Unsupervised Domain Adaptation for Transferring Plant Classification Systems to New Field Environments, Crops, and Robots.

Crops are an important source of food and other products. In conventional farming, tractors apply large amounts of agrochemicals uniformly across fields for weed control and plant protection. Autonomous farming robots have the potential to provide environment-friendly weed control on a per plant basis. A system that reliably distinguishes crops, weeds, and soil under varying environment conditions is the basis for plant-specific interventions such as spot applications. Such semantic segmentation systems, however, often show a performance decay when applied under new field conditions. In this paper, we therefore propose an effective approach to unsupervised domain adaptation for plant segmentation systems in agriculture and thus to adapt existing systems to new environments, different value crops, and other farm robots. Our system yields a high segmentation performance in the target domain by exploiting labels only from the source domain. It is based on CycleGANs and enforces a semantic consistency domain transfer by constraining the images to be pixel-wise classified in the same way before and after translation. We perform an extensive evaluation, which indicates that we can substantially improve the transfer of our semantic segmentation system to new field environments, different crops, and different sensors or robots.