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 field map generator to generate a field map including locations of a plurality of crop rows, the locations of the plurality of crop rows determined based on a first implement path travelled by a first implement of a first vehicle during a first operation, the first implement having a first operational width, the first implement path different from a first vehicle path of the first vehicle during the first operation, and a guidance line generator to generate a guidance line for a second vehicle during a second operation on the field, the second vehicle including a second implement to perform the second operation, the second implement having a second operational width different from the first operational width, the guidance line based on (a) the field map and (b) the second operational width.

An agricultural implement broadly includes a ground-engaging tool, a time-of-flight sensor, and a controller. The time-of-flight sensor is configured to obtain information indicative of seed parameters, furrow parameters, and/or soil condition parameters. The controller is configured to process the information obtained by the time-of-flight sensor to generate the parameters, wherein the controller is further configured to automatically control operation of one or more components of the implement based on the parameters.

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 method for determining residue length within a field includes receiving, with a computing system, a captured image depicting an imaged portion of the field from one or more imaging devices. Furthermore, the method includes determining, with the computing system, an image gradient orientation at each of a plurality of pixels within the captured image. Additionally, the method includes identifying, with the computing system, a residue piece present within the image portion of the field based at least in part on the determined image gradient orientations. Moreover, the method includes determining, with the computing system, a length of the identified residue piece.

An illustrative control system for a precision agricultural implement includes a controller having a convolutional neural network, an imaging device, a plurality of sensors, and a plurality of actuators in communication with the controller, the controller configured for detecting and tracking objects of interest in a commodity field, such a commodity plants, and the plurality of actuators including a plurality of agricultural tool actuators the controller operates based on the detection and tracking of object of interest in the commodity field.

The present invention is provided to more reliably keep a work vehicle from coming into contact with an obstacle during automated driving. The work vehicle includes an electronic control system for automated driving that automatically drives the vehicle body. The electronic control system includes an obstacle detection module configured to detect presence or absence of an obstacle, and a contact avoidance control unit configured to perform, upon the obstacle detection module detecting an obstacle, contact avoidance control to keep the vehicle body from coming into contact with the obstacle. The obstacle detection module includes a plurality of obstacle searchers that are distributed on the front end portion and the right and left end portions of the vehicle body such that the front side and the right and left lateral sides of the vehicle body are search-target areas.

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.

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.

In an embodiment, autonomous vehicles with global positioning systems (GPS) are used for field inspection to reduce fuel and labor costs and improve reliability with increased consistency in field crop inspection. A vehicle may be programmed to traverse a field while using sensors to detect objects and operating in a first image capture mode, for example, capturing low-resolution images of objects in the field, typically crops. Under program control, machine vision techniques are used with the low-resolution images to recognize crops, non-crop plant material or undefined objects. Under program control, location data is used to correlate recognized objects with digitally stored field maps to resolve whether a particular object is in a location at which crop planting is expected or not expected. Under program control, depending on whether an object in a low-resolution digital image is recognized as a crop, and whether the object is in an expected geo-location for crops, the vehicle may cease traversing temporarily and switch to a second image capture mode, for example, capturing a high-resolution image of the object, for use in disease analysis or classification, weed analysis or classification, alert notifications or other messages, or other processing. In this manner, a field may be rapidly traversed and imaged using coarse-level, rapid techniques that require lower processing resources, storage or memory, while automatically switching to execute special processing only when necessary to resolve unexpected objects or to perform operations such as disease classification that benefit from high-resolution images and more intensive use of processing resources, storage or memory.

A system and method for automated grain inspection and analysis of results during harvest, using an inspection system mounted on a combine harvester with geolocation tracking, allowing for real time analysis during harvest and tracking of grain quality by location of harvest.