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

IF 4.7 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Shan Xu, Jia Shen, Yuzhen Wei, Yu Li, Yong He, Hui Hu, Xuping Feng
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引用次数: 0

Abstract

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.

利用深度学习方法和计算机视觉对甜瓜(Cucumis melo L.)种质资源进行自动植物表型分析。
Cucumis melo L.,俗称甜瓜,是一种重要的园艺作物。优良甜瓜种质资源的选育对提高甜瓜的市场竞争力起着至关重要的作用。然而,目前甜瓜外观表型分析的方法主要依赖专家判断和复杂的人工测量,不仅效率低下,而且成本高昂。因此,为了加快甜瓜的育种进程,我们利用人工智能(AI)技术分析了两个年度 117 个甜瓜品种的图像。通过整合语义分割模型 Dual Attention Network (DANet)、对象检测模型 RTMDet、关键点检测模型 RTMPose 和移动友好分割模型(Mobile-Friendly Segment Anything Model (MobileSAM)),我们构建了一个深度学习算法框架,能够高效、准确地分割甜瓜果实和瓜梗。在此基础上,设计了一系列特征提取算法,成功获得了甜瓜的 11 个表型性状。所选性状的线性拟合验证结果表明,算法预测值与人工测量的真实值之间具有很高的相关性,从而验证了算法的可行性和准确性。此外,利用所有性状进行的聚类分析显示,分类结果与基因型之间具有很高的一致性。最后,还开发了一种用户友好型软件,可快速自动获取甜瓜表型,为甜瓜育种提供了一种高效稳健的工具,并有助于深入研究甜瓜基因型与表型之间的相关性。
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来源期刊
Plant Methods
Plant Methods 生物-植物科学
CiteScore
9.20
自引率
3.90%
发文量
121
审稿时长
2 months
期刊介绍: Plant Methods is an open access, peer-reviewed, online journal for the plant research community that encompasses all aspects of technological innovation in the plant sciences. There is no doubt that we have entered an exciting new era in plant biology. The completion of the Arabidopsis genome sequence, and the rapid progress being made in other plant genomics projects are providing unparalleled opportunities for progress in all areas of plant science. Nevertheless, enormous challenges lie ahead if we are to understand the function of every gene in the genome, and how the individual parts work together to make the whole organism. Achieving these goals will require an unprecedented collaborative effort, combining high-throughput, system-wide technologies with more focused approaches that integrate traditional disciplines such as cell biology, biochemistry and molecular genetics. Technological innovation is probably the most important catalyst for progress in any scientific discipline. Plant Methods’ goal is to stimulate the development and adoption of new and improved techniques and research tools and, where appropriate, to promote consistency of methodologies for better integration of data from different laboratories.
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