Autonomous driving method in the agricultural field by means of an thermal camera comprising the procedure of obtaining an interpolating function of at least two pixels, of alignments of plants or swaths of a thermal image that appears in front of an agricultural vehicle, acquired through at least one thermal camera, said at least two pixels being corresponding to at least two homologous peaks identified in as many at least two vectors built on values of temperature intensity of corresponding consecutive pixels belonging to as many straight and horizontal lines of pixels extracted from the thermal image and a procedure for calculating an angular phase shift and/or a lateral deviation of the interpolating function with respect to a vertical axis of the thermal image.

Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include, receiving data representing a policy specifying a type of action for an agricultural object, selecting an emitter with which to perform a type of action for the agricultural object as one of one or more classified subsets, and configuring the agricultural projectile delivery system to activate an emitter to propel an agricultural projectile to intercept the agricultural object.

An automated implement control system includes one or more distance sensors configured for coupling with an agricultural implement. The one or more distance sensors are configured to measure a ground distance and a canopy distance from the one or more sensors to the ground and crop canopy, respectively. An implement control module is in communication with the one or more distance sensors. The implement control module controls movement of the agricultural implement. The implement control module includes a confidence module configured to determine a ground confidence value based on the measured ground distance and a canopy confidence value based on the measured canopy distance. A target selection module of the implement control module is configured to select one of the measured ground or canopy distances as a control basis for controlling movement of the agricultural implement based on the comparison of confidence values.

Embodiments describe a method for positioning a hinged vehicle including a primary part and a secondary part coupled to the primary part at a project site. The method includes receiving, from an image capturing device, digital image data representing one or more features of the secondary part; performing image analysis on the digital image data to identify positions of the one or more features of the secondary part; identifying an angle of at least a portion of the secondary part; calculating a current position of the secondary part based on the angle; calculating a positional difference between a correct position at the project site for the secondary part and a current position of the secondary part at the project site; and initiating a change in a position of the primary part to compensate for the positional difference and to position the secondary part on the correct position.

A methods for soil clod detection within a field is provided herein and can include receiving, with a computing system, data indicative of terrain variations within a region of an agricultural field. The region of the field is comprised of one or more adjacently positioned segments. The method can also include generating, with the computing system, a mean reference line. The method can further include calculating, with the computing system, a segment height for each of the one or more adjacently positioned segments. The method can also include determining, with the computing system, a presence of an object based on a deviation of one of the one or more segment heights being greater than a threshold height from the reference line.

The disclosure relates generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include identifying a subset of payloads to provide one or more actions based on data representing a policy for one or more subsets of agricultural objects, causing one or more cartridges to be charged based on the subset of payloads, and, and implementing one or more cartridges at an agricultural projectile delivery system.

In one embodiment, an autonomous agricultural vehicle includes a control interface disposed in an enclosure of the autonomous agricultural vehicle and configured to at least setup or control operation of the autonomous agricultural vehicle, an implement attached to the autonomous agricultural vehicle, or a combination thereof. The autonomous agricultural vehicle further includes a removable panel at least partially removably coupled to the autonomous agricultural vehicle over the enclosure, wherein the removable panel is positioned to be accessible to an operator who is operating the autonomous agricultural vehicle outside of the autonomous agricultural vehicle.

A method and system for estimating surface roughness of a ground for an off-road vehicle to control ground speed comprises detecting motion data of an off-road vehicle traversing a field or work site during a sampling interval. A pitch sensor is adapted to detect pitch data of the off-road vehicle for the sampling interval to obtain a pitch acceleration. A roll sensor is adapted to detect roll data of the off-road vehicle for the sampling interval to obtain a roll acceleration. An electronic data processor or surface roughness index module determines or estimates a surface roughness index based on the detected motion data, pitch data and roll data for the sampling interval. The surface roughness index can be displayed on the graphical display to a user or operator of the vehicle, or applied to control or execute a ground speed setting of the vehicle.

An agricultural implement includes a sensor supported on the frame. The sensor, in turn, is configured to emit output signals for refection off of a field surface of a field and detect reflections of the output signals as return signals. Moreover, the agricultural implement includes a computing system communicatively coupled to the sensor. In this respect, the computing system configured to receive data associated with the detected reflections from the sensor and fit a line or plane to received data. In addition, the computing system is configured to determine at least one of an orientation of the frame or a distance between the frame and the field surface based on the fitted line or plane.

A method for the operation of a self-propelled agricultural working machine has at least one working element and a driver assistance system for generating control actions within the working machine. A sensor arrangement for generating surroundings information is provided, and the driver assistance system generates the control actions based on the surroundings information. The sensor arrangement comprises a camera-based sensor system and a laser-based sensor system, each of which generates sensor information regarding a predetermined, relevant surroundings area of the working machine. The sensor information of the camera-based sensor system is present as starting camera images. The starting camera images are segmented into image segments by an image processing system according to a segmentation rule, and the segmented camera images are combined by a sensor fusion module with the sensor information from the laser-based sensor system.