From one field to another—Unsupervised domain adaptation for semantic segmentation in agricultural robotics

Abstract

In traditional arable crop fields, tractors treat the whole field uniformly applying large quantities of herbicides and pesticides for weed control and plant protection. Autonomous robots, instead, offer the potential to provide a per-plant treatment, thus turning weed control and plant protection environment-friendly. To this end, an autonomous robot has to reliably distinguish crops, weeds, and soil under a diverse range of environmental conditions using its onboard sensors. Such recognition ability forms the basis for targeted plant-specific treatments in the form of spot applications. Basically, all such perception systems used today rely on some form of machine learning technique. However, current learning-based solutions often show a performance decay when applied under new field conditions. This is a major bottleneck for real-world application and finally commercial adoption. In this paper, we propose a simple yet effective approach to unsupervised domain adaptation for semantic segmentation systems so that an existing segmentation pipeline can be adapted to different fields, different robots, and different crops. Our system yields a high segmentation performance in new target fields without the need for extra manual annotations. It exploits only annotations from the source domain, i.e., the original field used for training the robot’s vision system. Our thorough evaluation shows that our approach achieves high accuracy when transferring an existing segmentation system to different environmental conditions, different plant species, and different robotic systems.