SPM-IS: An auto-algorithm to acquire a mature soybean phenotype based on instance segmentation

• Published in: The Crop journal Vol. 10; no. 5; pp. 1412 - 1423
• Main Authors: Li, Shuai, Yan, Zhuangzhuang, Guo, Yixin, Su, Xiaoyan, Cao, Yangyang, Jiang, Bofeng, Yang, Fei, Zhang, Zhanguo, Xin, Dawei, Chen, Qingshan, Zhu, Rongsheng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2022; KeAi Communications Co., Ltd
• Subjects: Deep learning; Feature pyramid network; Instance segmentation; PCA; Soybean; Instance segmentation; Deep learning; Soybean; Feature pyramid network; PCA
• ISSN: 2214-5141; 2214-5141
• DOI: 10.1016/j.cj.2021.05.014

Mature soybean phenotyping is an important process in soybean breeding; however, the manual process is time-consuming and labor-intensive. Therefore, a novel approach that is rapid, accurate and highly precise is required to obtain the phenotypic data of soybean stems, pods and seeds. In this research, we propose a mature soybean phenotype measurement algorithm called Soybean Phenotype Measure-instance Segmentation (SPM-IS). SPM-IS is based on a feature pyramid network, Principal Component Analysis (PCA) and instance segmentation. We also propose a new method that uses PCA to locate and measure the length and width of a target object via image instance segmentation. After 60,000 iterations, the maximum mean Average Precision (mAP) of the mask and box was able to reach 95.7%. The correlation coefficients R2 of the manual measurement and SPM-IS measurement of the pod length, pod width, stem length, complete main stem length, seed length and seed width were 0.9755, 0.9872, 0.9692, 0.9803, 0.9656, and 0.9716, respectively. The correlation coefficients R2 of the manual counting and SPM-IS counting of pods, stems and seeds were 0.9733, 0.9872, and 0.9851, respectively. The above results show that SPM-IS is a robust measurement and counting algorithm that can reduce labor intensity, improve efficiency and speed up the soybean breeding process.