Briefly, an advanced data collection and processing system is provided to collect multiple types of data from farm terrain to drive a farm management processes, including a crop yield simulation tool. The system has disparate sensors mounted on a vehicle, such as a ground vehicle or airplane, which collects data from GPS, RADAR, camera, thermal, LiDAR and spectral scanners for an area of interest on a farm terrain. The system also collects data from public sources and the farm manager, which enable the simulation tool to accurately predict crop growth and maturity.

Methods, apparatus, systems and articles of manufacture are disclosed for field operations based on historical field operation data. An example apparatus disclosed herein includes a guidance line generator to generate a guidance line for operation of a vehicle during a second operation on a field, the guidance line based on (1) a field map generated from location data collected during a first operation in the field, the field map including a plurality of crop rows and (2) an implement of the vehicle, the implement to perform the second operation on the field. The example apparatus further includes a drive commander to cause the vehicle to traverse the field along the guidance line, and an implement commander to cause the implement to perform the second operation as the vehicle traverses the field along the guidance line.

A vehicle attachment carrier loading guidance system may include a sensor for being supported by vehicle supporting a removable attachment having a mount interface for interacting with a mount on an attachment carrier distinct from the vehicle, wherein the sensor is output steering angle signals. The system may further comprise a controller to output presentation signals based upon the steering angle signals. The presentation signals are to generate a visual representation of a projected path of the mount interface with respect to the mount.

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.

A system having a boom; a plurality of cameras attached to the boom; and a plurality of lenses on each camera; wherein not all lenses have a same field of view.

An autonomous work machine that includes a camera configured to capture an image of an external world, comprises a determination unit configured to determine, based on an image captured by the camera, whether backlighting has occurred, and a control unit configured to control, in a case in which the determination unit has determined that the backlighting has occurred, the autonomous work machine so as to avoid the backlighting.

Systems and methods for agricultural lane following are described. For example, a method includes accessing range data captured using a distance sensor connected to a vehicle and/or image data captured using an image sensor connected to a vehicle; detecting a crop row based on the range data and/or the image data to obtain position data for the crop row; determining, based on the position data for the crop row, a yaw and a lateral position of the vehicle with respect to a lane bounded by the crop row; and based on the yaw and the lateral position, controlling the vehicle to move along a length of the lane bounded by the crop row.

A mobile machine movable between multiple rows of trees includes one or more sensors to output sensor data indicating a distribution of objects in a surrounding environment of the mobile machine, a storage to store environment map data indicating a distribution of trunks of the multiple rows of trees, a localization processor, and a controller to control movement of the mobile machine in accordance with a position of the mobile machine estimated by the localization processor. The localization processor is configured or programmed detect the trunks of the rows of trees in the surrounding environment of the mobile machine based on the sensor data that is repeatedly output from the one or more sensors while the mobile machine is moving, and perform matching between the detected trunks of the rows of trees and the environment map data to estimate a position of the mobile machine.

A method for verifying regenerative management practices in agricultural parcels includes: determining a regenerative carbon footprint value for a parcel that comprises a difference of a regenerative carbon footprint and a baseline carbon footprint, where the baseline carbon footprint is derived by calculating greenhouse gas emissions based on simulating crops under current management practices, and where the regenerative carbon footprint is derived by calculating greenhouse gas emissions based on simulating crops under regenerative management practices corresponding to a plan proposed by a grower; approving and publishing carbon credits according to the plan; for key dates corresponding to each of the regenerative management practices, processing remotely sensed images against corresponding crop curves to determine compliance/noncompliance indicators corresponding to the key dates; and storing the compliance/noncompliance indicators database, and determining at a verification date compliance with the plan.

To provide an automatic traveling device capable of automatically traveling along a desired route without performing positioning.

An automatic traveling device 100 is a device that performs predetermined work on crops S which are detection targets arranged in its traveling direction. A camera 101 acquires image data by capturing an image of the crops S in the traveling direction of the automatic traveling device 100. The recognition unit 102 disposes a plurality of determination windows (such as determination windows 1L and 1R) at positions determined in advance with respect to the image data, and recognizes the crops S in the plurality of determination windows 1R and the like. The adjustment unit 104 adjusts the traveling direction of the automatic traveling device 100 on the basis of a result of recognition performed by the recognition unit 102.