A Vision-Based Navigation System for Arable Fields

TL;DR

This paper introduces a deep learning-based vision system for navigating arable fields, addressing challenges like weed density and illumination, with a comprehensive dataset and field testing demonstrating effective crop row detection and navigation accuracy.

Contribution

It presents a novel suite of perception algorithms and a comprehensive dataset for robust field navigation, advancing the development of generalised vision-based agricultural robots.

Findings

Achieved average heading error of 1.24°

Cross-track error of 3.32 cm in field tests

Successfully navigated 4.5 km in commercial fields

Abstract

Vision-based navigation systems in arable fields are an underexplored area in agricultural robot navigation. Vision systems deployed in arable fields face challenges such as fluctuating weed density, varying illumination levels, growth stages and crop row irregularities. Current solutions are often crop-specific and aimed to address limited individual conditions such as illumination or weed density. Moreover, the scarcity of comprehensive datasets hinders the development of generalised machine learning systems for navigating these fields. This paper proposes a suite of deep learning-based perception algorithms using affordable vision sensors for vision-based navigation in arable fields. Initially, a comprehensive dataset that captures the intricacies of multiple crop seasons, various crop types, and a range of field variations was compiled. Next, this study delves into the creation of robust infield perception algorithms capable of accurately detecting crop rows under diverse conditions such as different growth stages, weed density, and varying illumination. Further, it investigates the integration of crop row following with vision-based crop row switching for efficient field-scale navigation. The proposed infield navigation system was tested in commercial arable fields traversing a total distance of 4.5 km with average heading and cross-track errors of 1.24{\deg} and 3.32 cm respectively.