Computer Vision in Self-Steering Tractors

Abstract

Automatic navigation of agricultural machinery is an important aspect of Smart Farming. Intelligent agricultural machinery applications increasingly rely on machine vision algorithms to guarantee enhanced in-field navigation accuracy by precisely locating the crop lines and mapping the navigation routes of vehicles in real-time. This work presents an overview of vision-based tractor systems. More specifically, this work deals with (1) the system architecture, (2) the safety of usage, (3) the most commonly faced navigation errors, (4) the navigation control system of tractors and presents (5) state-of-the-art image processing algorithms for in-field navigation route mapping. In recent research, stereovision systems emerge as superior to monocular systems for real-time in-field navigation, demonstrating higher stability and control accuracy, especially in extensive crops such as cotton, sunflower, maize, etc. A detailed overview is provided for each topic with illustrative examples that focus on specific agricultural applications. Several computer vision algorithms based on different optical sensors have been developed for autonomous navigation in structured or semi-structured environments, such as orchards, yet are affected by illumination variations. The usage of multispectral imaging can overcome the encountered limitations of noise in images and successfully extract navigation paths in orchards by using a combination of the trees’ foliage with the background of the sky. Concisely, this work reviews the current status of self-steering agricultural vehicles and presents all basic guidelines for adapting computer vision in autonomous in-field navigation.

Row Detection BASED Navigation and Guidance for Agricultural Robots and Autonomous Vehicles in Row-Crop Fields: Methods and Applications

“…A binocular camera comprises two monocular cameras and is commonly used as a passive rangefinder. By capturing two images of an object from different positions, binocular cameras can determine the 3D geometric information of the object by calculating the position deviation between the corresponding points of the images [28]. This process is based on the principle of parallax, and it enables the camera to accurately measure the distance to the object, providing important information for crop row detection [29].…”

Design and development of orchard autonomous navigation spray system

“…In the orchard, the loss of signal caused by canopy occlusion, multipath effect, radio frequency interference, etc., results in great errors to GNSS navigation and even led to invalid navigation Binocular vision (Stefas et al, 2016;Lin et al, 2021;Ma et al, 2021;Vrochidou et al, 2022) Low cost and abundant information (depth map and RGB map)…”

Precision Agriculture Technologies for Crop and Livestock Production in the Czech Republic

“…In 1987 a dynamic thresholding technique helped to extract information from field images. After a short time, a vision guidance system to steer a tractor relative to crop rows was used so that the tractor could automatically acquire its track in the next row [40]. The leading technologies used in precision agriculture are described below.…”

“…One of the ways to use the images captured by CCD cameras is a specification of pig parameters (weight, circumference, height and other body information). Pig identification could be as follows [40,56,57]: facial recognition, live weight detection, growth patterns and mass calculation, and individual pig identification and tracking.…”