or more soil properties. The field sensor 208 may detect one or more soil properties at geographic locations in the field, and then the prediction model generator 210 may build a predictive model that models the relationship between the soil properties at harvest and the soil properties at the time of spraying or seeding.

[0057] In some examples, prediction map 264 may be provided to control zone generator 213. Control zone generator 213 groups adjacent portions of a region into one or more control zones based on data values ​​associated with adjacent portions of the prediction map 264. A control zone may include two or more consecutive portions of a region (e.g., a field), for which the control parameters corresponding to the control zone for controlling the controllable subsystem are constant. For example, changing the response time of the controllable subsystem 216 settings may not satisfactorily respond to changes in values ​​contained in a map such as prediction map 264. In this case, control zone generator 213 parses the map and identifies control zones with defined dimensions adapted to the response time of the controllable subsystem 216. In another example, control zones may be sized to reduce wear caused by excessive actuator movement due to continuous adjustment. 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 prediction control zone map 265. Therefore, except that the predicted control area map 265 includes control area information defining the control area, the predicted control area map 265 may be similar to the predicted map 264. Thus, as described herein, the functional predicted map 263 may or may not include control areas. Both predicted map 264 and predicted control area map 265 are functional predicted maps 263. In one example, the functional predicted map 263 does not include control areas (e.g., predicted map 264). In another example, the functional predicted map 263 does include control areas (e.g., predicted control area map 265). In some examples, if an intercropping production system is implemented, multiple crops may coexist in the field. In this case, the predicted map generator 212 and the control area generator 213 are able to identify the location and characteristics of two or more crops and then generate the predicted map 264 and the predicted map 265 with control areas accordingly.

[0058] It will also be understood that the control region generator 213 can cluster values ​​to generate control regions, and these control regions can be added to the predicted control region map 265 or to a separate map that only displays the generated control regions. In some examples, the control regions can be used to control and / or calibrate the agricultural harvester 100. In other examples, the control regions can be presented to the operator 260 for controlling or calibrating the agricultural harvester 100, and in still other examples, the control regions can be presented to the operator 260 or another user, or stored for later use.

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

[0060] The operator interface controller 231 is operable to generate control signals to control the operator interface mechanism 218. The operator interface controller 231 is also operable to present the predictive map 264 or the predictive control area map 265, or other information derived from or based on the predictive map 264, the predictive control area map 265, or both, to the operator 260. The operator 260 can be a local operator or a remote operator. As an example, the controller 231 generates control signals to control the display mechanism to display one or both of the predictive map 264 and the predictive control area map 265 to the operator 260. The controller 231 can generate an operator-actuable mechanism that is displayed and can be actuated by the operator to interact with the displayed map. The operator can edit the map by, for example, correcting characteristics displayed on the map based on the operator's observation. The setting controller 232 can generate control signals for various settings on the agricultural harvester 100 based on the predictive map 264, the predictive control area map 265, or both. For example, the setting controller 232 can generate control signals to control the machine and header actuator 248. In response to the generated control signals, the machine and header actuator 248 operate to control, for example, screen and chaff screen settings, concave plate clearance, drum settings, grain clearing fan speed settings, header height, header function, reel speed, reel position, belt conveyor function (where the harvester 100 is coupled to a belt conveyor header), grain header function, internal distribution control, and other actuators 248 affecting other functions of the harvester 100. The path planning controller 234 exemplarily generates control signals to control the steering subsystem 252 to turn the harvester 100 according to a desired path. The path planning controller 234 can control the path planning system to generate a route for the harvester 100 and can control the propulsion subsystem 250 and steering subsystem 252 to turn the harvester 100 along that route. The feed rate controller 236 can control various subsystems, such as the propulsion subsystem 250 and the machine actuator 248, to control the feed rate or throughput based on prediction map 264 or prediction control area map 265, or both. For example, as the agricultural harvester 100 approaches an upcoming crop area in the field with a biomass value higher than a selected threshold, the feed rate controller 236 can reduce the speed of the machine 100 to maintain a constant feed rate of biomass through the machine. The header and reel controller 238 can generate control signals to control the header or reel or other header functions. The belt conveyor controller 240 can generate control signals based on prediction map 264, prediction control area map 265, or both, to control the belt conveyor belt or other belt conveyor functions.The cover position controller 242 can generate control signals based on prediction diagram 264 or prediction control area diagram 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 prediction diagram 264 or prediction control area diagram 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. For example, based on the different types of seeds or weeds passing through the machine 100, a specific type of machine cleaning operation or the frequency of performing cleaning operations can be controlled. Other controllers included on the agricultural harvester 100 can also control other subsystems based on prediction diagram 264 or prediction control area diagram 265, or both.

[0061] Figure 3A and Figure 3B A flowchart is shown, illustrating an example of the operation of an agricultural harvester 100 in generating a prediction map 264 and a prediction control area map 265 based on prior information map 258.

[0062] At box 280, the agricultural harvester 100 receives a priori information map 258. Examples of priori information map 258 or receiving priori information map 258 are discussed with reference to boxes 281, 282, 284, and 286. As described above, priori information map 258 maps the value of a variable corresponding to a first characteristic to different locations in the field, as indicated by box 282. As indicated at box 281, receiving priori information map 258 may involve selecting one or more of a plurality of possible priori information maps available. For example, one priori information map may be a soil property map generated from aerial imagery. Another information map may be a map generated during previous passage through the field, which may be performed by different machines (such as sprayers or other machines) performing prior operations in the field. The process of selecting one or more information maps may be manual, semi-automatic, or automatic. Furthermore, a priori information map can be selected based on the similarity or difference between the current condition or characteristics of the field of interest and the condition or characteristics of the same field (or other fields) on which the previous map was based, for example, based on the similarity or difference in weather conditions or soil properties. Priori information map 258 is based on data collected prior to the current harvesting operation. This is indicated by box 284. For example, data can be collected based on aerial images acquired in the previous year or early in the current growing season, or at other times.

[0063] The data can be based on data detected in ways other than using aerial imagery. For example, the agricultural harvester 100 or another machine can be equipped with one or more sensors configured to sense a variety of different soil properties or characteristics, such as soil type, soil moisture, soil cover, or soil structure. Furthermore, the agricultural harvester 100 or another machine can be equipped with one or more sensors configured to sense the topography of the field of interest, and based on the topography and a variety of other data, such as weather characteristics (e.g., precipitation and wind) or residue characteristics (e.g., the height of remaining plant stalks), a variety of different soil properties can be predicted. For example, based on the topography of the field and precipitation levels, a variety of different soil properties of a field area can be predicted. For example, soil moisture in the low-lying areas of a field is generally higher than soil moisture in the higher areas of the field. In another example, water retention capacity, and thus soil moisture, is generally affected by the amount of remaining residue in the field (e.g., the height or mass of remaining crop stalks). These are merely examples. The data used for the prior information figure 258 can be transmitted to the agricultural harvester 100 using the communication system 206 and stored in the data storage device 202. Alternatively, the prior information map 258 can be provided to the agricultural harvester 100 in other ways using the communication system 206, and this is done by... Figure 3A Box 286 in the flowchart indicates this. In some examples, prior information can be received by communication system 206 (Figure 258).

[0064] At the start of the harvesting operation, field sensor 208 generates sensor signals indicating one or more field data values, which indicate agricultural characteristics such as non-machine characteristics (e.g., characteristics of the field) or machine characteristics (e.g., characteristics of machine settings, operating characteristics, or machine performance), as indicated in box 288. Examples of field sensor 208 are discussed with reference to boxes 222, 290, and 226. As described above, field sensor 208 includes: an airborne sensor 222; a remote field sensor 224, such as a UAV-based sensor that flies once to collect field data (shown in box 290); or other types of field sensors specified by field sensor 226. In some examples, position, heading, or speed data from geolocation sensor 204 is used to georeference the data from the airborne sensor.

[0065] Predictive model generator 210 controls prior information variables to pair with field variable model generator 228 to generate a model that models the relationship between the values ​​mapped in prior information graph 258 and the field values ​​sensed by field sensor 208, as indicated by box 292. The characteristics or data types represented by the values ​​mapped in prior information graph 258 and the field values ​​sensed by field sensor 208 can be the same or different characteristics or data types.

[0066] The relation or model generated by the prediction model generator 210 is provided to the prediction map generator 212. The prediction map generator 212 uses the prediction model and prior information map 258 to generate a prediction map 264, which predicts the values ​​of different characteristics sensed by the field sensor 208 or related to the characteristics sensed by the field sensor 208 at different geographical locations in the harvesting field, as indicated by box 294.

[0067] It should be noted that in some examples, the prior information map 258 may include two or more different maps, or two or more different layers of a single map. Each layer may represent a data type different from that of another layer, or the layers may have the same data type acquired at different times. The individual maps in the two or more different maps, or the individual layers in the two or more different layers of a map, map different types of variables to geographical locations in the field. In this example, the predictive model generator 210 generates a predictive model that models the relationship between field data and the different variables mapped by the two or more different maps or two or more different layers. Similarly, the field sensors 208 may include two or more sensors, each sensing different types of variables. Therefore, the predictive model generator 210 generates a predictive model that models the relationship between the various types of variables mapped by the prior information map 258 and the various 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 prior information map 258 to generate a functional prediction map 263 that 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 harvesting field.

[0068] Prediction map generator 212 configures prediction map 264 such that prediction map 264 can be manipulated (or used) by control system 214. Prediction map generator 212 may provide prediction map 264 to control system 214 or control area generator 213 or both. Some examples of different ways in which prediction map 264 can be configured or output will be described with reference to boxes 296, 295, 299 and 297. For example, prediction map generator 212 configures prediction map 264 such that prediction map 264 includes values ​​that can be read by control system 214 and used as the basis for generating control signals for one or more different controllable subsystems of agricultural harvester 100, as indicated in box 296.

[0069] Control zone generator 213 can divide prediction map 264 into control zones based on values ​​on prediction map 264. Geographically contiguous values ​​within each other's thresholds can be grouped into a control zone. This threshold can be a default threshold, or it can be set based on operator input, input from the automation system, or other criteria. The size of the zones can be based on the responsiveness of control system 214, controllable subsystem 216, wear considerations, or other criteria, as indicated in box 295. Prediction map generator 212 configures prediction map 264 for presentation to an operator or other user. Control zone generator 213 can configure prediction control zone map 265 for presentation to an operator or other user. This is indicated in box 299. When presented to an operator or other user, the presentation of prediction map 264 or prediction control area map 265, or both, may include geographic location-related predicted values ​​on prediction map 264, geographic location-related control areas on prediction control area map 265, and one or more setpoints or control parameters used based on the predicted values ​​on prediction map 264 or the areas on prediction control area map 265. In another example, the presentation may include more abstract or more detailed information. The presentation may also include a confidence level indicating the accuracy with which the predicted values ​​on prediction map 264 or the areas on prediction control area map 265 conform to measurements that can be measured by sensors on the agricultural harvester 100 as the 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 implement the verification and authorization process. For example, a hierarchy of individuals may exist who are authorized to view and modify information in the maps and other presentations. As an example, an onboard display device may display the maps locally on the machine in approximately real-time, and / or may generate the maps at one or more remote locations. In some examples, each physical display device at each location may be associated with a person or user permission level. The user permission level 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 an agricultural harvester 100 may not be able to see the information corresponding to prediction map 264 or make any changes to the machine's operation. However, a supervisor (e.g., a supervisor at a remote location) may be able to see prediction map 264 on the display but is prevented from making any changes. A manager at a separate remote location may be able to see all elements on prediction map 264 and also be able to change prediction map 264. In some cases, prediction map 264, accessible and modifiable by a manager at a remote location, can be used for machine control. This is an example of an achievable authorization hierarchy. Prediction map 264 or prediction control area map 265, or both, may also be configured in other ways, as indicated by box 297.

[0070] In box 298, the control system receives input from the geolocation sensor 204 and other field sensors 208. Specifically, in box 300, the control system 214 detects input from the geolocation sensor 204 identifying the geolocation of the harvester 100. Box 302 indicates that the control system 214 receives sensor input indicating the trajectory or heading of the harvester 100, and box 304 indicates that the control system 214 receives the speed of the harvester 100. Box 306 indicates that the control system 214 receives additional information from various field sensors 208.

[0071] At block 308, control system 214 generates a control signal to control controllable subsystem 216 based on prediction map 264 or prediction control area map 265, or both, and inputs from geographic location sensor 204 and any other field sensors 208. At block 310, control system 214 applies the control signal to the controllable subsystem. It will be understood that the specific control signal generated and the specific controllable subsystem 216 being controlled can vary based on one or more different things. For example, the generated control signal and the controllable subsystem 216 being controlled can be based on the type of prediction map 264 or prediction control area map 265, or both, being used. Similarly, the timing of the generated control signal, the controllable subsystem 216 being controlled, and the control signal can be based on various delays in the crop flow through agricultural harvester 100 and the responsiveness of controllable subsystem 216.

[0072] As an example, the generated prediction map 264, in the form of a predicted biomass map, can be used to control one or more subsystems 216. For example, the predicted biomass map may include biomass values ​​georeferenced to locations within a harvesting field. Biomass values ​​from the predicted biomass 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 harvester 100 can be controlled. Similarly, the header height can be controlled to introduce more or less material, and therefore, the header height can also be controlled to control the feed rate of material through the harvester 100. In other examples, if the prediction map 264 maps yields associated with locations in the field, control of the harvester 100 can be implemented. For example, if values ​​present in the predicted yield map indicate that the yield at one portion of the header 102 in front of the harvester 100 is higher than the yield at another portion of the header 102, control of the header 102 can be implemented. For example, the belt conveyor speed on one side of the header 102 can be increased or decreased relative to the belt conveyor speed on the other side of the header 102 to address additional biomass. Therefore, the header and reel controller 238 can be controlled using georegulated values ​​present in the predicted yield map to control the belt conveyor speed on the header 102. Furthermore, as the harvester 100 advances through the field using georegulated values ​​obtained from the predicted biomass map or the predicted yield map, and using georegulated values ​​obtained from various other predicted maps, the header and reel controller 238 can automatically change the header height. The foregoing examples of using predicted maps involving biomass and yield are provided only as examples. Therefore, various other control signals can be generated using values ​​obtained from predicted biomass maps, predicted yield maps, or other types of predicted maps derived from prior soil property maps to control one or more of the controllable subsystems 216. The predicted map can be any of several predicted agricultural maps that map predicted values ​​of agricultural characteristics to different locations in the field of interest.

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

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

[0075] Learning triggering criteria can include any of a variety of different criteria. Some examples of triggering criteria detection are discussed with reference to boxes 318, 320, 321, 322, and 324. For example, in some examples, triggered learning may involve recreating the relationships used to generate a predictive model when a threshold amount of field sensor data is received from field sensor 208. In these examples, receiving a threshold amount of field sensor data from field sensor 208 triggers or causes predictive model generator 210 to generate a new predictive model used by predictive map generator 212. Thus, as the agricultural harvester 100 continues its harvesting operation, receiving a threshold amount of field sensor data from field sensor 208 triggers the creation of a new relationship represented by the predictive model generated by predictive model generator 210. Furthermore, a new predictive map 264, predictive control area map 265, or both can be regenerated using the new predictive model. Box 318 indicates detecting a threshold amount of field sensor data used to trigger the creation of a new predictive model.

[0076] In other examples, the learning trigger criterion may be based on how much field sensor data from field sensor 208 has changed over time or compared to previous values. For example, if the change in the field sensor data (or the relationship between the field sensor data and the information in the prior information map 258) is within a selected range, less than a defined amount, or below a threshold, then the prediction model generator 210 does not generate a new prediction model. As a result, the prediction map generator 212 does not generate a new prediction map 264 and / or prediction control area map 265. However, for example, if the change in the field sensor data is outside the selected range, greater than a defined amount or threshold, or above a threshold, then the prediction model generator 210 uses all or part of the newly received field sensor data used by the prediction map generator 212 to generate a new prediction map 264 to generate a new prediction model. At box 320, changes in the field sensor data (e.g., the magnitude of the amount of data exceeding a selected range, or the magnitude of the change in the relationship between the field sensor data and the information in the prior information map 258) can be used as triggers to induce the generation of new prediction models and prediction maps. Continuing with the example described above, the threshold, range, and limited quantity can be set to default values, set by an operator or user through a user interface, set by an automation system, or otherwise.

[0077] Other learning trigger criteria can also be used. For example, if the predictive model generator 210 switches to a different prior infographic (different from the initially selected prior infographic 258), switching to a different prior infographic can trigger the predictive model generator 210, the predictive map generator 212, the control area generator 213, the control system 214, or other items to relearn. In another example, the agricultural harvester 100 changing to a different terrain or a different control area can also be used as a learning trigger criterion.

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

[0079] In some cases, operator 260 may also observe that the automatic control of the controllable subsystem is not as desired by the operator. In these cases, operator 260 may provide manual adjustments to the controllable subsystem, reflecting the operator's expectation that the controllable subsystem will operate in a manner different from that commanded by control system 214. Therefore, operator 260's manual change of settings may cause one or more of the following to occur based on the adjustments made by operator 260 (as shown in box 322): causing predictive model generator 210 to relearn the model, causing predictive graph generator 212 to regenerate graph 264, causing control area generator 213 to regenerate one or more control areas on predictive control area graph 265, and causing control system 214 to relearn the control algorithm or perform machine learning on one or more components of controller components 232 to 246 in control system 214. Box 324 indicates the use of other triggered learning criteria.

[0080] In other examples, relearning can be performed periodically or intermittently based on, for example, selected time intervals (e.g., discrete or variable time intervals), as indicated by box 326.

[0081] As indicated in box 326, if relearning is triggered (whether based on a learning trigger criterion or on a past time interval), one or more of the predictive model generator 210, predictive graph generator 212, control area generator 213, and control system 214 perform machine learning to generate a new predictive model, a new predictive graph, a new control area, and a new control algorithm, respectively, based on the learning trigger criterion. Any additional data collected since the last learning operation is performed is used to generate the new predictive model, new predictive graph, and new control algorithm. The execution of relearning is indicated in box 328.

[0082] If the harvesting operation is complete, the operation moves from box 312 to box 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 sent to a remote system using the communication system 206 for subsequent use.

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

[0084] Figure 4A yes Figure 1 A block diagram of a portion of an agricultural harvester 100. Specifically, among other things, Figure 4A Examples of the prediction model generator 210 and the prediction graph generator 212 are shown in more detail. Figure 4A The information flow between the various components is also shown. The predictive model generator 210 receives soil property map 332 as a priori information map. The predictive model generator 210 also receives geographic location 334 or an indication of geographic location from geographic location sensor 204. Field sensors 208 illustratively include agricultural characteristic sensors (e.g., agricultural characteristic sensor 336) and processing system 338. In some cases, agricultural characteristic sensor 336 may be located on agricultural harvester 100.

[0085] The characteristics detected by the processing system 338 may include any of a variety of agricultural characteristics, such as non-machinery characteristics, such as characteristics of the field or characteristics of the plants (e.g., crops) on the field, such as characteristics indicating biomass or yield, and a variety of other non-machinery characteristics. The agricultural characteristics detected by the processing system 338 may include any of a variety of machine characteristics of the harvester 100 or another machine, such as machine settings, operating characteristics, or machine performance characteristics, such as the height of the header 102 above the field, the force required to drive the threshing drum 112, the forward speed of the harvester 100, and a variety of other machine characteristics. Therefore, the field sensor 208 may be any of a variety of sensors capable of detecting agricultural characteristics (e.g., non-machinery characteristics or machine characteristics of the harvester 100 or another machine) and related characteristics.

[0086] Processing system 338 processes sensor data generated from agricultural characteristic sensor 336 to generate processed data, some examples of which are described below. For example, agricultural characteristic sensor 336 may be an optical sensor, such as a camera or other device performing optical sensing. The optical sensor may generate images indicating a variety of different agricultural characteristics (such as non-machine characteristics, or machine characteristics of harvester 100 or another machine, and related characteristics). Processing system 338 processes one or more sensor signals (e.g., images) obtained via the optical sensor to generate processed sensor data (e.g., processed image data) identifying one or more non-machine characteristics (e.g., characteristics of the field), or processed sensor data identifying one or more machine characteristics (such as machine settings, operating characteristics, or machine performance characteristics) or related characteristics of harvester 100.

[0087] The processing system 338 can also geolocate values ​​received from the field sensor 208. For example, the location of the harvester 100 at the moment it receives a signal from the field sensor 208 is typically not its exact location in the field. This is because a period of time may have elapsed between the moment the harvester initially contacts the agricultural feature and the moment the field sensor senses the feature. Therefore, when georegistering the sensed data, the transient time between the moment the harvester 100 initially encounters the agricultural feature and the moment the feature is sensed by the field sensor 208 is taken into account. By doing so, the sensed agricultural feature can be accurately georegistered to its location in the field. By way of example, as the cut crop travels along the header in a direction transverse to the harvester's direction of travel, the yield value is typically geolocated to a V-shaped area behind the harvester as the harvester travels forward. Processing system 338 allocates or distributes the total yield detected by yield sensors during each time interval or measurement interval back to an earlier georegistered area based on the crop travel time from different parts of the harvester (e.g., different lateral positions along the width of the harvester's header) and the harvester's ground speed. For example, processing system 338 allocates the total yield measured from a measurement interval or time back to georegistered areas traversed by the harvester's header during different measurement intervals or times. Processing system 338 also distributes or assigns the total yield from a specific measurement interval or time to a previously traversed georegistered area, which is part of a V-shaped region.

[0088] Similarly, in the example where the field sensor 208 is a threshing drum drive force sensor that generates a sensor signal indicating biomass, the processing system 338 can also geolocate a value (e.g., a biomass value) by calculating the time delay between the moment the crop is encountered in the field and the moment the crop will be threshed, for example, by the threshing drum 112, such that the threshing drum drive force characteristic can be georegistered as an indicator of biomass to a precise location in the field. This time delay can be based at least in part on the forward speed of the agricultural harvester 100.

[0089] In other examples, field sensors 208, including agricultural characteristic sensor 336, may rely on the wavelength of electromagnetic energy and how the electromagnetic energy is reflected, absorbed, attenuated, or transmitted through the biomass or harvested grain, for example. Characteristic sensor 336 may sense other electromagnetic properties of the biomass or harvested grain, such as its dielectric constant, as the material passes between two capacitive plates. Characteristic sensor 336 may also rely on the mechanical properties of the biomass or grain, such as the signal generated when the grain strikes a piezoelectric element (e.g., a piezoelectric plate) or when that impact is detected by a microphone or accelerometer. Other material properties and sensors may also be used. In some examples, raw or processed data from characteristic sensor 336 may be presented to operator 260 via operator interface mechanism 218. Operator 260 may be on the operating agricultural harvester 100 or at a remote location.

[0090] This discussion is conducted with reference to an example in which the agricultural characteristic sensor 336 is configured to sense agricultural characteristics, such as non-machine characteristics or machine characteristics of the harvester 100 or another machine, and related characteristics. For the purposes of this disclosure, a non-machine characteristic is any agricultural characteristic unrelated to the machine. For example, a non-machine characteristic may include characteristics of the field on which the harvester 100 operates, characteristics of the plants (e.g., crops) in the field, and characteristics of the harvested plant material (e.g., harvested crop material). It should be understood that non-machine characteristics may be sensed externally to or internally to the harvester 100. For the purposes of this disclosure, a machine characteristic is any agricultural characteristic associated with the machine (e.g., the harvester 100 or another machine). For example, a machine characteristic may include machine settings, operating characteristics, or machine performance characteristics, as well as other machine characteristics. It should be noted that in some examples, a machine characteristic may also indicate a non-machine characteristic, or vice versa. For example, the threshing drum drive force (machine characteristic) may indicate biomass (non-machine characteristic). It should be understood that these are merely examples, and other examples of the aforementioned sensors as agricultural characteristic sensors 336 are also considered herein. Furthermore, it should be understood that the field sensors 208, including agricultural characteristic sensor 336, can sense any one of multiple agricultural characteristics. The predictive model generator 210, discussed below, can determine the relationship between any one of the multiple agricultural characteristics detected or represented in the sensor data at a geographic location corresponding to the sensor data and soil property values ​​from a soil property map (e.g., soil property map 332) corresponding to the same location in the field, and generate a predictive agricultural characteristic model based on this relationship. Moreover, it should be understood that the predictive map generator 212, discussed below, can use any one of the agricultural characteristic models generated by the predictive model generator 210 to predict any one of the multiple agricultural characteristics at the same location in the field based on georegistered soil type values ​​or georegistered soil moisture values ​​contained in soil property map 332 at different locations in the field.

[0091] like Figure 4A As shown, the example prediction model generator 210 includes one or more of the following: a non-machine characteristic to soil type model generator 342, a non-machine characteristic to soil moisture model generator 344, a machine characteristic to soil type model generator 346, and a machine characteristic to soil moisture model generator 347. In other examples, the prediction model generator 210 may include more than Figure 4AThe examples shown may include more, fewer, or different components. Therefore, in some examples, the predictive model generator 210 may also include additional items 348, which may include other types of predictive model generators to generate other types of characteristic models, such as other non-machine characteristic models or other machine characteristic models, for example, other non-machine characteristic to soil property models or other machine characteristic to soil property models. Other models may include any of a variety of soil properties (e.g., soil cover, soil structure, and a variety of other different soil properties).

[0092] The non-machinery characteristic to soil type model generator 342 determines the relationship between the non-machinery characteristic detected or represented in sensor data 340 at a geographic location corresponding to that sensor data 340 and the soil type value from soil property map 332 corresponding to the same location in the field associated with that non-machinery characteristic. Based on this relationship established by the non-machinery characteristic to soil type model generator 342, the non-machinery characteristic to soil type model generator 342 generates a predictive agricultural characteristic model. The non-machinery characteristic map generator 352 uses this predictive agricultural characteristic model to predict the non-machinery characteristic at the same location in the field based on the georegistered soil type values ​​at different locations in the field included in soil property map 332.

[0093] The non-machinery characteristic to soil moisture model generator 344 determines the relationship between the non-machinery characteristic detected or represented in sensor data 340 at a geographic location corresponding to that sensor data 340 and the soil moisture value from soil property map 332 corresponding to the same location in the field associated with that non-machinery characteristic. Based on this relationship established by the non-machinery characteristic to soil moisture model generator 344, the non-machinery characteristic to soil moisture model generator 344 generates a predictive agricultural characteristic model. The non-machinery characteristic map generator 352 uses this predictive agricultural characteristic model to predict the non-machinery characteristic at the same location in the field based on the georegistered soil moisture values ​​at different locations in the field included in soil property map 332.

[0094] The machine characteristic to soil type model generator 346 determines the relationship between the machine characteristic detected or represented in sensor data 340 at a geographic location corresponding to that sensor data 340 and the soil type value from soil property map 332 corresponding to the same location in the field associated with that machine characteristic. Based on this relationship established by the machine characteristic to soil type model generator 346, the machine characteristic to soil type model generator 346 generates a predictive agricultural characteristic model. The predictive agricultural characteristic model is used by the machine characteristic map generator 354 to predict the machine characteristic at the same location in the field based on the georegistered soil type values ​​at different locations in the field included in soil property map 332.

[0095] The machine characteristic to soil moisture model generator 347 determines the relationship between the machine characteristic detected or represented in sensor data 340 at a geographic location corresponding to that sensor data 340 and the soil moisture value from soil property map 332 corresponding to the same location in the field associated with that machine characteristic. Based on this relationship established by the machine characteristic to soil moisture model generator 347, the machine characteristic to soil moisture model generator 347 generates a predictive agricultural characteristic model. The predictive agricultural characteristic model is used by the machine characteristic map generator 354 to predict the machine characteristic at the same location in the field based on the georegistered soil moisture values ​​at different locations in the field included in soil property map 332.

[0096] In view of the foregoing, the prediction model generator 210 is operable to generate multiple prediction agricultural characteristic models, such as one or more of the prediction agricultural characteristic models generated by model generators 342, 344, 346, and 347. In another example, two or more of the above-mentioned prediction agricultural characteristic models can be combined into a single prediction characteristic model, which predicts two or more of non-machinery characteristics or machinery characteristics based on soil property values ​​at different locations in the field. Any one of these agricultural characteristic models or combinations thereof is generated by... Figure 4A The characteristic model 350 is uniformly represented in the model.

[0097] The predictive agricultural characteristic model 350 is provided to the predictive map generator 212. Figure 4AIn one example, the prediction map generator 212 includes a non-machinery characteristic map generator 352 and a machinery characteristic map generator 354. In other examples, the prediction map generator 212 may include more, fewer, or different map generators. Therefore, in some examples, the prediction map generator 212 may include additional items 358, which may include other types of map generators to generate characteristic maps for other types of characteristics. The non-machinery characteristic map generator 352 receives a predictive agricultural characteristic model 350 that predicts non-machinery characteristics based on soil property values ​​and a soil property map 332, and generates functional prediction maps that predict non-machinery characteristics at different locations in the field.

[0098] The machine characteristic map generator 354 receives a predictive agricultural characteristic model 350 that predicts machine characteristics based on soil property values ​​and a soil property map 332, and generates a functional prediction map that predicts machine characteristics at different locations in the field.

[0099] Prediction map generator 212 outputs one or more functional predicted agricultural characteristic maps 360, which predict one or more agricultural characteristics, such as one or more non-machinery characteristics or machinery characteristics, or one or more of both non-machinery and machinery characteristics. Each functional predicted agricultural characteristic map 360 maps the corresponding predicted agricultural characteristic at different locations in the field. Each generated functional predicted agricultural characteristic map 360 can be provided to control zone generator 213 and / or control system 214. Control zone generator 213 generates control zones and incorporates those control zones into the functional prediction maps (i.e., functional prediction maps 360) to provide functional prediction maps 360 with control zones. The functional prediction maps 360 (with or without control zones) can be provided to control system 214, which generates control signals based on the functional prediction maps 360 (with or without control zones) to control one or more controllable subsystems 216.

[0100] Figure 4B This is a block diagram showing some examples of the real-time (field) sensor 208. Figure 4B Some of the sensors or different combinations of sensors shown may have both sensor 336 and processing system 338. Figure 4B Some of the possible field sensors 208 shown are illustrated and described relative to the previous figures and are similarly numbered. Figure 4BThe field sensor 208 is shown to include an operator input sensor 980, a machine sensor 982, a harvested material property sensor 984, a field and soil property sensor 985, and an environmental property sensor 987, and may also include a variety of other sensors 226. The non-machine sensor 983 includes the operator input sensor 980, the harvested material property sensor 984, the field and soil property sensor 985, and the environmental property sensor 987, and may also include other sensors 226. The operator input sensor 980 may be a sensor that senses operator input via an operator interface mechanism 218. Therefore, the operator input sensor 980 can sense user movement via a linkage, joystick, steering wheel, button, dial, or pedal. The operator input sensor 980 can also sense user interaction with other operator input mechanisms, such as interaction with a touchscreen, with a microphone utilizing voice recognition, or with any of a variety of other operator input mechanisms.

[0101] Machine sensor 982 can sense various characteristics of the agricultural harvester 100. For example, as discussed above, machine sensor 982 may include machine speed sensor 146, separator loss sensor 148, clean grain camera 150, forward-view image capture mechanism 151, loss sensor 152, or geolocation sensor 204, examples of which are described above. Machine sensor 982 may also include machine setting sensor 991 for sensing machine settings. (See above references) Figure 1Examples of machine settings are described. A front-end device (e.g., header) position sensor 993 can sense the position of the header 102, reel 164, cutter 104, or other front-end devices relative to the frame of the harvester 100. For example, sensor 993 can sense the height of the header 102 above the ground. Machine sensor 982 may also include a front-end device (e.g., header) orientation sensor 995. Sensor 995 can sense the orientation of the header 102 relative to the harvester 100 or relative to the ground. Machine sensor 982 may include a stability sensor 997. Stability sensor 997 senses vibrational or bouncing movements (and amplitude) of the harvester 100. Machine sensor 982 may also include a residue setting sensor 999, configured to sense whether the harvester 100 is configured to shred residue, create a stockpile, or otherwise process residue. Machine sensor 982 may include a cleaning chamber fan speed sensor 951 that senses the speed of the cleaning fan 120. Machine sensor 982 may include a concave plate gap sensor 953 that senses the gap between the roller 112 and the concave plate 114 on the agricultural harvester 100. Machine sensor 982 may include a husk sieve gap sensor 955 that senses the size of the openings in the husk sieve 122. Machine sensor 982 may include a threshing drum speed sensor 957 that senses the drum speed of the roller 112. Machine sensor 982 may include a drum pressure sensor 959 that senses the pressure used to drive the roller 112. Machine sensor 982 may include a screen gap sensor 961 that senses the size of the openings in the screen 124. Machine sensor 982 may include a MOG humidity sensor 963 that senses the humidity level of the MOG passing through the harvester 100. Machine sensor 982 may include a machine orientation sensor 965 that senses the orientation of the harvester 100. Machine sensor 982 may include a material feed rate sensor 967 that senses the rate at which material is fed as it travels through the feeder housing 106, the clean grain elevator 130, or other locations within the harvester 100. Machine sensor 982 may include a biomass sensor 969 that senses the biomass traveling through the feeder housing 106, the separator 116, or other locations within the harvester 100. Machine sensor 982 may include a fuel consumption sensor 971 that senses the rate at which the harvester 100 consumes fuel over time.Machine sensor 982 may include a power utilization sensor 973 that senses power utilization in the harvester 100 (such as which subsystems are utilizing power), the rate at which subsystems are utilizing power, or the power distribution among the subsystems in the harvester 100. Machine sensor 982 may include a tire pressure sensor 977 that senses the inflation pressure in the tires 144 of the harvester 100. Machine sensor 982 may include a variety of other machine performance sensors or machine characteristic sensors (as shown in box 975). The machine performance sensors and machine characteristic sensors 975 can sense the machine performance or characteristics of the harvester 100.

[0102] While crop material is being processed by the agricultural harvester 100, the harvest material property sensor 984 can sense the characteristics of the cut crop material. Crop properties may include things such as crop type, crop moisture content, grain quality (e.g., broken grain), MOG level, grain composition (e.g., starch and protein), MOG moisture content, and other crop material properties. Other sensors can sense the "toughness" of the stalk, the adhesion of corn to the ear, and other characteristics that can be beneficially used to control the processing to achieve better grain capture, reduced grain damage, reduced power consumption, reduced grain loss, and so on.

[0103] The 985 field and soil property sensor can sense the characteristics of fields and soils. Field and soil properties may include soil moisture, soil compaction, presence and location of waterlogging, soil type, and other soil and field characteristics.

[0104] The environmental characteristic sensor 987 can sense one or more environmental characteristics. Environmental characteristics may include things such as wind direction and speed, precipitation, fog, dust level, or other obstacles or other environmental features.

[0105] Figure 5 This is a flowchart illustrating an example of the operation of the prediction model generator 210 and the prediction map generator 212 in generating a prediction agricultural property model 350 and a functional prediction agricultural property map 360. At box 362, the prediction model generator 210 and the prediction map generator 212 receive a priori soil property map 332. At box 364, the processing system 338 receives one or more sensor signals from field sensors 208 (e.g., property sensors 336). As discussed above, the field sensors 208 can be: agricultural property sensors 336, such as non-machine property sensors, as indicated in box 366; machine property sensors, as indicated in box 368; or another type of agricultural property sensor, as indicated in box 370.

[0106] At box 372, processing system 338 processes one or more received sensor signals to generate sensor data indicating agricultural characteristics present in the one or more sensor signals. At box 374, the sensor data may indicate one or more non-machinery characteristics corresponding to a location on the field. In some cases, as indicated in box 376, the sensor data may indicate a machine characteristic corresponding to a location on the field. In some cases, as indicated in box 380, the sensor data may indicate any one of a plurality of agricultural characteristics.

[0107] At box 382, ​​the prediction model generator 210 also acquires the geographic location corresponding to the sensor data. For example, the prediction model generator 210 can acquire a geographic location or a geographic location indication from the geographic location sensor 204 and determine the precise geographic location on the field corresponding to the sensor data based on machine delay, machine speed, etc., for example, by generating sensor signals or deriving the precise geographic location of sensor data 340 from them.

[0108] At box 384, prediction model generator 210 generates one or more prediction models (e.g., agricultural characteristic prediction model 350) that model the relationship between soil property values ​​obtained from a priori information map (e.g., soil property map 332) and characteristics or related characteristics sensed by field sensor 208. For example, prediction model generator 210 may generate a prediction model that models the relationship between soil property values ​​and sensed agricultural characteristics, such as non-machine or machine characteristics indicated by sensor data obtained from field sensor 208.

[0109] At box 386, a predictive model, such as predictive agricultural characteristic model 350, is provided to predictive map generator 212. Predictive map generator 212 generates a functional predictive agricultural characteristic map 360 based on a soil property map or soil property values ​​in a soil property map and predictive agricultural characteristic model 350. This functional predictive agricultural characteristic map 360 maps predicted agricultural characteristics or predicted agricultural characteristic values. For example, in some examples, predictive agricultural characteristic map 360 predicts non-machinery characteristics or machine characteristics. In some examples, functional predictive characteristic map 360 predicts non-machinery characteristics, as indicated in box 388. In some examples, functional predictive agricultural characteristic map 360 predicts machine characteristics, as indicated in box 390, and in other examples, predictive map 360 predicts other items, as indicated in box 392. For example, functional predictive agricultural characteristic map 360 can predict one or more non-machinery characteristics and one or more machine characteristics. Furthermore, functional predictive agricultural characteristic map 360 can be generated during agricultural operations. Therefore, as agricultural harvesters move across fields to perform agricultural operations, functional predictive agricultural characteristic maps 360 are generated during the performance of these operations.

[0110] At block 394, the prediction map generator 212 outputs a functional predicted agricultural characteristic map 360. At block 391, the prediction map generator 212 outputs the functional predicted agricultural characteristic map to present to the operator 260 and to facilitate possible interaction with the operator 260. At block 393, the prediction map generator 212 can configure the functional predicted agricultural characteristic map for use by the control system 214. At block 395, the prediction map generator 212 can also provide the functional predicted agricultural characteristic map 360 to the control area generator 213 for the generation of control areas. At block 397, the prediction map generator 212 also configures the functional predicted agricultural characteristic map 360 in other ways. The functional predicted agricultural characteristic map 360 (with or without control areas) is provided to the control system 214. At block 396, the control system 214 generates control signals based on the prediction characteristic map 360 to control the controllable subsystem 216.

[0111] Control system 214 can generate control signals to control header or other machine actuators 248. Control system 214 can generate control signals to control propulsion subsystem 250. Control system 214 can generate control signals to control steering subsystem 252. Control system 214 can generate control signals to control residue subsystem 138. Control system 214 can generate control signals to control machine cleaning subsystem 254. Control system 214 can generate control signals to control thresher 110. Control system 214 can generate control signals to control material handling subsystem 125. Control system 214 can generate control signals to control crop cleaning subsystem 118. Control system 214 can generate control signals to control communication system 206. Control system 214 can generate control signals to control operator interface mechanism 218. Control system 214 can generate control signals to control various other controllable subsystems 256.

[0112] Therefore, it can be seen that this system employs a priori information maps that map characteristics, such as soil property values ​​or information from previous or a priori operations, to different locations in the field. The system also uses one or more field sensors to sense field sensor data indicating agricultural characteristics (such as non-machine characteristics, machine characteristics, or any of several other agricultural characteristics that can be sensed by field sensors or are indicated by field sensors), and generates a model that models the relationship between agricultural characteristics or related characteristics sensed using field sensors and the characteristics mapped in the priori information maps. Thus, the system uses the model, field data, and priori information maps to generate functional prediction maps, and the generated functional prediction maps can be configured for use by the control system and / or presented to local operators, remote operators, or other users. For example, the control system can use this map to control one or more systems of an agricultural harvester.

[0113] Processors and servers have been mentioned in this discussion. In some examples, processors and servers include computer processors with associated memory and timing circuitry (not shown separately). Processors and servers are functional parts of the system or device to which they belong, and are activated by and facilitate the function of other components or items in these systems.

[0114] Furthermore, numerous user interface displays have been discussed. These displays can take various forms and can have various user-actuable operator interface mechanisms mounted on them. For example, user-actuable operator interface mechanisms can be text boxes, checkboxes, icons, links, drop-down menus, search boxes, etc. User-actuable operator interface mechanisms can also be actuated in various ways. For example, they can be actuated using operator interface mechanisms such as click devices (e.g., trackballs or mice, hardware buttons, switches, joysticks or keyboards, thumb switches or thumb pads, etc.), virtual keyboards, or other virtual actuators. Furthermore, if the screen displaying the user-actuable operator interface mechanism is a touch-sensitive screen, touch gestures can be used to actuate the mechanism. Moreover, voice recognition functionality can be used to actuate the mechanism using voice commands. Voice recognition can be implemented using voice detection devices (e.g., microphones) and software for recognizing the detected voice and executing commands based on the received voice.

[0115] Many 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 data storage devices may be local to the system accessing that data storage device; one or more data storage devices may all be located remotely from the system utilizing that data storage device; or one or more data storage devices may be local while the others are remote. This disclosure considers all of these configurations.

[0116] Furthermore, the accompanying diagram shows multiple boxes, with functionality belonging to each box. It should be noted that fewer boxes can be used to illustrate that functionality attributed to multiple different boxes is performed by fewer components. Moreover, more boxes can be used to show that the functionality can be distributed across more components. In different examples, some functionality can be added, and some functionality can be removed.

[0117] It should be noted that the foregoing discussion has described various different systems, components, logic, and interactions. It should be understood that any or all of such systems, components, logic, and interactions can be implemented by hardware items, such as processors, memory, or other processing components, some of which are described below, performing the functions associated with those systems, components, logic, or interactions. Furthermore, any or all of such systems, components, logic, and interactions can be implemented by software loaded into memory and subsequently executed by a processor, server, or other computing component, as described below. Any or all of such systems, components, logic, and interactions can also be implemented by different combinations of hardware, software, firmware, etc., some examples of which are described below. These are some examples of different structures that can be used to implement any or all of the systems, components, logic, and interactions described above. Other structures may also be used.

[0118] Figure 6 This is a block diagram of an agricultural harvester 600, which can be similar to... Figure 2 The agricultural harvester 100 is shown in the diagram. The agricultural harvester 600 communicates with components in a remote server architecture 500. In some examples, the remote server architecture 500 can provide computing, software, data access, and storage services that do not require the end user to know the physical location or configuration of the system delivering the services. In various examples, the remote server can deliver the services over a wide area network (such as the Internet) using appropriate protocols. For example, the remote server can deliver 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, along with associated data, can be stored on servers at remote locations. Computing resources in a remote server environment can be consolidated at a remote data center location, or they can be distributed across multiple remote data centers. Remote server infrastructure can deliver services through a shared data center, even if the service appears as a single access point for the user. Therefore, the components and functionalities described herein can be provided from remote servers at remote locations using a remote server architecture. Alternatively, the components and functionalities can be provided from a server, or they can be installed directly or otherwise on client devices.

[0119] exist Figure 6 In the examples shown, some items are similar to Figure 2 The items shown are numbered similarly. Figure 6 Specifically, a prediction model generator 210 or a prediction graph generator 212, or both, can be located at a server location 502, away from the agricultural harvester 600. Therefore, in Figure 6In the example shown, the agricultural harvester 600 accesses the system via a remote server location 502.

[0120] Figure 6 Another example of a remote server architecture is also described. Figure 6 It shows Figure 2 Some components can be located at a remote server location 502, while others can be located elsewhere. As an example, data storage device 202 can be located at a separate location from location 502 and accessed via a remote server at location 502. Regardless of their location, these components can be directly accessed by the agricultural harvester 600 via a network (such as a wide area network or local area network); these components can be hosted as a service at a remote site; or they can be provided as a service or accessed by a connection service residing at a remote location. Furthermore, data can be stored anywhere, and the stored data can be accessed or forwarded to an operator, user, or system. For example, a physical carrier wave can be used instead of an electromagnetic carrier wave, or a physical carrier wave can be used in addition to an electromagnetic carrier wave. In some examples, where wireless telecommunications service coverage is poor or nonexistent, another machine (such as a fuel truck or other mobile machine or vehicle) can have an automatic, semi-automatic, or manual information collection system. When the combine harvester 600 approaches a machine (such as a fuel truck) containing the information collection system before refueling, the information collection system collects information from the combine harvester 600 using any type of temporary dedicated wireless connection. The collected information can then be forwarded to another network when the machine containing the received information reaches a location with wireless telecommunications service coverage or other available wireless coverage. For example, when the fuel truck travels to a location to refuel other machines or at a main fuel storage location, the fuel truck can enter an area with wireless communication coverage. All these architectures are considered in this paper. Furthermore, information can be stored on the combine harvester 600 until it enters an area with wireless communication coverage. The combine harvester 600 itself can transmit the information to another network.

[0121] 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 airborne computer, electronic control unit, display unit, server, desktop computer, laptop computer, tablet computer or other mobile devices such as PDAs, cellular phones, smartphones, multimedia players, personal digital assistants, etc.

[0122] In some examples, the remote server architecture 500 may include network security measures. These measures, without limitation, include encryption of data on storage devices, encryption of data transmitted between network nodes, authentication of personnel or processes accessing data, and the use of a ledger to record metadata, data, data transfer, data access, and data transformation. In some examples, the ledger may be distributed and immutable (e.g., implemented as a blockchain).

[0123] Figure 7 This is a simplified block diagram illustrating a schematic example of a handheld computing device or mobile computing device 16 that can be used as a user's or customer's handheld device, in which the system (or a portion thereof) can be deployed. For example, a mobile device could be deployed in the operator's compartment of an agricultural harvester 100 for use in generating, processing, or displaying the diagrams discussed above. Figures 8 to 9 Examples are handheld or mobile devices.

[0124] Figure 7 A general block diagram of the components of client device 16, which can run... is provided. Figure 2 Some of the components shown in the diagram, the client device 16 can be connected to Figure 2 Some components, or both, are shown interacting. In device 16, a communication link 13 is provided that allows the handheld device to communicate with other computing devices, and in some examples, a channel is provided for automatically receiving information (e.g., by scanning). Examples of communication link 13 include those 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.

[0125] In other examples, the application can be received on a removable Secure Digital (SD) card connected to interface 15. Interface 15 and communication link 13 communicate along bus 19 with processor 17 (which may also be represented as a processor or server from other figures), which is also connected to memory 21 and input / output (I / O) components 23, as well as clock 25 and positioning system 27.

[0126] In one example, I / O component 23 is provided to facilitate input and output operations. Various examples of I / O component 23 in device 16 may include input components (such as buttons, touch sensors, optical sensors, microphones, touchscreens, proximity sensors, accelerometers, orientation sensors) and output components (such as display devices, speaker and / or printer ports). Other I / O components 23 may also be used.

[0127] Clock 25 schematically includes a real-time clock component that outputs the time and date. Schematically, clock 25 may also provide timing functionality for processor 17.

[0128] Positioning system 27 schematically includes components that output the current geographic location of the device 16. 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. Positioning system 27 may also include, for example, mapping or navigation software that generates desired maps, navigation routes, and other geographic functions.

[0129] Memory 21 stores operating system 29, network settings 31, applications 33, application configuration settings 35, data storage device 37, communication driver 39, and communication configuration settings 41. Memory 21 may include all types of tangible volatile and non-volatile computer-readable storage devices. Memory 21 may also include computer storage media (described below). Memory 21 stores computer-readable instructions that, when executed by processor 17, cause the processor to perform computer-implemented steps or functions. Processor 17 may also be activated by other components to facilitate the function of those components.

[0130] Figure 8 The illustration shows an example where device 16 is a tablet computer 600. Figure 8 In the diagram, computer 600 is shown with a user interface display screen 602. Screen 602 may be a touchscreen or a pen-enabled interface that receives input from a pen or stylus. Tablet computer 600 may also use 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 USB port). Computer 600 may also schematically receive voice input.

[0131] Figure 9 Similar to Figure 8 In addition to being a smartphone 71, the smartphone 71 has a touch-sensitive display 73 that shows icons, tiles, or other user input mechanisms 75. Users can use the mechanisms 75 to run applications, make calls, perform data transfer operations, etc. Generally, the smartphone 71 is built on a mobile operating system and offers more advanced computing power and connectivity than feature phones.

[0132] Note that other forms of device 16 are possible.

[0133] Figure 10 It is one of the deployable ones Figure 2 An example of a computing environment for the components. Reference Figure 10An 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 computer 810 may include (but are not limited to) a processing unit 820 (which may include a processor or server from the previous figures), system memory 830, and a system bus 821 that connects various system components, including the system memory, to the processing unit 820. System bus 821 may be any of several types of bus architectures, including a memory bus or memory controller, a peripheral bus, and a local bus using any of various bus architectures. Regarding... Figure 2 The described memory and program can be deployed in Figure 10 In the corresponding part.

[0134] Computer 810 typically includes a variety of computer-readable media. Computer-readable media can be any available medium accessible to computer 810, and includes volatile and non-volatile media, removable and non-removable media. By way of example and not limitation, computer-readable media can include computer storage media and communication media. Computer storage media are distinct from and do not include modulated data signals or carriers. Computer-readable media include hardware storage media, including volatile and non-volatile, removable and non-removable media implemented in any way or by any technique for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media include (but are not limited to) RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical disc storage devices, magnetic tape, magnetic tape, disk storage devices or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to computer 810. Communication media can implement computer-readable instructions, data structures, program modules, or other data in a transmission mechanism, and includes any information delivery medium. The term "modulated data signal" refers to a signal that has one or more characteristics set or changed in a manner that encodes information in the signal.

[0135] System memory 830 includes computer storage media in the form of volatile and / or non-volatile memory or both, such as read-only memory (ROM) 831 and random access memory (RAM) 832. The basic input / output system 833 (BIOS) (which contains basic routines such as those that help transfer information between components within computer 810 during startup) is typically stored in ROM 831. RAM 832 typically contains data and / or program modules, or both, that are readily accessible to and / or currently being operated by processing unit 820. By way of example and not limitation, Figure 10The operating system 834, application program 835, other program modules 836, and program data 837 are shown.

[0136] Computer 810 may also include other removable / non-removable volatile / non-volatile computer storage media. This is just one example. Figure 10 A hard disk drive 841 is shown that reads from or writes to a non-removable non-volatile magnetic medium, an optical disk drive 855, and a non-volatile optical disk 856. The hard disk drive 841 is typically connected to the system bus 821 via a non-removable memory interface (such as interface 840), and the optical disk drive 855 is typically connected to the system bus 821 via a removable memory interface (such as interface 850).

[0137] Alternatively or additionally, the functions described herein may be performed at least in part by one or more hardware logic components. For example, but not limited to, illustrative types of hardware logic components that may be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (e.g., ASSPs), systems-on-chips (SoCs), complex programmable logic devices (CPLDs), etc.

[0138] The above discussion and Figure 10 The driver and its associated computer storage medium shown provide the computer 810 with storage for computer-readable instructions, data structures, program modules, and other data. For example, in Figure 10 In this diagram, hard disk drive 841 is shown storing operating system 844, application programs 845, other program modules 846, and program data 847. Note that these components may be the same as or different from operating system 834, application programs 835, other program modules 836, and program data 837.

[0139] Users can input commands and information into computer 810 using input devices such as keyboard 862, microphone 863, and pointing devices 861 (such as mouse, trackball, or touchpad). Other input devices (not shown) may include joysticks, game controllers, satellite dishes, scanners, etc. These and other input devices are typically connected to processing unit 820 via user input interface 860, which is coupled to the system bus, but may also be connected via other interfaces and bus structures. Visual display 891 or other types of display devices are also connected to system bus 821 via an interface such as video interface 890. In addition to the monitor, the computer may also include other peripheral output devices, such as speakers 897 and printers 896, which can be connected via peripheral output interface 895.

[0140] Computer 810 operates in a networked environment using a logical connection (such as a controller local area network (CAN), local area network (LAN), or wide area network (WAN)) of one or more remote computers (such as remote computer 880).

[0141] When used in a LAN networking environment, computer 810 connects to LAN 871 via a network interface or adapter 870. When used in a WAN networking environment, computer 810 typically includes a modem 872 or other devices for establishing communication over a WAN 873 (such as the Internet). In a networking environment, program modules can be stored in remote memory storage devices. For example, Figure 10 This demonstrates that remote application 885 can reside on remote computer 880.

[0142] It should also be noted that the different examples described in this article can be combined in different ways. That is, parts of one or more examples can be combined with parts of one or more other examples. All of this is considered in this article.

[0143] Example 1 is an agricultural operating machine, comprising:

[0144] A communication system that receives a priori information map, the priori information map including soil property values ​​corresponding to different geographical locations in the field;

[0145] A geolocation sensor that detects the geolocation of the agricultural machinery;

[0146] A field sensor that detects values ​​of agricultural characteristics corresponding to the geographical location;

[0147] A prediction model generator generates a prediction model based on the values ​​of the soil properties at the geographical location in the prior information map and the values ​​of the agricultural characteristics corresponding to the geographical location sensed by the field sensors. The prediction model models the relationship between the soil properties and the agricultural characteristics.

[0148] A predictive map generator generates a functional predictive agricultural map of the field based on the values ​​of the soil properties in the prior information map and the values ​​of the agricultural characteristics detected by the field sensors. The functional predictive agricultural map maps the predicted values ​​of the agricultural characteristics to the different geographical locations in the field.

[0149] Example 2 is any or all of the agricultural operating machines of the foregoing examples, wherein the prediction map generator configures the functional predictive agricultural map for use by a control system, which generates control signals based on the functional predictive agricultural map to control controllable subsystems on the agricultural operating machine.

[0150] Example 3 is any or all of the agricultural machinery described in the foregoing examples, wherein the field sensors include:

[0151] An image sensor configured to detect images indicative of the agricultural characteristics.

[0152] Example 4 is any or all of the agricultural machinery described in the foregoing examples, wherein the image detector is oriented to detect an image of at least a portion of the field, and the image detector further comprises:

[0153] An image processing system configured to process the image to identify values ​​of the agricultural characteristic in the image, the values ​​of the agricultural characteristic indicating the agricultural characteristic.

[0154] Example 5 is any or all of the agricultural operating machines of the foregoing examples, wherein the field sensors on the agricultural operating machine are configured to detect values ​​of non-machine characteristics corresponding to the geographic location, the values ​​of the non-machine characteristics being used as values ​​of the agricultural characteristics.

[0155] Example 6 is any or all of the agricultural machinery described in the foregoing examples, wherein the prior information map includes values ​​of soil types corresponding to the different geographical locations in the field as values ​​of the soil properties, and wherein the predictive model generator is configured to determine the relationship between the soil type and the non-machine characteristic based on values ​​of the non-machine characteristic corresponding to the geographical location detected by the field sensors and the values ​​of the soil type at the geographical location in the prior information map, and the predictive characteristic model is configured to receive the value of the soil type as model input and generate predicted values ​​of the non-machine characteristic as model output based on the determined relationship.

[0156] Example 7 is any or all of the agricultural machinery described in the foregoing examples, wherein the prior information map includes soil moisture values ​​corresponding to the different geographical locations in the field as values ​​of the soil properties, and wherein the predictive model generator is configured to determine the relationship between the soil moisture and the non-machine properties based on the values ​​of the non-machine properties detected by the field sensors corresponding to the geographical locations and the soil moisture values ​​at the geographical locations in the prior information map, and the predictive property model is configured to receive the soil moisture values ​​as model inputs and generate predicted values ​​of the non-machine properties as model outputs based on the determined relationship.

[0157] Example 8 is any or all of the agricultural operating machines of the foregoing examples, wherein the field sensors on the agricultural operating machine are configured to detect machine characteristics corresponding to the geographic location, the values ​​of the machine characteristics serving as the values ​​of the agricultural characteristics.

[0158] Example 9 is any or all of the agricultural machinery described in the foregoing examples, wherein the prior information map includes values ​​of soil types corresponding to the different geographical locations in the field as values ​​of the soil properties, and wherein the predictive model generator is configured to determine the relationship between the soil type and the machine characteristics based on values ​​of the machine characteristics corresponding to the geographical locations detected by the field sensors and values ​​of the soil type at the geographical locations in the prior information map, and the predictive characteristic model is configured to receive the soil type values ​​as model inputs and generate predicted values ​​of the machine characteristics as model outputs based on the determined relationship.

[0159] Example 10 is any or all of the agricultural machinery of the foregoing examples, wherein the prior information map includes soil moisture values ​​corresponding to the different geographical locations in the field as values ​​of the soil properties, and wherein the predictive model generator is configured to determine the relationship between the soil moisture and the machine characteristics based on the values ​​of the machine characteristics corresponding to the geographical locations detected by the field sensors and the soil moisture values ​​at the geographical locations in the prior information map, and the predictive characteristic model is configured to receive the soil moisture values ​​as model inputs and generate predicted values ​​of the machine characteristics as model outputs based on the determined relationship.

[0160] Example 11 is a computer-implemented method for generating functional predictive agricultural maps, comprising:

[0161] Receive prior information maps at agricultural machinery, the prior information maps including soil property values ​​corresponding to different geographical locations in the field;

[0162] Detect the geographical location of the agricultural machinery;

[0163] Values ​​of agricultural characteristics corresponding to the geographical location are detected using field sensors;

[0164] Generate a predictive model that models the relationship between the soil properties and the agricultural characteristics; and

[0165] The control prediction map generator generates a functional predictive agricultural map of the field based on the values ​​of the soil properties in the prior information map and the values ​​of the agricultural characteristics detected by the field sensors. The functional predictive agricultural map maps the predicted values ​​of the agricultural characteristics to the different geographical locations in the field.

[0166] Example 12 is a computer-implemented method of any or all of the foregoing examples, and further includes:

[0167] The control system is configured with the functional predictive agriculture map, and the control system generates control signals based on the functional predictive agriculture map to control the controllable subsystems on the agricultural machinery.

[0168] Example 13 is a computer-implemented method of any or all of the foregoing examples, wherein detecting the value of the agricultural characteristic with field sensors includes detecting the value of the non-machinery characteristic corresponding to the geographic location.

[0169] Example 14 is a computer-implemented method of any or all of the foregoing examples, wherein generating a predictive model includes:

[0170] The relationship between the soil properties and the non-machinery properties is determined based on the values ​​of the non-machinery characteristics detected by the field sensors corresponding to the geographical location and the values ​​of the soil properties at that geographical location in the prior information map; and

[0171] The predictive model generator is controlled to generate the predictive model, which receives the values ​​of the soil properties as model inputs and generates predicted values ​​of the non-machine characteristics as model outputs based on the determined relationships.

[0172] Example 15 is a computer-implemented method of any or all of the foregoing examples, wherein detecting the value of the agricultural characteristic with field sensors includes detecting the value of the machine characteristic corresponding to the geographical location.

[0173] Example 16 is a computer-implemented method of any or all of the foregoing examples, wherein generating a predictive model includes:

[0174] The relationship between the soil properties and the machine characteristics is determined based on the values ​​of the machine characteristics detected by the field sensors corresponding to the geographical location and the values ​​of the soil properties at that geographical location in the prior information map; and

[0175] The predictive model generator is controlled to generate the predictive model, which receives soil property values ​​as model inputs and generates predicted values ​​of the machine characteristics as model outputs based on the determined relationships.

[0176] Example 17 is a computer-implemented method of any or all of the foregoing examples, further comprising:

[0177] The operator interface mechanism is controlled to present the functional predictive agricultural map.

[0178] Example 18 is an agricultural operating machine, comprising:

[0179] A communication system that receives a soil property map, the soil property map indicating soil property values ​​corresponding to different geographical locations in the field;

[0180] A geolocation sensor that detects the geolocation of the agricultural machinery;

[0181] A field sensor that detects agricultural characteristic values ​​corresponding to the geographical location;

[0182] A prediction model generator generates a prediction model based on soil property values ​​at the geographical location in the soil property map and agricultural characteristic values ​​corresponding to the geographical location detected by the field sensors. The prediction model determines the relationship between the soil property values ​​and the agricultural characteristic values.

[0183] A prediction map generator generates a functional predictive agricultural map of the field based on the soil property values ​​in the prior information map and the prediction model, the functional predictive agricultural map mapping the predicted agricultural characteristic values ​​to the different geographical locations in the field.

[0184] Example 19 is an agricultural operation machine of any or all of the foregoing examples, wherein the prior information map includes soil type values ​​corresponding to the different geographical locations in the field as soil property values, and wherein the prediction model generator is configured to determine the relationship between the soil type values ​​and the agricultural property values ​​based on the agricultural property values ​​corresponding to the geographical locations detected by the field sensors and the soil type values ​​at the geographical locations in the prior information map, and the prediction model is configured to receive soil type values ​​as model inputs and generate predicted agricultural property values ​​as model outputs based on the determined relationship.

[0185] Example 20 is an agricultural machine of any or all of the foregoing examples, wherein the prior information map includes soil moisture values ​​corresponding to the different geographical locations in the field as soil property values, and wherein the prediction model generator is configured to determine the relationship between the soil moisture values ​​and the agricultural property values ​​based on the agricultural property values ​​corresponding to the geographical locations detected by the field sensors and the soil moisture values ​​at the geographical locations in the prior information map, and the prediction model is configured to receive soil moisture values ​​as model inputs and generate predicted agricultural property values ​​as model outputs based on the determined relationship.

[0186] Although the subject matter has been described in language specific to structural features or methodological actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are disclosed as examples of the claims.

Claims

1. An agricultural system comprising: A communication system (206) receives a priori information map (258), the priori information map including values ​​of soil properties corresponding to different geographical locations in the field; A geolocation sensor (204) detects the geolocation of the agricultural machinery (100); A field sensor (208) detects values ​​of agricultural characteristics corresponding to the geographical location; A prediction model generator (210) generates a prediction model based on the values ​​of the soil properties at the geographic location in the prior information map (258) and the values ​​of the agricultural characteristics corresponding to the geographic location sensed by the field sensor (208), the prediction model modeling the relationship between the soil properties and the agricultural characteristics; and A prediction map generator (212) generates a functional predictive agricultural map of the field based on the values ​​of the soil properties in the prior information map (258) and the values ​​of the agricultural characteristics detected by the field sensor (208). The functional predictive agricultural map predicts the values ​​of the agricultural characteristics and maps the predicted values ​​of the agricultural characteristics 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 the control system, which generates control signals based on the functional predictive agricultural map to control controllable subsystems on the agricultural machinery.

3. The agricultural system according to claim 1, wherein, The field sensors include: An image sensor configured to detect images indicative of the agricultural characteristics.

4. The agricultural system according to claim 1, wherein, The field sensors include: An agricultural characteristic sensor, comprising an optical sensor configured to generate an image indicating the agricultural characteristic; and A processing system configured to receive the image and process the image to generate processed data identifying the agricultural characteristics.

5. The agricultural system according to claim 1, wherein, The field sensor is configured to detect values ​​of non-machinery characteristics corresponding to the geographic location, and the values ​​of the non-machinery characteristics are used as values ​​of the agricultural characteristics.

6. The agricultural system according to claim 5, wherein, The prior information map includes soil type values ​​corresponding to the different geographical locations in the field, which are values ​​of the soil properties. The predictive model generator is configured to determine the relationship between the soil type and the non-machinery characteristic based on the value of the non-machinery characteristic corresponding to the geographic location detected by the field sensors and the value of the soil type at the geographic location in the prior information map. The predictive model is configured to receive the value of the soil type as model input and generate a predicted value of the non-machinery characteristic as model output based on the determined relationship.

7. The agricultural system according to claim 5, wherein, The prior information map includes soil moisture values ​​corresponding to the different geographical locations within the field, which are values ​​of the soil properties. The predictive model generator is configured to determine the relationship between soil moisture and the non-machinery characteristic based on the value of the non-machinery characteristic corresponding to the geographic location detected by the field sensors and the value of soil moisture at the geographic location in the prior information map. The predictive model is configured to receive the value of soil moisture as model input and generate a predicted value of the non-machinery characteristic as model output based on the determined relationship.

8. The agricultural system according to claim 3, wherein, The field sensor is configured to detect values ​​of machine characteristics corresponding to the geographical location, and the values ​​of the machine characteristics are used as values ​​of the agricultural characteristics.

9. A computer-implemented method for generating functional predictive agricultural maps, comprising: Receive a priori information map (258), the priori information map including soil property values ​​corresponding to different geographical locations in the field; Detect the geographical location of agricultural machinery (100); The values ​​of agricultural characteristics corresponding to the geographical location are detected using field sensors (208); Generate a predictive model that models the relationship between the soil properties and the agricultural characteristics; and The control prediction map generator (212) generates the functional predictive agricultural map of the field based on the values ​​of the soil properties in the prior information map (258) and the values ​​of the agricultural characteristics detected by the field sensor (208), the functional predictive agricultural map predicting the values ​​of the agricultural characteristics and mapping the predicted values ​​of the agricultural characteristics to the different geographical locations in the field.

10. An agricultural system comprising: Communication system (206), the communication system receiving soil property map, the soil property map indicating soil property values ​​corresponding to different geographical locations in the field; A geolocation sensor (204) detects the geolocation of agricultural machinery; A field sensor (208) detects values ​​of agricultural characteristics corresponding to the geographical location; A prediction model generator (210) generates a prediction model based on soil property values ​​at the geographical location in the soil property map and agricultural characteristic values ​​corresponding to the geographical location detected by the field sensor (208), the prediction model determining the relationship between the soil property values ​​and the agricultural characteristic values; and A prediction map generator (212) generates a functional predictive agricultural map of the field based on the soil property values ​​in the soil property map and the prediction model. The functional predictive agricultural map predicts the values ​​of the agricultural characteristics and maps the predicted values ​​of the agricultural characteristics to the different geographical locations in the field.

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