A mowing vehicle 1 provided with a traveling machine 10 and a mowing device 20 includes a first image-capturing device 30 and a controlling unit C configured to control the traveling machine 10 to travel autonomously along a boundary line of grass before and after mowing formed by the mowing device 20. The controlling unit C includes a boundary-detecting unit C2 configured to detect the boundary line and a traveling-controlling unit C3 configured to control traveling directions of the traveling machine 10. The boundary-detecting unit C2 is configured to generate intensity distribution information regarding texture information in a predetermined direction by filtering with a Gabor filter on a captured image. The boundary-detecting unit C2 is configured to carry out statistical processing on the intensity distribution information per inspection area divided in plural in a vertical direction so as to detect boundary points and to detect the boundary line from the boundary points per the inspection area.

An illustrative control system for an precision agricultural implement includes a controller having a convolutional neural network, a machine vision module, a plurality of sensors, and a plurality of actuators in communication with the controller, the plurality of actuators including a plurality of agricultural tool actuators.

Apparatuses, systems, and techniques to generate one or more confidence values associated with one or more objects identified by one or more neural networks. In at least one embodiment, one or more confidence values associated with one or more objects identified by one or more neural networks are generated based on, for example, one or more neural network outputs.

An automated work system includes a control apparatus capable of controlling an automated work machine that performs work in a work area. The automated work system comprises a generation unit configured to generate a schedule for the work. The generation unit generates the schedule based on a scheduled work time during which a user arrives at the work area and performs work in the work area or a time slot during which the user is not in the work area.

An obstacle detection system for an agricultural work vehicle includes: an obstacle estimation unit 52 that estimates a region in a field based on a detection signal from an obstacle sensor 21 in which region an obstacle is present, and outputs obstacle present region information; an image capturing unit 22 that outputs a captured image of the field; an obstacle detection unit 54 that detects the obstacle from an input image and outputs obstacle detection information; and an image preprocessing unit 53 that generates, as an image to be input to the obstacle detection unit 54, a trimmed image obtained by trimming the captured image so as to include the region in which the obstacle is present, based on the obstacle present region information and shooting-angle-of-view information regarding the image capturing unit 22.

An agricultural tractor with a system for identifying downstream road users includes a control unit in communication with a user interface, and a sensor having a detection range which runs in a vicinity of a ground and which is in a direction of an attachment connected to the agricultural tractor. The sensor is configured to identify a vehicle located in the detection range or which enters therein, and then generates a corresponding message signal which is supplied to the control unit. The control unit activates the user interface based on the message signal for the output of a driver message. A free region visible from the agricultural tractor by the sensor is formed between a road surface and a lower contour of the attachment facing the road surface.

An intelligent pesticide spraying robot self-adaptive to terrain of mountain land is provided. Walking mechanisms are respectively connected to both side ends of a chassis. A pesticide spraying mechanism includes rotary drive mechanisms respectively connected to both sides of the rear end of the chassis, telescopic spray boom mechanisms correspondingly connected to the rotary drive mechanisms, rotary nozzle mechanisms correspondingly connected to the telescopic spray boom mechanisms, a liquid pesticide storage box fixed to the top of the chassis, a first water pump fixed in the liquid pesticide storage box, and first water pipes in communication with the first water pump and the rotary nozzle mechanisms. A control chip of a control identification mechanism is communicatively connected to the walking mechanisms, the rotary drive mechanisms, the telescopic spray boom mechanisms, the rotary nozzle mechanisms, the first water pump, a front-end camera, side-end cameras and ultrasonic sensors respectively.

A debris accumulation control system is provided for usage within a work vehicle including an operator station and a work vehicle compartment. In embodiments, the work vehicle debris accumulation control system includes a display device located in the operator station of the work vehicle, a three dimensional (3D) imaging device having a field of view (FOV) encompassing a debris-gathering region of the work vehicle compartment, and a controller operably coupled to the display device and to the 3D imaging device. The controller is configured to: (i) utilize 3D imaging data provided by the 3D imaging device to estimate a debris accumulation risk level within the work vehicle compartment; and (ii) generate a first visual alert on the display device when the debris accumulation risk level surpasses a first predetermined threshold.

A system for identifying objects present within a field across which an agricultural vehicle is traveling includes a transceiver-based sensor configured to capture point cloud data associated with a portion of the field present within a field of view of the transceiver-based sensor as the agricultural vehicle travels across the field. Additionally, the system includes a display device and a controller communicatively coupled to the transceiver-based sensor and the display device. The controller, in turn, is configured to analyze the captured point cloud data to create a sparse point cloud identifying at least one of a crop row or a soil ridge located within the portion of the field present within the field of view of the transceiver-based sensor. Furthermore, the controller is configured to initiate display of an image associated with the sparse point cloud on the display device.

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.