Automatic segmentation of cotton roots in high-resolution minirhizotron images based on improved OCRNet - Huang, Yuxian; Yan, Jingkun; Zhang, Yuan; Ye, Weixin; Zhang, Chu; Gao, Pan; Lv, Xin (Frontiers in Plant Science, 2023)
Root phenotypic parameters are the important basis for studying the growth state of plants, and root researchers obtain root phenotypic parameters mainly by analyzing root images. With the development of image processing technology, automatic analysis of root phenotypic parameters has become possible. And the automatic segmentation of roots in images is the basis for the automatic analysis of root phenotypic parameters. We collected high-resolution images of cotton roots in a real soil environment using minirhizotrons. The background noise of the minirhizotron images is extremely complex and affects the accuracy of the automatic segmentation of the roots. In order to reduce the influence of the background noise, we proposed an improved OCRNet by adding a Global Attention Mechanism (GAM) module to OCRNet to enhance the focus of the model on the root targets. The improved OCRNet model proposed in this paper achieved automatic segmentation of roots in the soil and performed well in the root segmentation of the high-resolution minirhizotron images, achieving an Intersection over Union (IoU) of 84.26% and a Dice Similarity Coefficient (DCS) of 91.46%. The method provided a new approach to automatic and accurate root segmentation of high-resolution minirhizotron images taken in the real environment.