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Automatic plant phenotyping analysis of Melon (Cucumis melo L.) germplasm resources using deep learning methods and computer vision

文献类型： 外文期刊

作者： Xu, Shan 1 ; Shen, Jia 2 ; Wei, Yuzhen 3 ; Li, Yu 4 ; He, Yong 1 ; Hu, Hui 5 ; Feng, Xuping 1 ;

作者机构： 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Peoples R China

2.Zhejiang Acad Agr Sci, Inst Vegetables, Hangzhou 310021, Peoples R China

3.Huzhou Univ, Sch Informat Engn, Huzhou 313000, Peoples R China

4.Zhejiang Univ, Agr Expt Stn & Agr Scitech Pk Management Comm, Hangzhou 310058, Peoples R China

5.Sichuan Yuheyuan Agr Technol Co Ltd, Chengdu 610066, Peoples R China

关键词： Plant phenotyping; Machine learning; Deep learning; Computer vision

期刊名称：PLANT METHODS （ 影响因子：4.4； 五年影响因子：5.7 ）

ISSN：

年卷期： 2024 年 20 卷 1 期

页码：

收录情况： SCI

摘要： Cucumis melo L., commonly known as melon, is a crucial horticultural crop. The selection and breeding of superior melon germplasm resources play a pivotal role in enhancing its marketability. However, current methods for melon appearance phenotypic analysis rely primarily on expert judgment and intricate manual measurements, which are not only inefficient but also costly. Therefore, to expedite the breeding process of melon, we analyzed the images of 117 melon varieties from two annual years utilizing artificial intelligence (AI) technology. By integrating the semantic segmentation model Dual Attention Network (DANet), the object detection model RTMDet, the keypoint detection model RTMPose, and the Mobile-Friendly Segment Anything Model (MobileSAM), a deep learning algorithm framework was constructed, capable of efficiently and accurately segmenting melon fruit and pedicel. On this basis, a series of feature extraction algorithms were designed, successfully obtaining 11 phenotypic traits of melon. Linear fitting verification results of selected traits demonstrated a high correlation between the algorithm-predicted values and manually measured true values, thereby validating the feasibility and accuracy of the algorithm. Moreover, cluster analysis using all traits revealed a high consistency between the classification results and genotypes. Finally, a user-friendly software was developed to achieve rapid and automatic acquisition of melon phenotypes, providing an efficient and robust tool for melon breeding, as well as facilitating in-depth research into the correlation between melon genotypes and phenotypes.

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