Research on Automatic Detection Methods for Soybean Stem Related Phenotypes

CHEN Jiajun; LIU Zhiyu; ZHOU Wan; LI Yang; ZHAN Wei; HUANG Lan; WANG Jun; QIU Lijuan

doi:10.15933/j.cnki.1004-3268.2024.10.018

Abstract：Targeting at the problem of automatic acquisition of stem node number and stem thickness phenotypes in soybean plants,this study decomposed the task into several sub-problems including stem node identification,stem region detection,stem node localization and sorting,and stem thickness calculation,and proposed a method of automatic detection of stem-related phenotypes that integrated target detection,semantic segmentation,and path planning.Based on A* path planning algorithm and target detection algorithm,the automatic detection and count of stem node were realized.Based on analogic method,curvature method and the maximum tangent circle method,automatic measurement and calculation of stem diameter were realized.By incoporating the coordinate attention mechanism into the YOLOv5s baseline model,the model achieved an average detection accuracy of 93.2%for stem node identification,which was also optimized by fusing a filtering algorithm incorporating the stem node position information to address the adverse effect of pod overlapping in dense plants at the maturity stage,which resulted in a 2.4 percentage point improvement when compared to the original YOLOv5s.At the same time,the mean intersection over union of U2-Net semantic segmentation network for segmentation of stems reached 82.6%after optimizing.Based on the stem regions after semantic segmentation,compared with the three stem diameter detection methods proposed in this paper,the maximum tangent circle method had the highest accuracy.Results on real plant samples showed that for the number of stem nodes and main stem diameter,the mean absolute errors of the optimized method were 1.33 and 0.99 mm respectively,which were basically consistent with the threshold requirement for manual measurement data.The root mean square errors were 1.74 and 1.20 respectively,and the mean average percentage errors were 8.96%and 16.37%respectively.