Semi-supervised Learning for Weed and Crop Segmentation Using UAV Imagery - Nong, Chunshi; Fan, Xijian; Wang, Junling (Frontiers in Plant Science, 2022)
Weed control has received great attention due to its significant influence on crop yield and food production. Accurate mapping of crop and weed is a prerequisite for the development of an automatic weed management system. In this paper, we propose a weed and crop segmentation method, SemiWeedNet, to accurately identify the weed with varying size in complex environment, where semi-supervised learning is employed to reduce the requirement of a large amount of labelled data. SemiWeedNet takes the labelled and unlabelled images into account when generating a unified semi-supervised architecture based on semantic segmentation model. A multi-scale enhancement module is created by integrating the encoded feature with the selective kernel attention, to highlight the significant features of the weed and crop while alleviating the influence of complex background. To address the problem caused by the similarity and overlapping between crop and weed, an online hard example mining (OHEM) is introduced to refine the labelled data training. This forces the model to focus more on pixels that are not easily distinguished, and thus effectively improve the image segmentation. To further exploit the meaningful information of unlabelled data, consistency regularization is introduced by maintaining the context consistency during training, making the representations robust to the varying environment. Comparative experiments conducted on a publicly available dataset shows that SemiWeedNet outperforms state-of-the-art methods. and its main components have promising potential in improving segmentation accuracy.