Abstract: The present disclosure provides systems and methods that measure crop residue in a field from imagery of the field. In particular, the present subject matter is directed to systems and methods that include or otherwise leverage a machine-learned crop residue classification model to determine a crop residue parameter value for a portion of a field based at least in part on imagery of such portion of the field captured by an imaging device. Furthermore, principal components analysis, such as projecting image patches onto Eigen-images, can be performed to reduce the dimensionality of the feature vector provided to the classification model.

Abstract: A control system for a work vehicle includes a controller, a processor, and a memory that causes the processor to receive, via a sensor assembly, sensor signals and convert the sensor signals into a plurality of entries of a full measurement vector. The memory devices causes the processor to determine a first state vector using IMU Kalman filter, update a first subset of entries the full state vector, determine a second state vector using a spatial positioning Kalman filter, update a second subset of entries the full state vector based on the second state vector, determine a third state vector using a vehicle Kalman filter, update a third subset of entries of the plurality of entries of the full state vector based on the third state vector, and control movement of the work vehicle based on at least one of the first state vector, the second state vector, the third state vector, and the full state vector.

Abstract: The present disclosure provides systems and methods that measure crop residue in a field from imagery of the field. In particular, the present subject matter is directed to systems and methods that include or otherwise leverage a machine-learned semantic segmentation model to determine a crop residue parameter value for a portion of a field based at least in part on imagery of such portion of the field captured by an imaging device. For example, the imaging device can be a camera positioned in a downward-facing direction and physically coupled to a work vehicle or an implement towed by the work vehicle through the field.

Abstract: The present disclosure provides systems and methods that measure crop residue in a field from imagery of the field. In particular, the present subject matter is directed to systems and methods that include or otherwise leverage a machine-learned convolutional neural network to determine a level of crop residue for a portion of a field based at least in part on imagery of such portion of the field captured by an imaging device. For example, the imaging device can be a camera positioned in a downward-facing direction and physically coupled to a work vehicle or an implement towed by the work vehicle through the field.

Abstract: The present disclosure provides systems and methods for adjusting the output of a field measurement system to conform to agronomy measurements. In particular, the present subject matter is directed to a calibration process and system that uses a calibration model to convert field measurement data expressed according to an automatic system metric into agronomy data that is expressed according to an agronomy metric.

Abstract: A control system of an agricultural work vehicle system includes a controller configured to receive a set of swath paths, and separate each swath path of the set of swath paths into a set of half swaths. Also, the controller is configured to determine multiple possible connecting paths, and each connecting path of the multiple possible connecting paths connects one half swath of a first half swath of the set of half swaths to a second half swath of the set of half swaths. Moreover, the controller is configured to determine a cost for each possible connecting path of the multiple possible connecting paths. Further, the controller is configured to select a chosen connecting path from the multiple possible connecting paths based on the cost, and output a signal indicative of a travel path for an agricultural work vehicle.

Abstract: A path controller for guiding an autonomous vehicle along a desired path may include an input module that may receive input signals such as, a normal error signal that indicates an off-path deviation of the autonomous vehicle relative to a desired path, a heading signal, and a curvature signal associated with the autonomous vehicle. The path controller may also include a curvature rate module that calculates a curvature rate output signal to guide the autonomous vehicle along the desired path and a communication module that communicates the curvature rate output signal to a steering control system.

Abstract: In one aspect, a system for monitoring sensor performance on an agricultural machine may include a controller configured to receive a plurality of images from the vision-based sensor mounted on an agricultural machine. The controller may be configured to determine an image parameter value associated with each of a plurality of pixels contained within each of the plurality of images. For each respective pixel of the plurality of pixels, the controller may be configured to determine a variance associated with the image parameter values for the respective pixel across the plurality of images. Furthermore, when the variance associated with the image parameter values for a given pixel of the plurality of pixels falls outside of a predetermined range, the controller may be configured to identify the given pixel as being at least one of obscured or inoperative.

Abstract: A work vehicle includes a roof panel forming a portion of a cab of the work vehicle. The work vehicle also includes a headliner disposed between the roof panel and an interior of the cab. In addition, the work vehicle includes a spatial locating antenna positioned between the roof panel and the headliner, such that a top side of the roof panel is positioned above the spatial locating antenna relative to a ground surface. Furthermore, the top side of the roof panel extends beyond a lateral extent and a longitudinal extent of the spatial locating antenna.

Abstract: A method for automatically monitoring soil surface roughness as a ground-engaging operation is being performed within a field may include receiving pre-operation surface roughness data associated with a given portion of the field and receiving post-operation surface roughness data associated with the given portion of the field. In addition, the method may include analyzing the pre-operation and post-operation surface roughness data to determine a surface roughness differential associated with the performance of the ground-engaging operation and actively adjusting the operation of at least one of an associated work vehicle and/or implement when the surface roughness differential differs from a target set for the surface roughness differential.

Abstract: The present disclosure provides systems and methods that measure soil roughness in a field from imagery of the field. In particular, the present subject matter is directed to systems and methods that include or otherwise leverage a machine-learned clod detection model to determine a soil roughness value for a portion of a field based at least in part on imagery of such portion of the field captured by an imaging device. For example, the imaging device can be a camera positioned in a downward-facing direction and physically coupled to a work vehicle or an implement towed by the work vehicle through the field.

Abstract: A system includes a processor. The processor is configured to derive one or more partitions of a field based on a vehicle system data via a learning system. The processor is further configured to derive one or more turn features representative of vehicle turns in the field based on the vehicle system via the learning system. The processor is also configured to derive an autonomous vehicle plan based on the partitions of the field and the turn features via a planning system, wherein the autonomous vehicle plan comprises a planned route of the autonomous vehicle in the field.

Abstract: A planning system for an autonomous work vehicle system includes a controller configured to determine a plan for the autonomous work vehicle system by reducing a cost function, and to output one or more signals indicative of the plan and/or instructions to execute the plan. The plan includes a route of the autonomous work vehicle system through a field, and the cost function includes multiple costs associated with operation of the autonomous work vehicle system.

Abstract: An obstacle detection system for a work vehicle includes a controller having a memory and a processor. The controller is configured to receive a first signal indicative of a position of an obstacle relative to the work vehicle, to determine whether the position of the obstacle corresponds to a position of a movable object coupled to the work vehicle, and to output a second signal indicative of detection of the obstacle in response to determining that the position of the obstacle does not correspond to the position of the movable object.

Abstract: A control system for an autonomous agricultural system includes a display configured to display at least one control function associated with at least one operation. The display is configured to output a first signal indicative of a first input. The control system includes a controller comprising a processor and a memory. The controller is communicatively coupled to the display and configured to receive the first signal indicative of the first input and to send a second signal to the display indicative of instructions to display a second control in an unlocked state. The display is configured to output a third signal to the controller indicative of the second input. The controller is configured to receive the third signal and to output a fourth signal indicative of instructions to control the at least one operation of the autonomous agricultural system.

Abstract: In one embodiment, one or more tangible, non-transitory, machine-readable media comprising instructions configured to cause a processor to receive a signal indicative of a residue coverage on a surface of an agricultural field from a sensor, and the sensor is positioned in front of a row unit of an agricultural product application system relative to a direction of travel. Further, the instructions are configured to cause the processor to determine the residue coverage on the surface of the agricultural field based on the signal, and the residue coverage includes a percentage of the agricultural field that is covered by residue, an evenness of the residue, or a combination thereof. Moreover, the instructions are configured to cause the processor to control an agricultural product control system of the agricultural product application system based on the residue coverage.

Abstract: An electronic control system for a work allows for control of steering despite movement of an implement that may support a load. Control may be based on vehicle position, velocity, acceleration, center of gravity, and heading. A control point is determined despite movement of the load, and may be based upon one or more of roll, yaw, and pitch of the vehicle. The vehicle may be of the type that allows for control only of wheel or track speed and rotational direction. A desired center of gravity is maintained while controlling an error between a desired vehicle trajectory and a determined trajectory in a closed loop manner.

Abstract: A path planning system for a work vehicle includes a controller configured to determine an initial path between an end point of a first swath and a starting point of a second swath. In addition, the controller is configured to identify a restricted region positioned along the initial path, and to determine a first bounding point of the restricted region. The controller is also configured to determine a first waypoint based on the first bounding point. A distance between the first waypoint and the first bounding point is greater than or equal to half of a lateral extent of the work vehicle and less than half of a lateral extent of an implement. Furthermore, the controller is configured to determine an updated path that intersects the first waypoint, and to output signal(s) indicative of the updated path and/or instructions to direct the work vehicle along the updated path.

Abstract: An obstacle detection system includes a controller configured to receive a first signal from a first sensor assembly indicative of presence of an obstacle within a field of view of the first sensor assembly. The controller is configured to receive a second signal from a second sensor assembly indicative of a position of the obstacle within a field of view of the second sensor assembly in response to presence of a movable object coupled to the work vehicle within the field of view of the first sensor assembly. The controller is configured to determine presence of the obstacle within the field of view of the first sensor assembly based on the position of the obstacle, and the controller is configured to output a third signal indicative of detection of the obstacle in response to presence of the obstacle within the field of view of the first sensor assembly.

Abstract: A control system of an agricultural work vehicle system includes a controller that includes a memory and a processor. The controller is configured to determine multiple partitions based, at least in part, on a map of an agricultural field. Further, the controller is configured to determine a partition list of the multiple partitions based, at least in part, on a set of bounding characteristics of each of the multiple partitions. In addition, the controller is configured to determine an order of the multiple partitions based on the partition list of the multiple partitions. Moreover, the controller is configured to output a signal indicative of a travel path for the agricultural work vehicle.