Embodiments of the present disclosure relate generally to generating and utilizing three-dimensional terrain maps for vehicular control. Other embodiments may be described and/or claimed.

Provided is a working vehicle capable of operation and driving that includes a plurality of image capture devices 4 that capture an image of surroundings of a vehicle body; a direction detection unit 51 that detects an advancement direction of the operation and driving; an image selection unit 52 that acquires, as a detection image, a surrounding area image captured by one image capture device 4 of the plurality of image capture devices 4 capturing a front advancement direction detected by the direction detection unit 51; and a detection unit 53 that analyzes the detection image acquired by the image selection unit 52 and detects an obstacle.

An illustrative modular smart implement for precision agriculture includes a chassis having a hydraulic system, a control system, and articulating tool arms that are adapted to releasably receive one of a tool attachment for working a crop and/or field, including precision planting, cultivating, thinning, spraying, harvesting, and/or data collection. A toolbar fixed to the chassis receives and supports the articulating tools arms. An alignment member and side shift actuator provide movement of a portion of the tool arms along an axis parallel to a longitudinal axis of the toolbar, and a lift actuator provide movement along a vertical axis.

A method for operating an agricultural vehicle. The method including capturing, by the at least one image capturing device, image data of the implement and receive, by the controller, the image data of the implement, The method further includes identifying the implement, by the controller, depending at least in part upon the image data of the implement. The method further includes setting, by the controller, at least one operational parameter of the implement, and managing, by the controller, a turning maneuver of the agricultural vehicle depending at least in part upon the image data of the implement.

In accordance with an example embodiment, an agricultural work vehicle includes a propulsion system for modifying vehicle speed, a harvesting system for harvesting crop, a perception system having at least one camera with a field of view of a forward zone in front of the vehicle, and a controller. The controller communicates with the propulsion, harvesting, and perception systems, and is configured to receive the perception signal, identify a harvest condition in the forward zone, adjust an operation setting of the propulsion system or harvesting system, and generate a forward-looking perception interface for a display screen. The forward-looking perception interface includes a forward zone area displaying the field of view and a status area displaying at least one harvest condition identified. The status area also displays a time and a sensitivity level associated with the at least one harvest condition identified.

A detection system detects malfunctions in an autonomous farming vehicle during an autonomous routine using one or more models and data from sensors coupled to the autonomous farming vehicle. The models may include machine-learned models trained on the sensor data and configured to identify objects indicative of an operational or malfunctioning component within a tilling assembly such as a tilling shank or sweep. Additionally, a machine-learned model may be trained on sensor data to detect whether debris has plugged the tilling assembly of the autonomous farming vehicle. In response to detecting a malfunction or a plug, the detection system may modify the autonomous routine (e.g., pausing operation) or provide information for the malfunction to be addressed (e.g., the likely location of a malfunctioning sweep that has detached from the tilling assembly).

Systems and methods for vehicle controllers for agricultural and industrial applications are described. For example, a method includes accessing a map data structure storing a map representing locations of physical objects in a geographic area; accessing current point cloud data captured using a distance sensor connected to a vehicle; detecting a crop row based on the current point cloud data; matching the detected crop row with a crop row represented in the map; determining an estimate of a current location of the vehicle based on a current position in relation to the detected crop row; and controlling one or more actuators to cause the vehicle to move from the current location of the vehicle to a target location.

A method includes obtaining, by the treatment system configured to implement a machine learning (ML) algorithm, one or more images of a region of an agricultural environment near the treatment system, wherein the one or more images are captured from the region of a real-world where agricultural target objects are expected to be present, determining one or more parameters for use with the ML algorithm, wherein at least one of the one or more parameters is based on one or more ML models related to identification of an agricultural object, determining a real-world target in the one or more images using the ML algorithm, wherein the ML algorithm is at least partly implemented using the one or more processors of the treatment system, and applying a treatment to the target by selectively activating the treatment mechanism based on a result of the determining the target.

In one aspect, a system for determining soil clod size as an implement is being towed across a field by a work vehicle may include an imaging device provided in operative association with the work vehicle or the implement such that the imaging device is configured to capture images of the field. Furthermore, the system may include a controller communicatively coupled to the imaging device. The controller may be configured to receive, from the imaging device, image data associated with an imaged portion of the field. Moreover, the controller may be configured analyze the received image data to identify at least one edge of a soil clod within the imaged portion of the field. Additionally, the controller may be configured to determine a size of the soil clod based on the identified at least one edge of the soil clod.

The invention relates to a collaborative agricultural field processing control device intended to be mounted on an agricultural machine (1), composed of a set of detectors (2) for weeds or leaf symptoms of deficiencies or diseases collaborating in the decision to control the treatment devices (3) of the agricultural field.