SCAG: A Stratified, Clustered, and Growing-Based Algorithm for Soybean Branch Angle Extraction and Ideal Plant Architecture Evaluation
Songyin Zhang, Yinmeng Song, Ran Ou, Yiqiang Liu, Shaochen Li, Xinlan Lu, Shan Xu, Yanjun Su, Dong Jiang, Yanfeng Ding, Haifeng Xia, Qinghua Guo, J. Wu, Jiaoping Zhang, Jiao Wang, Shichao Jin
Abstract
= 0.55). Moreover, after applying the SCAG to 405 soybean varieties over 2 consecutive years, we quantified various 3D traits, including canopy width, height, stem length, and average angle. After data filtering, we identified novel heritable and repeatable traits for evaluating soybean density tolerance potential, such as the ratio of average angle to height and the ratio of average angle to stem length, which showed greater potential than the well-known ratio of canopy width to height trait. Our work demonstrates remarkable advances in 3D phenotyping and plant architecture screening. The algorithm can be applied to other crops, such as maize and tomato. Our dataset, scripts, and software are public, which can further benefit the plant science community by enhancing plant architecture characterization and ideal variety selection.