摘要
【目的】为解决生产中板栗品种易混淆、辨别困难等问题,采用几何形态测量法对不同板栗品种叶形进行数字化分析,建立板栗品种叶形鉴别方法。【方法】以来自我国不同板栗产区的80个品种叶片为试验材料,2年间重复采集叶样,每年6 400张。通过扫描获取叶片图像,采用几何形态测量法及Image J软件,结合板栗叶片特征,选取24个鉴定点并按照一致顺序获取叶形坐标数据。利用Morpho J软件将叶形数据以产区、品种进行分类,进行普氏叠印分析将叶片大小与形状因子分离,进一步形成对称及非对称组分。对数据进行主成分分析、偏最小二乘法的异速生长分析并结合网格变化图将不同品种间叶形差异可视化,同时依据鉴别贡献率对24个鉴定点进行分类;借助典型变量、判别分析及显著性检验进行品种鉴别。【结果】1)不同品种叶片形态差异主要受对称组分影响,在非对称组分中差异不明显。对称组分前2个主成分的累计贡献率为80.6%,可作为板栗品种差异分析的典型变量,对称组分的网格变化图显示品种间差异显著。2)主成分及异速生长分析显示,贡献率排名前14的鉴定点一致,可作为一级鉴定点。3)对称组分中前2个变量的累计贡献率为81.4%。散点图显示,除‘怀九’与‘燕丰’,‘燕山红栗’与‘燕昌’,‘燕龙’与‘燕明’,‘六月爆’与‘叶里藏’相似度较高,其余品种均能准确区分。4)产区间判别分析显示,除湖北与安徽(97.5%vs. 96.9%),其余产区间的正确判别率均达到100.0%;品种间判别分析显示,有99.3%的品种间判别率达到100.0%,个别品种判别率较低且均为95.0%以上,判别结果均存在显著差异(P<0.05)。5)聚类分析反映产区及品种间叶形的相似性,分类结果与种源地理分布基本吻合。【结论】基于24个鉴定点的几何形态测量分析可实现不同板栗品种的准确鉴别,筛选出的14个一级鉴定点,3个次级鉴定点,7个补充鉴定点,可准确反映不同板栗品种叶形的主要差异部位,正确判别率达95.3%~100.0%。建立的板栗品种叶形鉴别数据库和基于叶片形态数字化分析的板栗品种鉴别方法,将对板栗品种精准鉴别提供技术支撑。
【Objective】This study aims to solve the problems of easy confusion and difficult identification of chestnut varieties in production,through using the geometric morphometry(GMMs)to digitally analyze the leaf morphology of different chestnut varieties,so as to establish a method of leaf morphology identification of chestnut varieties.【Method】The leaves of 80 varieties from different chestnut producing areas in China were used as materials,a total of 6400 leaves were collected repeatedly in two years.Images were obtained by scanning,geometric morphometric method and Image J software was used to select 24 identification points combined with chestnut leaf characteristics,and the leaf morphology coordinate data were obtained in a consistent order.Morpho J software was used to classify leaf morphology data by production regions and varieties,and a generalized Procrustes analysis was performed to separate the leaf size and the morphology factor,and further form symmetric and asymmetric components.Principal component analysis and partial least squares allometric analysis were performed on the data,and the difference in leaf morphology among different varieties was visualized with grid change diagram.The 24 identification points were classified according to the discrimination contribution rate.The varieties were identified with canonical variables(CVs),discriminant analysis and significance test.【Result】1)The difference in leaf morphology of different varieties was mainly affected by the symmetric components,and the difference was not obvious in the asymmetric components.The cumulative contribution rate of the first two principal components(PCs)in the symmetric components reached 80.6%,which could be used as a CV for the difference analysis of chestnut varieties.The grid change diagram of the symmetric components showed that there were significant differences between varieties.2)Principal component and allometric analysis showed that the top 14 identification marks with the highest contribution rate were the same,and could be used as the first-level identification marks.3)The cumulative contribution rate of the first two CVs in the symmetric components reached 81.4%.The scatter plots showed that except for the similarity between'Huaijiu'and'Yanfeng','Yanshanhongli'and'Yanchang','Yanlong'and'Yanming','Liuyuebao'and'Yelizang'higher,the other varieties could be accurately distinguished.4)Discriminant analysis(DA)of chestnut production regions showed that,except Hubei and Anhui(97.5%vs.96.9%),the correct discrimination rates of other regions reached 100.0%.The DA among varieties showed that 99.3%of the varieties had a discrimination rate of 100.0%,a few of varieties were lower discrimination rate but all above 95.0%,and the discrimination results were significantly different(P<0.05).5)Cluster analysis reflected the similarity of leaf morphology among production regions and varieties,and the classification results were mostly consistent with the geographical distribution of provenance.【Conclusion】The GMMs based on 24 identification marks can accurately identify different chestnut varieties.The screened 14 primary identification marks,3 secondary identification marks,and 7 supplementary identification marks can accurately reflect the main differences in leaf morphology of chestnut varieties,and the correct discrimination rate reaches 95.3%~100.0%.The established chestnut variety leaf morphology identification database and the chestnut variety identification method based on the digital analysis of leaf morphology will provide technical support for the accurate identification of chestnut varieties.
作者
李彤彤
郭素娟
李艳华
Li Tongtong;Guo Sujuan;Li Yanhua(Key Laboratory for Silviculture and Conservation of Ministry of Education,Beijing Forestry University ,Beijing 100083;Yimen County Forestry and Grassland Bureau,Yunnna Province ,Yuxi 651100)
出处
《林业科学》
EI
CAS
CSCD
北大核心
2023年第3期115-126,共12页
Scientia Silvae Sinicae
基金
国家重点研发计划(2019YFD1001604)
林业和草原科技成果国家级推广项目(2020133118)。
关键词
板栗
叶片
几何形态学
品种鉴别
Castanea mollissima
leaf
geometric morphology
variety identification
