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

作     者：Shuai Li Zhuangzhuang Yan Yixin Guo Xiaoyan Su Yangyang Cao Bofeng Jiang Fei Yang Zhanguo Zhang Dawei Xin Qingshan Chen Rongsheng Zhu Shuai Li;Zhuangzhuang Yan;Yixin Guo;Xiaoyan Su;Yangyang Cao;Bofeng Jiang;Fei Yang;Zhanguo Zhang;Dawei Xin;Qingshan Chen;Rongsheng Zhu

作者机构：College of EngineeringNortheast Agricultural UniversityHarbin 150030HeilongjiangChina College of Arts and SciencesNortheast Agricultural UniversityHarbin 150030HeilongjiangChina College of AgricultureNortheast Agricultural UniversityHarbin 150030HeilongjiangChina 

出 版 物：《The Crop Journal》 (作物学报（英文版）)

年 卷 期：2022年第10卷第5期

页      面：1412-1423页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 09[农学] 0901[农学-作物学] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China (31400074, 31471516, 31271747, and 30971809) the Natural Science Foundation of Heilongjiang Province of China(ZD201213) the Heilongjiang Postdoctoral Science Foundation(LBH-Q18025) 

主　　题：Soybean Feature pyramid network PCA Instance segmentation Deep learning 

摘      要：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(m AP) of the mask and box was able to reach 95.7%. The correlation coefficients R^(2) 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 R^(2) 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.