[1]周欣兴,王好让,王晨宇,等.多源表型信息融合下大豆品种(系)鉴定与系谱分析[J].江苏农业学报,2025,(04):644-655.[doi:doi:10.3969/j.issn.1000-4440.2025.04.003]
　ZHOU Xinxing,WANG Haorang,WANG Chenyu,et al.Multi-source phenotypic information fusion for soybean variety （line） identification and pedigree analysis[J].,2025,(04):644-655.[doi:doi:10.3969/j.issn.1000-4440.2025.04.003]
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多源表型信息融合下大豆品种(系)鉴定与系谱分析()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2025年04期
页码:
644-655
栏目:
遗传育种·生理生化
出版日期:
2025-04-30

文章信息/Info

Title:
Multi-source phenotypic information fusion for soybean variety （line） identification and pedigree analysis
作者:
周欣兴1; 王好让1; 王晨宇2; 徐泽俊1; 李剑1; 王幸1; 刘亚菊1
(1.江苏徐淮地区徐州农业科学研究所,江苏徐州221131；2.农业农村部科技发展中心,北京100176)
Author(s):
ZHOU Xinxing1; WANG Haorang1; WANG Chenyu2; XU Zejun1; LI Jian1; WANG Xing1; LIU Yaju1
(1.Xuzhou Institute of Agricultural Sciences of the Xuhuai District of Jiangsu Province, Xuzhou 221131, China;2.Development Center of Science and Technology, Ministry of Agriculture and Rural Affairs, Beijing 100176, China)
关键词:
大豆; DUS测试; 性状; 品种（系）鉴定; 遥感; 图像处理
Keywords:
soybean; DUS testing; traits; variety (line) identification; remote sensing; image processing
分类号:
TP391；S565.1
DOI:
doi:10.3969/j.issn.1000-4440.2025.04.003
文献标志码:
A
摘要:
特异性、一致性和稳定性(DUS)是大豆新品种审定和品种权授权的必备条件,也是大豆种质评价和品种鉴定的重要依据。为验证引入多源表型数据进行大豆品种（系）精准鉴定和系谱分析的可能性,本研究利用田间调查、低空遥感和计算机视觉技术获取大豆的22个DUS性状、16个冠层性状和23个豆荚图像性状,并通过随机森林分类(RFC)模型和支持向量分类(SVC)模型进行大豆品种（系）鉴定,采用K均值(K-means)聚类算法探究品种(系)间的系谱关系。研究发现,低空遥感与计算机视觉技术获取的各性状间具有较高的相关性,DUS性状对于大豆品种(系)的准确识别至关重要,但也存在一些性状对品种（系）鉴定的贡献较弱。冠层光谱性状和豆荚图像的部分形态、颜色和纹理性状可以作为大豆品种（系）鉴定的优异候选性状。引入多源表型性状后模型实例化鉴定结果得到一定程度的改善,2个模型分类精度最终均能保持在0.9以上。聚类分析结果显示，聚类数在4个及以下时轮廓系数较好。本研究结果表明,利用多源表型信息创新品种测试技术具有巨大潜力,可为今后大豆DUS测试、品种（系）鉴定及系谱研究提供新的方法。
Abstract:
Distinctness, uniformity and stability (DUS) are prerequisites for a new soybean variety to obtain approval or plant variety rights, and they are also important bases for soybean germplasm evaluation and variety identification. To verify the possibility of introducing multi-source phenotypic data for accurate identification and pedigree analysis of soybean varieties (lines), this study utilized field investigation, low-altitude remote sensing, and computer vision technologies to obtain 22 DUS traits, 16 canopy traits and 23 pod image traits. The soybean varieties (lines) were identified by random forest classification (RFC) model and support vector classification (SVC) model. K-means clustering algorithm was used to explore the pedigree relationship among varieties (lines). We found that there were high correlations between the traits acquired by low-altitude remote sensing and computer vision technologies. DUS traits were crucial for the accurate identification of soybean varieties (lines), but there were also some traits that contributed less to the identification of varieties (lines). Canopy spectral traits and partial morphological color, and textural traits of pod images could serve as excellent candidate traits for the identification of soybean varieties (lines). After incorporating multi-source phenotypic traits, the model instantiation identification results were improved to a certain extent, and the accuracy of the two models was ultimately maintained above 0.9. The cluster analysis results showed that the silhouette coefficient demonstrated relatively good performance when the number of clusters was four or fewer. This study demonstrates that innovating variety testing technologies through multi-source phenotypic information holds significant potential, providing new methods for future soybean DUS testing, variety (line) identification, and pedigree research.

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备注/Memo

备注/Memo:
收稿日期：2024-08-29基金项目：江苏省种业振兴“揭榜挂帅”项目 [JBGS(2021)057]作者简介：周欣兴(1996-),男,安徽铜陵人,硕士,助理研究员,主要从事农业植物新品种测试和农业信息技术研究。(E-mail)20211012@jaas.ac.cn通讯作者：刘亚菊,(E-mail)yajuliu@jaas.ac.cn
更新日期/Last Update: 2025-05-26