Unsupervised domain adaptation with self-training for weed segmentation

Yingchao Huang, Amina E. Hussein, Xin Wang, Abdul Bais, Shanshan Yao, Tanis Wilder

Abstract: Highlights•We introduced a self-training framework for weed segmentation.•Four novel strategies were developed to enhance the performance.•The method was evaluated on datasets with varying domain gaps.•It is demonstrated generalization across different robots and growth stages.•The experimental code is publicly available for future research.