Abstract: For each of a plurality of spray nozzles of an autonomous agricultural vehicle, a set of instructions provided by the autonomous agricultural vehicle to the spray nozzle is accessed. Each instruction in the set is generated by analyzing a respective image of a portion of a geographic area captured by the autonomous agricultural vehicle. A spray performance of at least one spray nozzle from among the plurality of spray nozzles is identified as an outlier by analyzing the sets of instructions respectively provided to the plurality of spray nozzles. An action with respect to the at least one spray nozzle identified as the outlier is performed. The action may be to place the vehicle in fallback state in which a targeted spray may be switched to broadcast spray.

Abstract: An agricultural material application machine includes a material reservoir, a controllable valve, a pump that pumps material from the material reservoir to the controllable valve, an image sensor that captures in image of a portion of the worksite ahead of the controllable valve, and a light sensor that detects ambient lighting conditions at the worksite and generates sensor data indicative of the ambient lighting conditions. The machine further includes one or more processors and memory storing instructions executable by the one or more processors. The instructions, when executed by the one or more processors, cause the one or more processors to adjust the image based on the sensor data indicative of the ambient lighting conditions to generate an adjusted image of the portion of the worksite ahead of the controllable valve and to control the controllable valve based on the adjusted image.

Abstract: An automated farming machine can operate in low light environments (e.g., at night) using one or more lights, attached to a boom, to illuminate the ground surface of a field for cameras of the machine. The cameras capture images of the field, which the machine processes by identifying a region of interest (ROI) within the image and a plant within the ROI. The boom moves as the machine goes over uneven ground, causing the brightness captured in the images to change. The machine may analyze a portion of the ground surface that is illuminated to identify an ROI within the image. The machine identifies the ROI by comparing the brightness of the image to a threshold brightness level. The machine takes actions based on the ROI, such as spraying herbicide on a weed identified in the ROI at a certain time after identifying the weed.

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: A camera is mounted on the front of a sprayer. Targets are identified from images generated by the camera. The location of the target is identified relative to a nozzle on the sprayer. A control signal is generated to control the timing and duration of actuation of the nozzle.

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 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.