Abstract: A plurality of different visual markers are deployed on a field. The markers include a target marker identifying a portion of the field that has a target of material to be applied to the field and a non-target marker identifying a portion of the field that does not have a target. An on-machine target identification system sense targets as an agricultural machine travels over the visual markers to identify targets in the field. An image processing adjustment controller correlates identified targets with the visual markers to determine an accuracy of the target identification system. An action signal is generated based upon the identified accuracy of the target identification system.

Abstract: An agricultural sprayer includes a spray boom supported by a frame, a spraying system comprising a set of spray nozzles spaced along the spray boom, and a control system configured to control the spraying system to spray a liquid based on a target application to an agricultural field, generate a spray performance metric indicative of performance of the spraying system relative to the target application, and generate a control signal to control the agricultural sprayer based on the spray performance metric.

Abstract: An agricultural sprayer includes a spray boom supported by a frame, a spraying system comprising a set of spray nozzles spaced along the spray boom, and a control system configured to control the spraying system to spray a liquid based on a target application to an agricultural field, generate a spray performance metric indicative of performance of the spraying system relative to the target application, and generate a control signal to control the agricultural sprayer based on the spray performance metric.

Abstract: An agricultural applicator, that applies material to an agricultural field, includes an on-board, real-time image sensor that senses targets for the material to be applied. A controller controls applicators, such as nozzles or other applicators, to apply the material to the sensed targets.

Abstract: An example work machine control system may include cost factor logic to obtain a cost factor for a resource, cost variable logic to obtain a consumption signal from a consumption sensor indicative of consumption of the resource, fill measurement logic configured to receive a fill signal from a fill sensor, the fill signal indicative of a fill state of a container of an earth-moving work machine, fill target logic to determine a target fill level for the container based on the cost factor, the consumption signal and the fill signal and control logic to generate a machine control signal based on the target fill level.

Abstract: A computing system includes image receiving logic configured to receive image data indicative of an image of a field, ground identification logic configured to identify a first image portion of the image representing ground in the field, image segmentation logic configured to identify a remaining image portion that omits the first image portion from the image, and crop classification logic configured to apply a crop classifier to the remaining image portion and identify a second image portion of the image that represents locations of crop plants in the field. The computing system also includes weed identification logic configured to identify locations of weed plants in the field based on the identification of the first and second image portions and control signal generation logic configured to generate a machine control signal based on the identified locations of the weed plants.

Abstract: An example work machine control system includes target fill level determination logic configured to determine a target fill level for a container of an earth-moving work machine, fill level measurement logic configured to receive a sensor signal from a sensor that detects contents of the container and generate a measurement metric indicative of a current fill level of the container based on the sensor signal, and control logic configured to generate a machine control signal based on the measurement metric and the target fill level.

Abstract: A volume of contents in a container of a work vehicle can be estimated in various examples. One example involves a system with a 3D sensor on a work vehicle, where the sensor captures images of a material in a container of the work vehicle. A processor device that is in communication with the 3D sensor determines a volume of the material in the container using the images.

Abstract: A computing system includes image receiving logic configured to receive image data indicative of an image of a field, ground identification logic configured to identify a first image portion of the image representing ground in the field, image segmentation logic configured to identify a remaining image portion that omits the first image portion from the image, and crop classification logic configured to apply a crop classifier to the remaining image portion and identify a second image portion of the image that represents locations of crop plants in the field. The computing system also includes weed identification logic configured to identify locations of weed plants in the field based on the identification of the first and second image portions and control signal generation logic configured to generate a machine control signal based on the identified locations of the weed plants.

Abstract: One or more undesired objects can be detected in a restricted zone at worksite. In one example, a base station is configured to be placed in or around the restricted zone. The base station includes a housing, a sensor suite, and a base station controller. The sensor suite is configured to detect one or more undesired object in a subzone at least partially overlapping with the restricted zone and generate an output signal based on the undesired object. The subzone is limited by a range of detection from the sensor suite. The base station controller can receive the output signal from the sensor suite and transmit an alert signal. A warning indicator can receive the alert signal and activate in response to receiving the alert signal. The warning indicator can be positioned on the base station or on a mobile device located remote from the base station.

Abstract: An agricultural applicator, that applies material to an agricultural field, includes an on-board, real-time image sensor that senses targets for the material to be applied.

Abstract: An agricultural applicator, that applies material to an agricultural field, includes an on-board, real-time image sensor that senses targets for the material to be applied.

Abstract: An agricultural machine includes a product application system comprising a plurality of actuatable applicator mechanisms, each actuatable applicator mechanism configured to apply a substance on an agricultural surface, an imaging system configured to obtain image data indicative of an image of plant matter on the agricultural surface, and a control system configured to determine a characteristic of the plant matter in an image region defined within the image, control one or more of the actuatable applicator mechanisms to apply the substance on the agricultural surface based on the determined characteristic, and generate a user interface display that displays the image and includes a display element that visually represents the image region within the image.

Abstract: A computing system includes image receiving logic configured to receive image data indicative of an image of a field, ground identification logic configured to identify a first image portion of the image representing ground in the field, image segmentation logic configured to identify a remaining image portion that omits the first image portion from the image, and crop classification logic configured to apply a crop classifier to the remaining image portion and identify a second image portion of the image that represents locations of crop plants in the field. The computing system also includes weed identification logic configured to identify locations of weed plants in the field based on the identification of the first and second image portions and control signal generation logic configured to generate a machine control signal based on the identified locations of the weed plants.

Abstract: A vehicle grade control system and method of controlling an implement position of a motor grader moving along a path of a surface. The motor grader includes a frame supported by wheels and an implement adjustably coupled to the frame. The control system includes a processor and a memory configured to receive a grade target to grade the surface to a desired grade with the implement based on the grade target. Surface irregularities of the surface in a path of the motor grader are located. An angle of the frame is determined based on the located surface irregularities and a difference between the identified angle of the frame and the grade target is determined. A position of the implement with respect to the frame based on the determined difference is identified and the surface is graded with the identified position of the implement.

Abstract: A vehicle grade control system and method of controlling an implement position of a motor grader moving along a path of a surface. The motor grader includes a frame supported by a ground engaging traction device and an implement adjustably coupled to the frame. The control system includes a processor and a memory configured to receive a grade target to grade the surface to a desired grade with the implement, based on the grade target. A front image sensor provides images of a front surface profile, an implement image sensor provides images of collected surface material on the implement, and a rear image sensor provides images on a rear surface profile. The surface is graded by adjusting a position of the implement based on the images provided by each of the front image sensor, the implement image sensor, and the rear image sensor.

Abstract: Methods and apparatus to track a blade are disclosed. A disclosed example apparatus includes a blade that is pivotable, first and second optical targets operatively coupled to the blade, and a first camera for collecting first imaging data of the first optical target within a first field of view. The example apparatus also includes a second camera for collecting second imaging data of the second optical target within a second field of view. The example apparatus also includes a selector to select at least one of the first or second imaging data, and a processor to determine an orientation of the blade based on the selected imaging data.

Abstract: A mobile work machine includes a frame and a ground engaging element movably supported by the frame and driven by an engine to drive movement of the mobile work machine. The mobile work machine includes a container movably supported by the frame, the container configured to receive contents and an actuator configured to controllably drive movement of the container relative to the frame. The mobile work machine includes a control system configured to generate an actuator control signal, indicative of a commanded movement of the actuator, and provide the actuator control signal to the actuator to control the actuator to perform the commanded movement and a content density determination system, communicatively coupled to the control system, configured to determine a density of the contents of the container.

Abstract: An agricultural sprayer includes a spray boom supported by a frame, a spraying system comprising a set of spray nozzles spaced along the spray boom, and a control system configured to control the spraying system to spray a liquid based on a target application to an agricultural field, generate a spray performance metric indicative of performance of the spraying system relative to the target application, and generate a control signal to control the agricultural sprayer based on the spray performance metric.

Abstract: A computing system includes image receiving logic configured to receive image data indicative of an image of a field, ground identification logic configured to identify a first image portion of the image representing ground in the field, image segmentation logic configured to identify a remaining image portion that omits the first image portion from the image, and crop classification logic configured to apply a crop classifier to the remaining image portion and identify a second image portion of the image that represents locations of crop plants in the field. The computing system also includes weed identification logic configured to identify locations of weed plants in the field based on the identification of the first and second image portions and control signal generation logic configured to generate a machine control signal based on the identified locations of the weed plants.