在对不结球白菜进行植物新品种DUS(Distinctness,Uniformity and Stability)测试过程中,性状获取大多依靠人工测量、目视测量等方式,存在测量精度不高、主观判断成分高以及相关性状仅能定性描述等问题.通过数字图像处理技术,依据NY/T 2223-2012对采集的不结球白菜图像进行分析,定义了叶长宽比、叶弧度、叶内角等10个与不结球白菜形状相关的表型性状,定义了叶面积、叶周长、株高等13个与不结球大小相关的表型性状,定义了叶面泡状程度、叶均匀度2个与不结球纹理相关的表型性状.数字化手段可更为精准地获取以往人工测量的性状指标,同时使用新的数字化表型指标可量化代替仅能定性描述的性状指标.据此开发的一套不结球白球表型性状分析软件,可实现对不结球白菜生长、发育过程中关键性状的自动提取和定量分析,提高不结球白菜DUS测试工作中表型信息采集以及分析的效率和准确性.
Digital image processing assisted phenotypic analysis of non-heading Chinese cabbage
In the process of DUS(Distinctness,Uniformity and Stability)testing of new plant varieties of non-heading Chinese cabbage,most of the trait acquisition relies on manual measurement,visual measurement and other methods,which has the problems of low measurement accuracy,high subjective judgement and only qualitative description of related traits.Firstly,this paper analyses the collected images of non-heading Chinese cabbage through digital image processing technology according to the NY/T 2223-2012,and then defines 10 phenotypic traits related to the shape of non-heading Chinese cabbage such as leaf aspect ratio,leaf curvature,leaf internal angle,and so on,and 13 phenotypic traits related to the size of the non-heading Chinese cabbage such as leaf area,leaf perimeter,plant height,and so on,as well as 2 phenotypic traits related to the size of the non-heading Chinese cabbage,namely,leaf surface vesicle degree,leaf uniformity,and leaf surface vesiculation.degree,leaf uniformity,and 2 phenotypic traits related to non-bearing texture.At the same time,we used new digital phenotypic indicators to quantitatively replace those that could only be described qualitatively.Finally,we developed a set of phenotypic trait analysis software for non-heading Chinese cabbage,which realised the automatic extraction and quantitative analysis of key traits in the growth and development process of non-heading Chinese cabbage,and improved the efficiency and accuracy of the collection and analysis of phenotypic information in the DUS test of non-heading Chinese cabbage.It improves the efficiency and accuracy of phenotypic information collection and analysis in the DUS testing of Botrytis cinerea.
Non-heading Chinese cabbageDigital image processingPhenotypic traitsDUS
万方数据
维普
