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. 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: In one aspect, a system for adjusting a sampling rate of a sensor mounted on an agricultural machine may include an agricultural machine and a sensor mounted on the agricultural machine, with the sensor being configured to capture data at a sampling rate. The system may also include a controller communicatively coupled to the sensor. The controller may be configured to receive an input indicative of an operational parameter of the agricultural machine and adjust the sampling rate at which the sensor captures data based on the received input.

Abstract: A system for controlling an implement during a tillage operation may include a frame and a ground-engaging tool pivotally coupled to the frame and movable relative to the frame between a retracted position and an extended position. An actuator may be configured to bias the ground-engaging tool towards the extended position during the tillage operation. An adjustable valve may be configured to permit flow out of the actuator when a fluid pressure of the actuator exceeds a reset pressure such that the actuator allows the ground-engaging tool to pivot towards the retracted position. A controller may be configured to determine at least one of an actuator position or a load value indicative of a force applied by the ground-engaging tool against the soil during the tillage operation and adjust the reset pressure based on the at least one of the actuator position or the load value.

Abstract: A system for refilling a resource of an agricultural system includes the agricultural system including a tank that stores the resource. The system also includes a support vehicle including a container that stores the resource, a locating system that outputs a first signal indicative of a position of the support vehicle, a transfer system that includes a resource transfer device, and a controller communicatively coupled to the locating system and the transfer system including a processor and a memory. The controller determines a target location in a field to position the support vehicle, control the support vehicle such that the support vehicle is directed toward the target location, and instruct the transfer system to activate the resource transfer device to transfer the resource from the container to the tank while the position of the agricultural system is within a threshold distance of the target location.

Abstract: In one embodiment, a vehicle system includes a chassis and a vehicle bed coupled to the chassis, the vehicle bed comprising an attachment system configured to fasten a detachable mission platform onto the vehicle bed. The vehicle system further includes a plurality of wheels coupled to the chassis and configured to carry the chassis over a ground. The vehicle system additionally includes a control system comprising a processor configured to determine a mission type for the detachable mission platform. The processor is additionally configured to communicate with the detachable mission platform to actuate at least one actuator of the detachable mission platform based on the mission type.

Abstract: In one aspect, a method for distributing crop material for ensilage may include determining, with a computing device, a storage volume for the crop material. The method may also include dividing, with the computing device, the determined storage volume into a plurality of planes. Each plane of the plurality of planes may be spaced apart from each other plane of the plurality of planes along a vertical direction. Furthermore, the method may include controlling, with the computing device, an operation of at least one of a work vehicle or an associated implement in a manner that distributes a portion of the crop material on a given plane of the plurality of planes.

Abstract: In one aspect, a method for compressing crop material for ensilage may include monitoring, with a computing device, a location of a work vehicle within a silage heap as the work vehicle traverses a layer of crop material within the silage heap. The method may also include determining, with the computing device, a current density of the layer of crop material as the work vehicle traverses the layer of crop material. Furthermore, the method may include controlling, with the computing device, an operation of the work vehicle based on the monitored location and the determined current density such that the work vehicle compresses the layer of crop material.

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: 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 combine having a feeder housing for receiving harvested crop, a separating system for threshing the harvested crop to separate grain from residue, a grain tank for storing the separated grain, a grain tank level sensor for detecting a level of grain in the grain tank, and a controller that controls the combine. The controller is configured to receive the level of grain from the grain tank level sensors, determine a volume of a grain base from the level of the grain, determine a volume of a grain heap above the grain base, determine a total volume by combining the volume of the grain base with the volume of the grain heap.

Abstract: A system including a beamforming-sensor configured to acquire sensor-data representative of a swath in an agricultural field; and a controller configured to determine swath-property-data based on the sensor-data.

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 combine harvester comprising a controller, at least one first sensor adapted to measure a first parameter related to soil deformation, and at least one second sensor adapted to measure a second parameter related to wheel slip. The first sensor is adapted to provide a first output to the controller. The second sensor is adapted to provide a second output to the controller. The controller is configured to determine a ground bearing capacity based on a combination of the first output and the second output.

Abstract: A combine having a feeder housing for receiving harvested crop, a separating system for threshing the harvested crop to separate grain from residue, a grain tank for storing the separated grain, a grain tank level sensor for detecting a level of grain in the grain tank, and a controller that controls the combine. The controller is configured to receive the level of grain from the grain tank level sensors, determine a volume of a grain base from the level of the grain, determine a volume of a grain heap above the grain base, determine a total volume by combining the volume of the grain base with the volume of the grain heap.

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: In one aspect, a system for illuminating a field of view of a vision-based sensor mounted on an agricultural machine may include an agricultural machine having a vision-based sensor. The system may also include a light source configured to emit supplemental light to illuminate at least a portion of the field of view of the vision-based sensor. Furthermore, the system may include a controller communicatively the light source. The controller may configured to control an operation of the light source based on an input indicative of ambient light present within the field of view of the vision-based sensor.

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.