Enhancing Plant and Disease Segmentation through Semi-Supervised Learning with Feature Distillation

This paper proposes a network for plant and disease segmentation through semi-supervised learning in order to enhance agricultural production. Because of the hardness to get precisely labeled data, we use unlabeled data with pseudo labels. Furthermore, we employ a teacher-student network framework, where the teacher network imparts knowledge from labeled data to the student network. This boosts the segmentation precision of the student network, which is then exclusively trained on unlabeled data. We introduce a novel Exponential Moving Average (EMA) technique for teacher parameter updates, enhancing segmentation accuracy. Experimental results show that better segment performance can be achieved with the proposed network.