BR102022024438A2 - METHOD FOR CONTROLING AN AGRICULTURAL HARVESTING MACHINE, AGRICULTURAL HARVESTING MACHINE, AND CONTROL SYSTEM - Google Patents

Abstract

METHOD FOR CONTROLING AN AGRICULTURAL HARVESTING MACHINE, AGRICULTURAL HARVESTING MACHINE, AND, CONTROL SYSTEM A method for controlling an agricultural harvesting machine having a harvesting device configured to engage and cut crop in a field. The method includes receiving a field topography indication in a first area of the field prior to the first area being harvested, controlling a position of the harvesting device to harvest the crop in the first area based on the field topography indication in the first area, receiving IN SITU data from the first area of the field after the first area is harvested by the harvesting device, receive an indication of field topography in a second area of the field prior to the second area being harvested, and control the position of the harvesting device to harvest the crop in the second area based on IN SITU data from the field topography indication in the second area.

Description

DESCRIPTION FIELD

[001] The present description generally refers to agricultural harvesting machines. More specifically, but not by way of limitation, the present description relates to cutting height control for an agricultural harvesting machine.

FUNDAMENTALS

[002] There are many different types of agricultural machines. Some machines include harvesters such as combination harvesters, sugar cane harvesters, forage harvesters, etc. Controlling these types of harvesters is often very complicated. An operator can provide an extensive variety of different types of inputs to control a variety of different types of subsystems in the combine. Also, some or all of the controls of various subsystems may be automatic or semiautomatic. An example control is the cutting height which defines the height at which the crop is cut in relation to the ground.

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

SUMMARY

[004] A method for controlling an agricultural harvesting machine having a harvesting device configured to engage and cut crops in a field. The method includes receiving a field topography indication in a first area of the field prior to the first area being harvested, controlling a position of the harvesting device to harvest the crop in the first area based on the field topography indication in the first area, receiving IN SITU data from the first area of the field after the first area is harvested by the harvesting device, receive an indication of field topography in a second area of the field prior to the second area being harvested, and control the position of the harvesting device to harvest the crop in the second area based on IN SITU data from the field topography indication in the second area.

[005] Example 1 is a method for controlling an agricultural harvesting machine having a harvesting device configured to engage and cut crops in a field, the method comprising: receiving an indication of field topography in a first area of the field prior to first area being harvested; controlling a position of the harvesting device to harvest the crop in the first area based on the indication of field topography in the first area; receiving IN SITU data from the first area of the field after the first area is harvested by the harvesting device; receiving an indication of field topography in a second area of the field preceding the second area being harvested; and controlling the position of the harvesting device to harvest the crop in the second area based on IN SITU data from the field topography indication in the second area.

[006] Example 2 is the method according to any or all of the preceding examples, wherein the harvesting device comprises a front end assembly having a cutting device, and further comprising: determining a height of a culture bed in the first area based on the first indication of field topography; and adjusting a cutting height of the cutting device based on the height of the culture bed.

[007] Example 3 is the method according to any or all of the previous examples, wherein receiving the indication of field topography in the first area comprises: receiving, from a first sensor on the agricultural harvesting machine, a first signal of sensor indicating a distance from the first sensor to the culture bed.

[008] Example 4 is the method according to any or all of the previous examples, wherein receiving data IN SITU comprises receiving a second sensor signal from a second sensor on the agricultural harvesting machine.

[009] Example 5 is the method according to any or all of the previous examples, wherein each of the first and second sensors comprises at least one of: a radio detection and ranging (RADAR) sensor; an ultrasonic sensor; or an imaging sensor.

[0010] Example 6 is the method according to any or all of the previous examples, wherein the second sensor signal is indicative of crop stubble height in the first area, and the method further comprises: determining that the height of crop stubble is above a threshold; and adjusting a height of the cutting device based on the determination.

[0011] Example 7 is the method according to any or all of the previous examples, wherein the second sensor signal is indicative of a distance from the second sensor to the culture bed.

[0012] Example 8 is the method according to any or all of the previous examples, and further comprising: determining that the front end assembly has contacted the ground surface in the first area based on the first and second sensor signals ; and control the position based on the determination.

[0013] Example 9 is the method according to any or all of the previous examples, and further comprising: adjusting a height of the cutting device based on the second sensor signal.

[0014] Example 10 is the method according to any or all of the previous examples, and further comprising: calibrating the first sensor based on the second sensor signal.

[0015] Example 11 is the method according to any or all of the preceding examples, wherein the first field area comprises a first crop row of a plurality of crop rows in the field, the second field area comprises a second culture row, of the plurality of culture rows, which is adjacent to the first culture row, and the method further comprises: generating, based on the IN SITU data, a predictive culture bed height in the second culture row; and controlling the position of the harvesting device to harvest the crop in the second crop row based on the predicted crop bed height and the indication of field topography in the second crop row.

[0016] Example 12 is the method according to any or all of the previous examples, detecting the furrow depth between the first and second crop rows based on field topography; and based on the detected furrow depth, control the height of a harvesting device and crop collection of the agricultural harvesting machine.

[0017] Example 13 is the method according to any or all of the preceding examples, and further comprising: detecting a first depth of a groove between a first crop row and a second crop row preceding the first crop row being harvested by agricultural harvesting machine; detect a second furrow depth after the first row of crop is harvested by the agricultural harvesting machine; generate a compaction factor based on the first and second depths; and control the agricultural harvesting machine based on the compaction factor.

[0018] Example 14 is an agricultural harvesting machine comprising: a set of ground-engaging traction elements; a propulsion subsystem configured to drive the one or more of the ground engagement traction elements over a field; a harvesting device configured to engage and cut crops in the field; and a control system configured to: receive an indication of field topography in a first area prior to the first area being harvested by the harvesting device; controlling a position of the harvesting device to harvest the crop in the first area based on the indication of field topography in the first area; receiving IN SITU data from the first area of the field after the first area is harvested by the harvesting device; receiving an indication of field topography in a second area prior to the second area being harvested by the harvesting device; and controlling the position of the harvesting device to harvest the crop in the second area based on IN SITU data from the field topography indication in the second area.

[0019] Example 15 is the agricultural harvesting machine according to any or all of the preceding examples, wherein the harvesting device comprises a front end assembly having a cutting device, and the control system is configured to: determining a height of a crop bed in the second area based on IN SITU data from the field topography statement in the second area; and adjusting a cutting height of the cutting device based on the height of the culture bed.

[0020] Example 16 is the agricultural harvesting machine according to any or all of the previous examples, and further comprising: a first sensor configured to generate the field topography indication in the first area, the indication representing a height of one culture bed in the first area preceding the culture bed being harvested by the harvesting device; and a second sensor configured to generate IN SITU data representing the culture bed after the culture bed is harvested by the harvesting device.

[0021] Example 17 is the agricultural harvesting machine according to any or all of the previous examples, wherein the IN SITU data is indicative of crop stubble height in the first area, and the control system is configured to: determine that the crop stubble height is above a threshold; adjusting a height of the cutting device based on the determination; calibrate the first sensor; and receiving the field topography indication in the second area from the first calibrated sensor.

[0022] Example 18 is the agricultural harvesting machine according to any or all of the preceding examples, wherein the field topography indication in the first area is indicative of a location of a crop bed in the first area with respect to agricultural harvesting machine, and the control system is configured to: determine that the harvesting device has contacted the ground surface in the first area based on IN SITU data.

[0023] Example 19 is a control system for an agricultural harvesting machine having a harvesting device, the control system comprising: at least one processor; and memory storing instructions executable by the at least one processor, wherein the instructions, when executed, cause the control system to: receive a first indication of field topography in a first area to be harvested in a path of the harvesting machine agricultural; control a position of the harvesting device to harvest the crop in the first area based on the first indication of field topography; receive IN SITU data from the first area of the field after the first area is harvested by the harvesting device; receive a second indication of field topography in a second area of the field to be harvested; and control the position of the harvesting device to harvest the crop in the second area based on IN SITU data from the second field topography indication in the second area

[0024] Example 20 is the control system according to any or all of the preceding examples, wherein the harvesting device comprises a front end assembly having a cutting device, and the control system is configured to: determine a height of a crop bed in the second area based on IN SITU data from the second field topography statement in the second area; and adjusting a cutting height of the cutting device based on the height of the culture bed.

[0025] This Summary is provided to present a selection of concepts in a simplified form, which are described in more detail below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to implementations that address any or all of the disadvantages noted in the pleas.

BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a block diagram showing an example of an agricultural harvesting machine architecture that includes an agricultural harvesting machine.

[0027] Figure 2 illustrates an example of an agricultural harvesting machine.

[0028] Figures 3-1 and 3-2 illustrate the operation of an agricultural harvesting machine in a field, in an example.

[0029] Figure 4 is a block diagram illustrating an example of a cutting height detection and control system.

[0030] Figures 5-1 and 5-2 (collectively referred to as Figure 5) are a flowchart illustrating an example of a cutting height detection and control for an agricultural harvesting machine.

[0031] Figure 6 is a flowchart illustrating an example of determining the height of a culture bed.

[0032] Figure 7 is a flowchart illustrating an example of sensor calibration based on post-harvest IN SITU data.

[0033] Figure 8 is a flowchart illustrating an example of generating a culture bed height prediction.

[0034] Figure 9 is a flowchart illustrating an example of groove depth and detection compaction factor.

[0035] Figure 10 is a flowchart illustrating an example of field contact detection by a harvesting device.

[0036] Figure 11 is a flowchart illustrating an example of detection mode selection.

[0037] Figure 12 is a block diagram showing an example of the architecture illustrated in Figure 1, implemented in a remote server architecture.

[0038] Figures 13 to 15 show examples of mobile devices that can be used in the architectures shown in the previous figures.

[0039] Figure 16 is a block diagram showing an example of a computing environment that can be used in the architectures shown in the previous figures.

DETAILED DESCRIPTION

[0040] The present description generally refers to agricultural harvesting machines. More specifically, but not by way of limitation, the present disclosure relates to cutting height control for an agricultural harvesting machine (or “harvester”). Depending on the crop being harvested, cutting height control may affect the efficiency of the combine (e.g. in terms of production). For example, the height at which the crop is cut can change the level of crop constituents, such as the amount of protein, sugar, starch, oil, nutrients, water, among various other constituents, in the harvested material. By way of illustration, but not by way of limitation, the sugar content in sugarcane plants is typically concentrated in the lower section of the plant. Thus, if sugarcane plants are cut relatively high above the ground, a large portion of the sugar content is left unharvested. In some production scenarios, for every 1 inch (2.54 cm) of crop residue left in the field or otherwise unharvested, approximately half a ton per acre of crop production may be lost. That being said, low cutting rate can result in excessive wear or other damage to the combine, and can create unwanted changes to the topography of the field (or other job site). Also, the introduction of dirt, sand, soil, or other impurities can have detrimental effects on a mill or other equipment.

[0041] In some cases, a forward-facing sensor is used to sense the ground surface of the crop bed in front of the combine so that the height of the cutter can be adjusted accordingly. However, unharvested crop standing in the target area in front of the combine can reduce the accuracy of ground surface predictions. For example, in the case of sugarcane, plant density and mature green cane has a high leaf litter content that inhibits the ability to find a high leaf litter content that inhibits the ability to find a ground surface of the culture bed with reasonably high accuracy and/or consistency. Inaccurate detection of the ground surface can result in setting the cutting height too high (e.g. resulting in high unharvested crop stubble) or too low (e.g. resulting in contact with the ground).

[0042] Alternatively, or additionally, a crop planting map, field topography map, or other field data map can be generated prior to the harvesting operation. For example, during a previous harvest operation, data may be obtained from the combine to generate a planting map indicating the location of plants in the field along with field topography corresponding to the plant locations. However, over time, the location of plants may migrate or move. For example, some sugarcane sprout crops, such as sugar cane and tropical grasses, sprout new shoots from the base of the plant after harvest. This regrowth process may result in the plant being situated in a slightly different location in the field during subsequent harvest, and that the different location may be a different height from the ground. Furthermore, the topography of the field may change over time due to weather conditions, machine operations in the field, etc.

[0043] The present discussion continues with respect to a control system for an agricultural harvesting machine, such as a sugar cane harvester, which is configured to detect the harvester's operating performance and adjust the cutter height based on the pre-harvest field data and post-harvest data, obtained from IN SITU sensors on the harvesting machine. It is noted that examples are discussed below in the context of a sugar cane harvester for illustration, but not by way of limitation. It will be understood that the aspects described here can be used with other types of harvesting machines.

[0044] Figure 1 is a block diagram showing an example of an agricultural harvesting machine architecture 100 that includes an agricultural harvesting machine 102. The agricultural harvesting machine 102 may include autonomous or semi-autonomous machines, such as robotic vehicles or self-driving. Thus, exemplary machine 102 may operate in a fully autonomous mode and/or a semi-autonomous mode, in which an operator is on board or nearby to perform one or more functions. These functions may include, for example, without limitation, one or more of guidance, protection, diagnostics, task monitoring, task control, or data recording.

[0045] Machine 102 includes a control system 106 configured to control a set of controllable subsystems 108 that perform operations in a field. For example, an operator 110 may interact with, and control, the harvesting machine 102 through the operator interface mechanism(s) 112. The operator interface mechanism(s) 112 may include such equipment as a steering wheel, pedals, levers, control levers, keys, displays, articulated systems, etc. Additionally, the mechanism(s) 112 may include a display device that displays user-actuable elements, such as icons, connections, keys, etc. Furthermore, the device has a predictive touch-sensitive display, those user-actuable items can be actuated by touch gestures. Similarly, where engine(s) 112 include speech processing mechanisms, then operator 110 may provide inputs and receive outputs through a microphone and speaker, respectively. The operator interface mechanism(s) 112 may include any of a wide variety of other audio, visual, or haptic mechanisms.

[0046] Harvesting machine 102 includes a communication system 114 configured to communicate with other systems or machines in architecture 100. For example, communication system 114 may communicate with other machines 116 (such as other machines operating in the same field than the harvesting machine 102), remote computing system(s) 118, and/or previous data collection system(s) 120, either directly or through a network 122. The network 122 can be any from an extensive variety of different types of networks. For example, network 122 may be a wide area network, a local area network, a near field communication network, a cellular communication network, or any of a wide variety of other networks, or combinations thereof.

[0047] The communication system 114 may include wired and/or wireless communication components, which may be substantially any communication system that can be used by the systems and machine components 102 to communicate information to other items, such as between the control system 106, controllable subsystems 108, and sensors 124. In one example, the communication system 114 communicates via a controller area network (CAN) bus (or other network, such as an Ethernet network, etc.) to communicate information between those items. This information may include the various sensor signals and output signals generated by the sensor variables and/or sensed variables.

[0048] A remote user 123 is illustrated interacting with the remote computing system 118, such as to receive communications from, or send communications to, the harvesting machine 102 via the communications system 114. For example, but not by way of limitation, the user remote 123 may receive communications, such as notifications, requests for assistance, etc., from the harvesting machine 102 on a mobile device.

[0049] System(s) 120 is configured to collect past data that can be used by harvesting machine 102 in carrying out a harvesting operation in a field. Past data can be generated from a wide variety of different types or sources, such as aerial or satellite imagery, thermal imagery, etc. The foregoing data can be used to generate a model, such as a predictive map, that can be used to control harvesting machine 102. Examples of foregoing data include, but are not limited to (field topography maps and crop plantation, to name a few.

[0050] Figure 1 also shows that the harvesting machine 102 includes the IN SITU data collection system 126, one or more processors 128, a data store 130, and may also include other items 132. The sensors 124 may include any of a wide variety of sensors, depending on the type of harvesting machine 102. For example, sensors 124 may include material sensors 134, position/route sensors 136, speed sensor 138, field sensors 140, orientation sensors and/or inertial 141, and may also include other sensors 142.

[0051] Material sensors 134 are configured to sense material being moved, processed, or otherwise worked by harvesting machine 102. In the case of an agricultural harvester, material sensors 134 include production sensors.

[0052] Position/route sensors 136 are configured to identify a position of the harvesting machine 102 and a corresponding route (e.g., heading) of the harvesting machine 102 when the machine 102 traverses the field. A geographic position sensor, for example, senses or detects the geographic position or location of agricultural harvester 102 and may include, but is not limited to, a global navigation satellite system (GNSS) receiver, which receives signals from a GNSS satellite transmitter. The sensor 136 may also include a real-time kinematics (RTK) component that is configured to improve the accuracy of position data derived from the GNSS signal. Sensor 136 may include a passive recognition system, a cellular triangulation system, or any of a variety of other geographic position sensors. The speed sensors 138 are configured to provide a signal indicative of a speed of the harvesting machine 102.

[0053] Field sensors 140 are configured to obtain field data representing field conditions or states, which can be processed, for example, by IN SITU data collection system 126. Illustratively, field sensors 140 include IN SITU sensors , such as imaging sensors 144, ground surface sensors 146, stubble height sensors 148, and may include other sensors 150. IN SITU sensors may include on-board sensors that are mounted on board the harvesting machine 102 and/or remote IN SITU sensors that capture IN SITU information. IN SITU (or field) data can thus be obtained in real time from sensors 124 on machine 102 and/or from sensors on a support machine working in parallel with machine 102.

[0054] Sensors 141 are configured to detect an orientation and/or inertia of machine 102. Sensors 141 may include accelerometers, gyroscopes, roll sensors, pitch sensors, yaw sensors, to name a few.

[0055] The control system 106 includes the adjustment control component 152, the route control component 154, a cutting height detection and control system 156, and a display generator component 158. The control system 106 control 106 may include other items 159.

[0056] Controllable subsystems 108 may include propulsion subsystem 160, steering subsystem 162, harvesting subsystem 164, one or more different actuators 166 that may be used to change machine settings, machine configuration, etc., and may include an extensive variety of other systems 168.

[0057] Propulsion subsystem 160 includes a motor (or other power source) that drives a set of ground-engaging traction elements, such as wheels or tracks. The steering subsystem 162 is configured to control a machine steering 102 by directing one or more of the ground engagement drive elements.

[0058] Harvesting subsystem 164 includes a front end subsystem 170 having a harvesting device, such as a head platform, configured to engage and cut crop from the field. In the case of an example sugar cane harvester, the harvesting device includes bed cutters (or other cutting devices) and the pullout roller. The subsystem 164 also includes a material handling subsystem 172 configured to transport and/or process the crop cut by the front end subsystem 170 to a material repository 174. In one example of a combine harvester, the material handling subsystem 172 may include a threshing system. Subsystem 164 may also include other items 176.

[0059] Imaging sensors 144 are configured to obtain images of the field, such as areas of the field in front of subsystem 170 in a direction of travel (i.e., uncropped areas) and/or areas behind a imaging platform. cut (i.e. areas already harvested).

[0060] Ground surface sensors 146 are configured to detect the ground surface of the field in one or more areas, which may also include unharvested areas in front of subsystem 170 and/or already harvested areas behind subsystem 170. For example , sensors 146 are configured to generate indications of the height of the culture bed. The crop bed refers to the area in which the roots of the plant area are located. Therefore, the height of the culture bed is the height from the ground close to the base of the stem emerging from the ground surface. In the case of sugarcane plants in rows, the height of the crop bed typically indicates the height from the ground at, or near, the center of the row. Sensors 146 may also generate indications of furrow depth. A furrow, in the present case, refers to the area between the crop rows.

[0061] An example of the ground surface sensor 146 includes a RADAR (radio detection and ranging) detection system that uses radio waves to determine a distance from the sensor location to the ground surface (e.g., the bed of crop to be harvested). The given distance and position of the sensor mounting location can be used to determine a height of the culture bed with respect to the sensor. Then, based on the vertical distance between the sensor mounting location and the cutter in the front end subsystem 170, the height of the cutting device can be controlled to a desired position with respect to the crop bed, i.e., to cut the culture at a desired height (e.g., one inch, two inches, etc.) from the top of the culture bed. In one example, a cutting height of the cutting device is adjusted based on a predetermined delta or offset for the determined height of the culture bed.

[0062] The settings control component 152 may control one or more subsystems 108 in order to change machine settings based on predicted and/or observed field conditions or characteristics. By way of example, the adjustment control component 152 can actuate actuators 166 that change a positioning of the cutter based on predicted heights of the culture bed.

[0063] In one example, the transverse control of the machine 102 over the field may be automatic or semi-automatic, for example, using an automatic guidance system. For example, route control component 154 is configured to guide machine 102 along a path through the field using the geographic position generated by sensors 136. Subsystem 162 is configured to perform field operations, while machine 102 crosses the field.

[0064] The display generator component 158 illustratively generates control interface displays for the operator 110, or another user, such as the remote user 123. The display may be an interactive display with user input mechanisms for interaction by the operator 110.

[0065] Data storage 130 is configured to store data for use by machine 102. For example, in agricultural applications, the data may include field location data that identifies a field location to be operated by a machine 102, data shape and topography data that define a field shape and topography, crop location data that is indicative of a crop location in the field (for example, the location of crop rows), or any other data.

[0066] The prior data collection system 120 illustratively collects field data, such as prior data corresponding to a target field to be operated by machine 102. Briefly, by prior, it is understood that the data is formed or obtained in advance, prior to operation by machine 102. Data generated by system 120 may be sent to machine 102 directly and/or may be stored in a data store 178 as previous data 180. Control system 106 may use data 180 to control the operation of one or more subsystems 108.

[0067] As noted above, harvesting machine 102 can assume a wide variety of different Forms. Figure 2 illustrates an example of an agricultural harvesting machine. More specifically, Figure 2 is a simplified side view of a sugar cane harvester 200.

[0068] As shown in Figure 2, the harvester 200 includes a cab 202 for an operator (e.g., operator 110) and a frame 204 that supports various cutting, routing and processing devices. The frame 204 is supported by a transport frame, such as a track frame supporting the track assemblies 206. In another example, the combine 200 may include wheels supported by axle assemblies.

[0069] A motor 208 energizes a main hydraulic pump (not shown) and various driven components of the combine 200 may be energized by hydraulic motors (not shown) to receive hydraulic power to the main hydraulic pump.

[0070] A top piece of cane 209 extends in front of the frame 204 and is configured to remove the leafy tops of sugar cane plants (not shown). A set of crop dividers (e.g., the left-hand divider 210 shown in Figure 2) is configured to guide the remainder of the sugar cane toward the internal mechanisms of the harvester 200 for processing. As the combine 200 moves through the field, plants passing between crop dividers 210 are deflected downward by a felling roller 212 before being cut in close proximity to the base of the plants by one or more base cutters (or other cutting devices). cut) 214.

[0071] Rotating discs, guides, paddles (not shown in figure 2) or other conveying devices on the base cutter(s) 214 are configured to direct the cut ends of plants upward and backward within the combine 200 in direction to a feed train 216, which may include successive pairs of upper and lower feed rollers 218 and 220. A set of intake rollers 222 and 224 are configured to receive cut sugar cane from the base cutters. 214 at the front end of the feed train 216. Feed rollers 218 and 220 are rotated to convey the incoming sugar cane toward chopper drums 226 and 228 for chopping into relatively uniform beams. The sugar cane may be cleaned by a primary extractor 230 and transported upward on a loading elevator 232 for unloading into a trailer truck or other receptacle (not shown in figure 2).

[0072] Figures 3-1 and 3-2 illustrate an example of a sugar cane harvester 250 (such as, but not limited to, the combine 200 shown above) operating in a field 252. Figure 3- 1 is a top view of the combine 250 and Figure 3-2 is a side view of the field portion 252.

[0073] As the combine 250 moves in a direction of travel 254, a harvesting device 256 at the front end of the combine 250 (e.g., a cutting deck having a base cutter, rollers, etc., such as those illustrated above with respect to figure 2) engages and cuts the crop from one or more crop rows. In the present example, the combine 250 is configured to simultaneously harvest the crop from the first and second crop rows 258 and 260. Naturally, the combine 250 can be configured to harvest crops from more than or less than two rows at a time. A plurality of unharvested crop rows 262, 264, and 266 are also shown in Figures 3-1 and 3-2. Reference numeral 268 illustrates field areas 252 that have already been harvested by combine 250.

[0074] The harvester 250 includes a plurality of field sensors (e.g., the field sensors 140 illustrated in figure 1). Pre-harvest IN SITU field sensors 270 and 272 are configured to obtain indications of field topography in unharvested areas in the path of the combine 250 (e.g., crop beds in crop rows 258 and 260). Thus, sensors 270 and 272 are configured to detect field topography in field areas 252 prior to those areas being harvested by harvesting device 256. Some examples include, but are not limited to, RADAR detection systems, ultrasonic sensors, cameras or other image forming sensors, etc.

[0075] Harvester 250 also includes a plurality of post-harvest IN SITU field sensors configured to receive IN SITU data from field areas 252 after those areas have been harvested by harvesting device 256. In the illustrated example, the sensors IN SITU post-harvest field probes 274 and 276 are configured to measure furrow depth between adjacent crop beds. “Culture bed” refers to the planting locations of crop plants (usually in rows), and “furrow” refers to the area between adjacent crop beds. For illustration, as shown in Figure 3-2, a furrow 278 is formed between the crop row 258 and the crop row 280 in the already harvested area 268. The furrow depth 282 represents a distance from the bottom of the furrow 278 to a reference point, which can be used by the combine 250. In one example, the furrow depth 282 represents the distance from the crop bed 284 to the bottom of the furrow 278. In another example, the furrow depth can be the distance from the location from sensor mounting on combine 250 to the bottom of furrow 278. Similarly, crop bed or ground height represents a distance from a reference point to the top of crop bed 284. For example, based on signals from crop sensors sensor 270, the harvester 250 can determine the distance from the sensor 270 to the top of the crop bed 284, and this distance can be used to control the harvesting device 256 to cut the sugarcane plants 286 at a desired height of culture bed 284. Additionally, sensor 276 can generate sensor signals indicative of furrow depth between rows 260 and 262. Similarly, sensor 274 can generate sensor signals indicative of furrow depth between row 258 and row previously harvested 280.

[0076] External map layers can be created to show the pre and post row profiles and topography of the field, based on sensor signals, over time.

[0077] During operation, the conveyors 288 are controlled to move along these grooves to avoid damage to the sugar cane shoots. Also, sensors 274 and/or 276 generate sensor signals indicative of the crop bed height of the adjacent row, which is adjacent to the harvester 250. For example, sensor 276 receives an indication of the crop bed height of the crop row. 262 adjacent to combine 250, when combine 250 is harvesting crop row 260. As discussed in greater detail below, this indication of adjacent crop row height can be used as a prediction of ground height during the subsequent combine pass 250 in field 252 that harvests crop row 262.

[0078] Harvester 250 also includes IN SITU field sensors 290 and 292 that obtain data representing the area of crop rows 258 and 260 that have already been harvested by harvesting device 256. For example, sensors 290 and 292 can generate indications of the height of the crop bed of rows 258 and 260, after the crop has been harvested from that field area 252. Alternatively, or additionally, sensors 290 and 292 can generate indications of height of stubble remains after the crop has been cut by the harvesting device 256.

[0079] The combine 250 also includes a gyroscope or other orientation sensor 294 configured to sense a pitch and/or roll of the combine 250 as the combine 250 moves over the field 252.

[0080] Figure 4 is a block diagram illustrating an example of a cutting height detection and control system 156. As shown, the system 156 includes a previous data receiving component 302, a data receiving component IN SITU 304, a field topography detection component 306, a stubble height detection component 308, a furrow depth detection component 310, a compaction detection component 312, a cutting height component 314, a machine posture detection component 316, a mode selection detection component 318, remote system interaction component 320, and an action generation component 322. The system 156 is also illustrated as having one or more processors or servers 324, and may also include other items 325.

[0081] The back data receiving component 302 is configured to receive data previously generated or collected, for example, from the back data collection system 120, in FIG. 1. The back data may include, but is not limited to, data of sensors obtained from other machines, such as other agricultural machines, unmanned aerial vehicles or Drones, to name a few. Also, the above data may include field planting maps, field terrain maps, and field compaction maps.

[0082] The IN SITU data receiving component 304 is configured to receive IN SITU data, which includes reliable ground data and/or data obtained in real time or substantially real time during operation of the harvesting machine 102. The data may be obtained from sensors 124 and/or from the IN SITU data collection system 126.

[0083] The field topography detection component 306 is configured to detect field topography, such as crop bed height, furrow depth, etc., based on previous data and/or IN SITU data. Component 306 may include a pre-harvest detection component 326 configured to detect field topography in areas of the field prior to the current harvesting operation being performed by harvesting machine 102. Referring to the example of Figure 3-1, the crop bed height and/or furrow depth may be identified based on sensor signals from sensors 270 and 272. Also, component 306 includes a post-harvest sensing component 328 configured to detect field topography in crop areas. field after the harvesting operation being performed by harvesting machine 102. For example, component 328 may identify crop bed height, furrow depth, etc. based on sensor signals received from sensors 276, 278, 290, and/or 292. Component 306 also includes a subsequent pass prediction component 330 configured to generate field topography prediction in an adjacent crop row, to a subsequent pass. In the example of Figure 3-1, component 330 predicts a culture bed height for row 262 based on sensor signals received from sensor 276.

[0084] The crop stubble height detection component 308 is configured to detect the height of crop stubble in post-harvested areas of the field. For example, based on sensor signals from sensors 290 and 292, component 308 determines the height of crop residue left in rows 258 and 260 after they are harvested by harvesting device 256. In one example, crop stubble height detection crop stubble may be based on image processing on images obtained from imaging sensors, or any other appropriate sensor inputs.

[0085] The furrow depth detection component 310 is configured to detect the depth of furrows between crop rows. It is noted that furrow depth can be detected pre-harvest as well as post-harvest, and detected changes in furrow depth can be used by component 312 to generate a compaction metric that indicates a degree to which the furrow has been compacted. by the harvester 102 crossing the field. In one example, component 312 includes a map generator 332 configured to generate a compaction map that indicates, for different areas of the field, compaction that indicates changes in the topography of the field due to the operation of the harvesting machine 102.

[0086] The cutting height component 314 includes a target selector 334 configured to select a target cutting height, which may be based on operator input, automatically based on automated processes, or in other ways. Component 314 includes a limit generator 336 that generates a cutting height limit, a cutting height calculation component 338 configured to calculate the position of the harvesting device 256 to obtain the target cutting height, and a comparison component. 340 configured to compare the cutting height to the limit.

[0087] The machine posture detection component 316 is configured to detect changes to the pitch and/or roll of the machine 102 as the machine 102 traverses the field. For example, harvesting machine 102 may experience side-to-side rolling due to differences in furrow depth and/or different degrees of compaction when tracks, wheels, or other traction elements pass through the furrows.

[0088] The mode selection detection component 318 is configured to select a plurality of different detection modes for generating post-harvest performance. For example, component 318 may include a weighting component 342 for weighting different detection modes and a machine learning component 344 for selecting detection modes. A training component 345 is configured to train the machine learning component 344 based on training data.

[0089] For example, component 316 may utilize a predictive model that is reviewed when machine 102 is performing an operation and while additional IN SITU sensor data is collected. Revision of the model in response to new data may employ machine learning methods. Without limitation, machine learning methods may include memory networks, Bayes systems, decision trees, eigenvectors, eigenvalues and machine learning, evolutionary and genetic algorithms, expert systems/rules, Support Vector Machines, symbolic reasoning /by engines, generative adversarial networks (GANs), graph analytics and ML, linear regression, logistic regression, LSTMs and recurrent neural networks (RNNSs), convolutional neural networks (CNNs), MCMC, cluster analysis, Reom Forests, learning Reinforcement or reward-based machine learning. Learning can be supervised or unsupervised.

[0090] For example, artificial intelligence (AI) can be used to identify machine capabilities, and to determine how to adjust machine settings to obtain work assignment criteria and target work quality. Machine learning and training components include a variety of different types of learning mechanisms, such as a neural network that is trained based on corresponding training logic using training data. Briefly, a neural network can include a deep neural network (DNN), such as a convolutional neural network (CNN). Naturally, other types of classification or learning mechanisms, such as rule-based classifiers, Bayesian Network, decision trees, etc. can be used.

[0091] The machine learning component 344 includes a machine learning model configured to determine changes to adjust the subsystems 108 that obtain the target quality of work from the machine 102. The machine learning model may take into account inputs from external sensors.

[0092] The remote system interaction component 320 is configured to interact with a cloud computing system or other remote computing system, such as the system 118 illustrated in Figure 1. The action generator component 322 is configured to generate action signals for controlling the controllable subsystems 108, the communication system 114, the sensors 124, or any other component in the harvesting machine 102.

[0093] Figure 5 is a flowchart illustrating an example of a cutter height detection and control for an agricultural harvesting machine. For illustration, but not limitation, figure 5 will be discussed in the context of figure 4.

[0094] In block 402, machine 102 is operating in a field. A detection mode is selected in block 404. An example of detection mode selection is discussed below with reference to Figure 11. Briefly, however, the detection mode defines a data source and scheme for cutter height control. . For example, the selected mode may include a predictive mode (block 406) that generates and uses culture bed height predictions. Another example detection mode includes IN SITU pre-harvest sensing (block 408) which uses IN SITU sensors to detect field topography in areas of the field prior to harvest. Also, a sensing mode may utilize the load on the combine (block 410), such as pressure sensors that detect a type of load on the mower drive motors, which may indicate base mower performance (e.g., whether the base cutter is cutting too high or too low). The detection mode may also utilize a combination of various types of inputs and control schemes, as represented in block 412.

[0095] The detection mode can be selected by the operator 110 or by another user (block 414), and/or automatically (block 416). Of course, the detection mode can be selected in other ways as also represented in block 418.

[0096] In block 420, pre-collected field data is received, which indicates the topography of the field in a particular area of the field. For example, the data may include previous data, as represented in block 422. As noted above, the previous data may include a previously generated or obtained field map prior to the current operation of machine 102 in the field. Also, as depicted in block 424, the pre-generated field data may include IN SITU data generated based on IN SITU sensors during current operation of machine 102. Referring to the example of Figure 3-1, the IN SITU data may include data generated based on input from one or more of sensors 270, 272, 274, 276, 290, and 292. Naturally, the pre-collected field data may also include other types of data, as represented in block 426.

[0097] In block 428, a height of a crop bed in the particular area is determined based on pre-harvested field data. In one example, the height determination uses a prediction generated based on a previous pass, as represented in block 430. Naturally, the height of the culture bed can also be determined in other ways, as represented in block 432.

[0098] In block 434, a cutting height adjustment point is obtained. The cutting height set point represents a height at which the crop should be cut from the top of the crop bed. As noted above, in the case of harvesting sugar cane, it is often desired to cut the sugar cane stalk close to ground level without coming into contact with the ground. The cutting height set point can be achieved in any of a number of ways. For example, the cutting height set point may be based on operator input in block 436. Also, the cutting height set point may be automatically obtained in block 438, for example, based on a cutting plane. mission or automated control scheme. Of course, the cutting height adjustment point can also be adjusted in other ways, as represented in block 440.

[0099] In block 442, a height of the harvesting device (e.g., a base cutter) is controlled based on the given height of the crop bed and the cutting height set point. The harvesting device is controlled at block 444 to harvest culture from the particular area. During harvesting, changes in the posture of the machine can be detected in block 446, and changes in the load of the harvesting device can be detected in block 448.

[00100] In block 450, post-harvest field data is received and is indicative of the field topography of the particular area, after the crop has been harvested from that area. In one example, post-harvest field data is based on IN SITU sensor signals, e.g., from one or more sensors 274, 276, 290, or 292, as represented in block 452. The field data Post-harvest information may include field topography, such as crop bed height in the post-harvest area (block 454), and/or furrow depth (block 456). Alternatively, or additionally, stubble height is detected post-harvest in block 458. Of course, other post-harvest field data can also be obtained, as indicated in block 459.

[00101] In block 460, one or more actions are performed based on post-harvest field data. For example, the height of cut may be adjusted in block 462. For example, if the stubble height detected in block 458 is determined to have more than one threshold deviation from the height of cut adjustment point, the height of cut may be adjusted. be raised or lowered. In a particular example, assume that the cutting height set point is adjusted to cut the sugar cane stalk 2.54 cm (one inch) from the crop bed. If the stubble height is subsequently determined to be 7.62 cm (3 inches), then the cutting height can be lowered by 5.08 cm (2 inches). In another example, if the post-harvest field data indicates that the post-harvest crop bed height is lower than the crop bed height of the pre-generated field data, then the control system 156 may determine that the harvesting device contacted the field, that is, the harvesting device dragged on the portion of the soil when the harvesting device performed the harvesting operation. In this case, the cutting height can be raised to reduce the likelihood of further ground contact. Alternatively, or additionally, in block 462, a height of one or more culture collecting and gathering devices (e.g., volute or culture dividers, etc.) is controlled based on the detected groove depth.

[00102] In block 464, pre-harvest data collection can be calibrated based on post-harvest field data. For example, with reference to Figure 3-1, the pre-harvest IN SITU sensor 270 is calibrated based on determining sensor data generated by the sensor 290. For example, if the sensor 290 indicates a height from the ground that is 5 .08 cm (2 inches) higher than the floor height detected by sensor 270, then the output of sensor 270 can be calibrated to resume a more accurate measurement.

[00103] In the case of previous data, such as a field map, the calibration in block 464 may adjust the previous data based on post-harvest field data. For example, previous harvesting operations, spraying operations, or other field care operations, weather, erosion, etc. can change the field topography at the instant the data was obtained, and block 464 can adjust the data based on reliable real-time ground data.

[00104] In block 466, the prediction for a subsequent pass can be generated, based on post-harvest field data. For example, as discussed above with respect to Figure 3-1, sensor 276 may obtain an indication of furrow depth between rows 260 and 262 and an indication of the height of the crop bed in row 262. This indication of height of crop row from sensor 276 can be used as a prediction for crop bed height for row 262 when combine 250 makes a subsequent pass to harvest rows 262 and 264.

[00105] In block 468, furrow depth can be detected from post-harvest field data. For example, referring back to Figure 3-1, sensor 276 can generate a furrow depth indication between rows 260 and 262. Changes in furrow depth from pre-harvest to post-harvest can be used to generate a compaction indicating a degree to which traversal of the harvesting machine 102 has compacted the given furrow. Also, compaction can be detected based on machine posture, e.g., roll angle indicating a degree to which the machine 102 has rolled to one side or the other. A compression map can be created in block 470, and can be stored and/or output for future field operations. Naturally, other actions can also be performed, as represented in block 472.

[00106] In block 474, if the harvesting operation is continued, the operation returns to block 420 for subsequent areas of the field.

[00107] Figure 6 is a flowchart illustrating an example of determining the height of a culture bed in block 428 in Figure 5. In block 502, an IN SITU sensor signal is received indicating the vertical distance from the sensor position to the top of the culture bed. For example, pre-harvested field data received at block 420 may include a sensor signal from sensor 270 through the crop standing in front of harvesting machine 102. A distance between the sensor position of sensor 270 and the row culture bed 258 is determined.

[00108] In block 504, the relative positions of the sensor and the harvesting device (e.g., the base cutter) are determined. For example, the determination may be based on machine dimensions stored in data storage 130, as represented in block 506. Of course, relative positions may also be determined in other ways, as represented in block 508.

[00109] In block 510, the position of the cutter and the position of the culture bed are determined based on the distance from the culture bed to the sensor position and the relative positions of the sensor and the harvesting device.

[00110] Figure 7 is a flowchart illustrating an example of calibration in block 464 in Figure 5. In block 522, post-harvest field data is received, e.g., sensor signals from sensor 290, map layers generated by the system 156, etc. Field data may indicate crop bed height and/or crop stubble height. For example, the height of the culture bed is determined based on a z component of coordinates detected on the field surface. Furthermore, the crop stubble height can be calculated by component 308 based on signals from sensors 148.

[00111] For the culture bed height data, in block 524, the pre-harvest culture bed height, which was used to control the cutting height, is identified. The detected post-harvest culture bed height is compared to the identified pre-harvest height of the culture bed in block 526. If the difference is above a threshold in block 528, then in block 530, the pre-harvest is calibrated based on the difference. As noted above, if the pre-harvest detected culture bed height is significantly different from the post-harvest detected culture bed height, the pre-harvest sensor is calibrated so that its output more closely matches the detection of post-harvest.

[00112] In block 532, the cutting height is adjusted based on post-harvest data. For example, if the post-harvest crop bed height is determined to be shallower than the pre-harvest height, the cutting height can be raised to reduce the likelihood of other ground contact by the mower.

[00113] Referring back to block 522, if post-harvest field data indicates crop stubble height, block 534 identifies a cutting height set point for the particular area. That is, block 534 determines the expected height of the crop stubble. In other words, if the crop height is adjusted to cut 5.08 cm (2 inches) from the crop bed, then approximately 5.08 cm (2 inches) of crop residue would be expected post-harvest. In block 536, the detected crop stubble height is compared with the cutting height set point. If the difference is above a threshold in block 538, the pre-harvest sensor can be calculated, as noted above.

[00114] Figure 8 is a flowchart illustrating an example of generating a prediction in block 466 shown in Figure 5. In block 552, IN SITU field data is received from a sensor, such as sensor 276, in the post area. harvested. A furrow depth between a first harvest row (e.g., row 260) and an adjacent second unharvested row (e.g., row 262) is determined in block 554. In block 556, the prediction of the bed height crop in the second row (e.g. row 262) is generated based on field data. The prediction is correlated to the corresponding location along row 262, as represented in block 558.

[00115] In block 560, the prediction is used when harvesting the second row during a subsequent pass through the harvesting machine. For example, in the second pass, system 156 may include comparing a current sensed crop bed height (sensed by sensor 272 at the front of the machine) with the predicted value, as represented in block 562. In block 564, one of the predicted values or current values can be selected in block 564. The predicted or current values can be mixed in block 566, or the crop row height prediction can also be used in other ways, as represented in block 568.

[00116] Figure 9 is a flowchart illustrating an example of groove depth and compaction factor detection in block 468, shown in Figure 5. In block 602, pre-collected field data is received. In one example, block 602 is similar to block 420 discussed above with respect to Figure 5.

[00117] In block 604, a first indication of furrow depth between the first and second crop rows is generated. The harvester is controlled to harvest crop in block 606 and post-harvest field data is received in block 608. In one example, block 608 is similar to block 450 discussed above with respect to FIG. 5.

[00118] In block 610, a second furrow depth indication is generated based on post-harvest field data. The second furrow depth indication is generated based on sensor data obtained after the furrow has been crossed by the harvesting machine. In block 612, a compaction factor and/or a machine roll angle can be determined based on the first and second groove depth indications. For example, differences in furrow depth may indicate a level of compaction, such as a compaction depth, a degree of compaction, caused by the machine traversing the furrow. Machine roll can be determined based on groove depth, such as based on the amount of compaction of the given groove and/or comparing the groove depth with another groove on the opposite side of the machine.

[00119] The cutting height can be adjusted in block 614 based on the compaction and/or rolling of the machine. For example, if it is determined that the machine has rolled to one side, the base cutter can be raised on that side to prevent contact with the ground.

[00120] In block 616, a compression map can be generated, which maps compression factors to corresponding locations in the field. The compression map may be stored in block 618 and/or provided in block 620 to another system for use in a subsequent operation.

[00121] Figure 10 is a flowchart illustrating an example of field contact detection by a harvesting device. In block 652, pre-harvest field data is received, and in block 654, a pre-harvest height of a crop bed is determined. In block 656, a cutting height set point is obtained, and a harvesting device height is adjusted in block 658. In block 660, the combine is controlled to harvest the field and post-harvest field data is received. in block 662. In one example, blocks 652, 654, 656, and 658 are similar to blocks 420, 428, 434, and 442, respectively, discussed above with respect to Figure 5.

[00122] In block 664, a post-harvest height of the culture bed is determined. An example is discussed above with respect to block 454 in Figure 5. In block 666, a post-harvest measurement confidence is determined. For example, the confidence determination may be based on historical performance data in block 668. Illustratively, confidence indicates a degree to which post-harvest measurements are considered accurate.

[00123] In block 670, the operation determines whether the post-harvest height indication indicates a lower height than the pre-harvest indication. If so, the operation proceeds to block 672, where a field contact is identified, which indicates that the base cutter (or other cutting device) has contacted the field and changed the height of the floor. In one example, a field contact is identified based on trust determined in block 666 exceeding a threshold. Alternatively, or additionally, the field contact may be identified based on the detected groove depth and/or the detected compaction factors, as discussed above and illustrated in block 674.

[00124] In block 676, the cutting height is controlled to prevent further contact with the ground. In block 678, if a field map is used for the pre-generated field data, the field map may be updated to reflect ground contact. If the process continues in block 680, the operation returns to block 652.

[00125] Figure 11 is a flowchart illustrating an example of mode selection in block 404 shown in Figure 5. In block 702, a current detection mode is selected using a machine learning algorithm. Sensor input is received at block 704 based on the selected mode. For example, if a predictive detection mode is selected, then predictive crop heights can be obtained. Alternatively, if IN SITU pre-harvest sensing is selected, field data can be obtained from IN SITU sensors on the harvesting machine 102.

[00126] In block 706, the harvesting machine is controlled to harvest the field and performance is detected in block 708. Performance may indicate a degree to which the desired height of cut has been obtained, or any other performance metrics that are desired. For example, productivity, quality, etc. can be generated as performance metrics.

[00127] In block 710, the machine learning algorithm is trained or updated, and if the process continues in block 712, the operation proceeds to block 714, in which the detected performance is compared with a threshold. If the performance is not below the threshold, the operation returns to block 704, where additional sensor data is obtained and the machine learning algorithm can be further trained. If performance is below the threshold, operation returns to block 702, where a different detection mode can be selected to improve performance.

[00128] It can thus be seen that the present features provide a system for cutting height detection and control that improves the performance of the combine. The system uses IN SITU post-harvest data to identify post-harvest field topology and/or crop stubble height which is used to control cutting height in subsequent areas. For example, a pre-harvest field sensor can be calibrated based on post-harvest IN SITU data. The system can reduce the likelihood of ground contacts and/or increase production through increased cutting height accuracy.

[00129] The present discussion mentioned processors and servers. In one example, the processors and servers include computer processors with associated memory and timing circuitry, not separately shown. Processors and servers are functional parts of the systems or devices to which the processors and servers belong and by which they are powered, and facilitate the functionality of other components or items in those systems.

[00130] Also, a number of user interface displays have been discussed. User interface displays can take a wide variety of different forms and can have a wide variety of different user-actuable input mechanisms arranged within them. For example, user-actuable input mechanisms can be text boxes, check boxes, icons, connections, drop-down menus, search boxes, etc. User-actuable input mechanisms can be actuated in a wide variety of different ways. For example, user-actuable input mechanisms can be actuated using a point-and-click device (such as a rolling ball or mouse). User-actuable input mechanisms can be actuated using hardware keys, switches, a control lever or keyboard, free levers or thumb panels, etc. User-actuable input mechanisms can also be actuated using a virtual keyboard or other virtual actuators. Furthermore, when the screen on which the user-actuable input mechanisms are displayed is a touch screen, the user-actuable input mechanisms may be actuated using touch gestures. Also, when the device displaying the same has voice recognition components, the user-actuable input mechanisms can be actuated using voice commands.

[00131] A number of databases were also discussed. It will be noted that the databases can each be broken down into multiple databases. All of the databases may be local to the systems that access the databases, all of the databases may be remote, or some of the databases may be local while others may be remote. All of these configurations are covered here.

[00132] Also, the figures show a number of blocks with functionality assigned to each block. It will be noted that fewer blocks can be used so that the functionality is performed by fewer components. Also, more blocks can be used with functionality distributed among more of the components.

[00133] It will be noted that the above discussion described a variety of different systems, components, logic, and interactions. It will be appreciated that any or all of such systems, components, logic, and interactions may be implemented by hardware items such as processors, memory, or other processing components, including, but not limited to, artificial intelligence components such as neural networks, some of which are described below, that carry out the functions associated with those systems, components, logic, or interactions. Furthermore, any or all of the systems, components, logic and interactions may be implemented by software that is loaded into memory and is subsequently executed by a processor or server or other computing component, as described below. Any or all of the systems, components, logic and interactions can also be implemented by different combinations of hardware, software, firmware, etc., some examples of which are described below. These are some examples of different frameworks that can be used to implement any or all of the systems, components, logic, and interactions described above. Other structures can also be used.

[00134] Figure 12 is a block diagram of an example of a harvesting machine architecture 100, shown in Figure 1, where the machine 102 communicates with elements in a remote server architecture 800. In one example, the architecture Remote server server 800 may provide computing, software, data access, and storage services that do not require the end user's knowledge of the physical location or configuration of the system providing the services. In several examples, remote servers can provide services over a wide area network, such as the Internet, using appropriate protocols. For example, remote servers can deliver the applications over a wide area network and the remote servers can be accessed through a Web browser or any other computing component. Software or components shown in the previous figures as well as the corresponding data can be stored on servers in a remote location. The computing resources in a remote server environment can be consolidated at a remote data center location or the computing resources can be dispersed. Remote server infrastructures can provide services across shared data centers, even when the services appear as a single point of access to the user. Thus, the components and functions described here can be provided from a remote server at a remote location using a remote server architecture. Alternatively, the components and functions may be provided from a conventional server, or the components and functions may be installed on client devices directly, or in other ways.

[00135] In the example shown in figure 12, some items are similar to those shown in the previous figures and the items are similarly enumerated. 12 specifically shows that the system 156 of the previous figures can be located on the remote site server 802. Therefore, the machine 102, the machine 116, and/or the system 118 can access those systems through the remote site server 802 .

[00136] Figure 12 also represents another example of a remote server architecture. Figure 12 shows that it is also contemplated that some elements of the previous figures are arranged on the remote site server 802, while others are not. By way of example, one or more of databases 130 and system 156 may be located at a location separate from location 802, and accessed through the remote server at location 802. Regardless of where the systems and databases are located , the systems and databases can be accessed directly by the machines 102 and/or 116 through a network (either a wide area network or a local area network), the systems and databases can be hosted on a remote location by a service, or the systems and databases may be provisioned as a service, or accessed by a connection service that resides in a remote location. All of these architectures are covered here.

[00137] It will also be noted that the elements of the figures, or portions thereof, can be arranged in a wide variety of different devices. Some of those devices include servers, desktop computers, portable computers, tablet computers, or other mobile devices such as pocket computers, cell phones, smart phones, multimedia players, personal digital assistants, etc.

[00138] Figure 13 is a simplified block diagram of an illustrative example of a portable or mobile computing device that can be used as a user or client portable device 16, wherein the present system (or parts of the present system ) can be implemented. For example, a mobile device may be implemented in the machine operator compartment 102 for use in generating, processing, or displaying machine speed data and machine performance metrics. Figures 11 to 12 are examples of portable or mobile devices.

[00139] Figure 13 provides a general block diagram of the components of a client device 16 that can run some of the components shown in Figure 1, which interact with them, or both. In device 16, a communications connection 13 is provided, which allows the portable device to communicate with other computing devices and, in some embodiments, provides a channel for receiving information automatically, such as by scanning. Examples of communications connection 13 include enabling communication through one or more communication protocols, such as wireless services used to provide cellular access to a network, as well as protocols that provide local wireless connections to networks.

[00140] In other examples, applications may be received on a removable Secure Digital (SD) card, which is connected to an interface 15. The interface 15 and communication connections 13 communicate with a processor 17 (which may also incorporate the processors or servers of the other figures) together with a bus 19 that is also connected to memory 21 and input/output (I/O) components 23, as well as clock 25 and local system 27.

[00141] I/O components 23, in one embodiment, are provided to facilitate input and output operations. I/O components 23 for various embodiments of device 16 may include input components such as keys, touch sensors, optical sensors, microphones, touch screens, proximity sensors, accelerometers, orientation sensors, and output components. , such as a display device, a speaker, and or a printer port. Other I/O components 23 may also be used.

[00142] Clock 25 illustratively includes a real-time clock component, which provides the time and date. Clock 25 may also, illustratively, provide timing functions for processor 17.

[00143] Local system 27 illustratively includes a component that provides a geographic location of device 16. Local system 27 may include, for example, a global positioning system (GPS) receiver, a LORAN system, a passive recognition system , a cellular triangulation system, or other positioning systems. The local system 27 may also include, for example, mapping software or navigation software, which generate the desired maps, navigation routes and other geographic functions.

[00144] Memory 21 stores the operating system 29, network settings 31, applications 33, application configuration settings 35, data storage 37, communication controllers 39, and communication configuration settings 41 Memory 21 may include all types of tangible, volatile and non-volatile, computer-readable memory devices. Memory 21 may also include computer storage media (described below). Memory 21 stores computer-readable instructions, which, when executed by processor 17, cause the processor to perform computer-implemented steps or functions in accordance with the instructions. The processor 17 can be activated by other components to also facilitate its functionality.

[00145] Figure 14 shows an example in which the device 16 is a tablet computer 850. In Figure 14, the computer 850 is shown with the user interface display screen 852. The screen 852 may be a touch-sensitive screen. touch or a pen-enabled interface that receives input from a pen or needle. The display 852 may also use a virtual on-screen keyboard. Of course, screen 852 can also be affixed to a keyboard or other user input device via an appropriate affixing mechanism, such as a wireless connection or USB port, for example. The computer 850 may also illustratively receive voice input.

[00146] Figure 15 shows that the device may be a smart phone 71. The smart phone 71 has a touch-sensitive display 73 that displays icons or tiles or other user input mechanisms 75. The mechanisms 75 may be used by a user to run applications, make calls, perform data transfer operations, etc. In general, the smart phone 71 is built on a mobile operating system and offers more advanced computing power and connectivity than a regular phone.

[00147] Note that other shapes of the devices 16 are possible.

[00148] Figure 16 is an example of a computing environment, in which the elements of the previous figures, or parts thereof, (for example) can be implemented. Referring to Figure 16, an example system for implementing some embodiments includes a general purpose computing device in the form of a computer 910 programmed to operate as discussed above. Computer components 910 may include, but are not limited to, a processing unit 920 (which may include processors or servers of the preceding figures), a system memory 930, and a system bus 921 that couples various system components including system memory to processing unit 920. System bus 921 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of architectures. of bus. The memory and programs described with respect to the previous figures can be implemented in corresponding portions of figure 16.

[00149] Computer 910 typically includes a variety of computer-readable media. Computer-readable media can be any available media that can be accessed by computer 910 and includes both volatile and non-volatile media, removable media and non-removable media. By way of example, and not limitation, computer readable media may include computer storage media and communication media. Computer storage media is different from, and does not include, a modulated data signal or carrier wave. Computer storage media includes hardware storage media including removable and non-removable media, both volatile and non-volatile, implemented in any method or technology for storing information such as computer-readable instructions, data structures, modules, program or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile discs (DVD) or other optical disc storage, magnetic cassettes, magnetic tape , storage on magnetic disk or other magnetic storage devices, or any other medium, which can be used to store the desired information and which can be accessed by computer 910. The media can incorporate computer readable instructions, data structures , program modules or other data in a transport mechanism and include any means of providing information. The term “modulated data signal” means a signal that has one or more of its characteristics defined or changed in order to encode information in the signal.

[00150] System memory 930 includes computer storage media in the form of volatile and/or non-volatile memory, such as read-only memory (ROM) 931 and random access memory (RAM) 932. An input system/ Basic output 933 (BIOS), containing the basic routines that help transfer information between elements within the computer 910, such as during startup, is typically stored in ROM 931. RAM 932 typically contains data and/or program modules that are immediately 16 illustrates operating system 934, application programs 935, other program modules 936, and program data 937.

[00151] Computer 910 may also include other volatile/non-volatile, removable/non-removable computer storage media. By way of example, Figure 16 illustrates a hard disk drive 941 that reads from, or writes to, non-volatile, non-removable magnetic media, an optical disk drive 955, and non-volatile optical disk 956. The disk drive hard drive 941 is typically connected to the system bus 921 through a non-removable memory interface, such as interface 940, and the optical disk drive 955 is typically connected to the system bus 921 through a removable memory interface, such as interface 950 .

[00152] Alternatively, or additionally, the functionality described here may be performed, at least in part, by one or more hardware components. For example, and without limitation, illustrative types of hardware components that may be used include programmable logic gate networks (FPGAs), application-specific integrated circuits (e.g., ASICs), application-specific standard products (e.g., ASSPs) , systems on a chip (SOCs), complex programmable logic devices (CPLDs), etc.

[00153] The controllers and their associated computer storage media, discussed above and illustrated in Figure 16, provide storage of the computer-readable instructions, data structures, program modules, and other data for the computer 910. In Figure 16, For example, hard disk drive 941 is illustrated as storing operating system 944, application programs 945, other program modules 946, and program data 947. Note that these components may either be the same as, or different from, of operating system 934 , application programs 935 , other program modules 936 , and program data 937 .

[00154] A user can feed commands and information to computer 910 through input devices, such as a keyboard 962, a microphone 963, and a pointing device 961, such as a mouse, rolling ball, or touch panel. Other input devices (not shown) may include a control stick, game panel, satellite dish, digitizer, or the like. These and other input devices are often connected to the processing unit 920 through a user input interface 960 that is coupled to the system bus, but may be connected through other interface and bus structures. A visual display 991 or other type of display device is also connected to system bus 921 through an interface, such as a video interface 990. In addition to the monitor, computers may also include other peripheral output devices, such as speakers 997 and printer 996, which can be connected via a peripheral output interface 995.

[00155] Computer 910 is operated in a networked environment using logical connections (such as a controller area network - CAN, a local area network - LAN, or wide area network WAN) to one or more remote computers , such as a remote computer 980.

[00156] When used in a LAN-connected environment, the computer 910 is connected to the LAN 971 through a network interface or adapter 970. When used in a WAN-connected environment, the computer 910 typically includes a Modem 972 or other means of establishing communications over the WAN 973, such as the Internet. In a networked environment, program modules can be stored on a remote memory storage device. 16 illustrates, for example, that remote application programs 985 may reside on remote computer 980.

[00157] It should also be noted that the different examples described here can be combined in different ways. That is, parts of one or more examples may be combined with parts of one or more other examples. All of this is covered here.

[00158] Although the matter has been described in language specific to structural characteristics and/or methodological acts, it should be understood that the matter defined in the attached claims is not necessarily limited to the specific characteristics or acts described above. On the contrary, the specific characteristics and acts described above are set out as exemplary forms of implementation of the claims.

Claims (15)

1. Method for controlling an agricultural harvesting machine (102; 200; 250) having a harvesting device (164) configured to engage and cut crops in a field, characterized by the fact that the method comprises: receiving (420) an indication of field topography in a first area of the field prior to the first area being harvested; controlling (442) a position of the harvesting device to harvest the crop in the first area based on the indication of field topography in the first area; receiving (450) IN SITU data from the first area of the field after the first area is harvested by the harvesting device; receiving (420) an indication of field topography in a second area of the field prior to the second area being harvested; and controlling (442) the position of the harvesting device to harvest the crop in the second area based on the IN SITU data of the field topography indication in the second area. 2. The method of claim 1, wherein the harvesting device comprises a front end assembly having a cutting device, and further comprising: determining a height of a culture bed in the first area based on the first indication of field topography; and setting a cutting height of the cutting device based on the height of the culture bed. 3. The method of claim 2, wherein receiving indication of field topography in the first area comprises: receiving, from a first sensor on the agricultural harvesting machine, a first sensor signal indicative of a distance from the first sensor to the culture bed. 4. The method of claim 3, wherein receiving data IN SITU comprises receiving a second sensor signal from a second sensor on the agricultural harvesting machine. 5. Method according to claim 4, characterized by the fact that each of the first and second sensors comprises at least one of: a radio detection and ranging (RADAR) sensor; an ultrasonic sensor; or an imaging sensor. 6. The method of claim 4, wherein the second sensor signal is indicative of crop stubble height in the first area, and the method further comprises: determining that the crop stubble height is above a limit; and adjusting a height of the cutting device based on the determination. 7. Method according to claim 4, characterized by the fact that the second sensor signal is indicative of a distance from the second sensor to the culture bed. 8. The method of claim 7, further comprising: determining that the front end assembly has contacted the ground surface in the first area based on the first and second sensor signals; and control the position based on the determination. 9. The method of claim 4, further comprising: adjusting a height of the cutting device based on the second sensor signal. 10. The method of claim 4, further comprising: calibrating the first sensor based on the second sensor signal. 11. The method of claim 1, wherein the first field area comprises a first crop row of a plurality of crop rows in the field, the second field area comprises a second crop row of the plurality of culture rows, which is adjacent to the first culture row, and the method further comprises: generating, based on IN SITU data, a predictive culture bed height in the second culture row; and controlling the position of the harvesting device to harvest the crop in the second crop row based on the predicted crop bed height and the field topography indication in the second crop row. 12. Method according to claim 1, characterized by the fact that it further comprises: detecting the furrow depth between the first and second crop rows based on the field topography; and based on the detected furrow depth, control height of a harvesting device and crop collection of the agricultural harvesting machine. 13. The method of claim 1, further comprising: detecting a first depth of a furrow between a first crop row and a second crop row prior to the first crop row being harvested by the agricultural harvesting machine ; detecting a second furrow depth after the first row of crop is harvested by the agricultural harvesting machine; generating a compaction factor based on the first and second depths; and control the agricultural harvesting machine based on the compaction factor. 14. Agricultural harvesting machine (102; 200; 250), characterized by the fact that it comprises: a set of traction elements for engaging the ground (206; 288); a propulsion subsystem (160) configured to drive one or more of the ground engagement traction elements over a field; a harvesting device (164) configured to engage and cut crops in the field; and a control system (106) configured to: receive an indication of field topography in a first area prior to the first area being harvested by the harvesting device; controlling a position of the harvesting device to harvest the crop in the first area based on the indication of field topography in the first area; receiving IN SITU data from the first area of the field after the first area is harvested by the harvesting device; receiving an indication of field topography in a second area prior to the second area being harvested by the harvesting device; and controlling the position of the harvesting device to harvest the crop in the second area based on the IN SITU data and the field topography indication in the second area. 15. Control system (106) for an agricultural harvesting machine having a harvesting device, characterized in that the control system comprises at least one processor (128); and memory storing instructions executable by at least one processor, wherein the instructions, when executed, cause the control system to: receive (420) a first indication of field topography in a first area to be harvested in a path of the machine agricultural harvest; controlling (442) a position of the harvesting device to harvest the crop in the first area based on the first indication of field topography; receive (450) IN SITU data from the first area of the field after the first area is harvested by the harvesting device; receive (420) a second indication of field topography in a second area of the field to be harvested; and controlling (442) the position of the harvesting device to harvest the crop in the second area based on the IN SITU data and the second field topography indication in the second area.