基于光谱特征的棉花苗期耐涝性鉴定
作者:
作者单位:

1.长江大学农学院 / 作物抗逆技术研究中心;2.湖北省荆州农业科学院

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基金项目:

国家重点研发计划项目(2018YFD0100403)


Identification of Cotton Seedling Waterlogging Tolerance Based on Spectral Imaging Technology
Author:
Affiliation:

1.School of Agriculture,Yangtze University/Research Center of Crop Stresses Resistance Technologies;2.Jingzhou Academy of Agricultural Science

Fund Project:

National Key Research and Development Programs (2018YFD0100403)

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    摘要:

    耐涝性是一个复杂农艺性状,采用传统方法大规模鉴定难度大。高光谱成像在作物表型检测方面具有无损、高效的优点,可望实现作物耐涝性的快速鉴定。本研究以 27 个陆地棉品种为供试材料,测定低氧胁迫与正常供氧条件下的株高、茎粗、叶面积、总根长,利用便携式光谱仪获取光谱图像,分析涝渍特征曲线并提取与色素、水分及氮素相关的 6 个光谱特征指数,采用主成分、逐步回归、系统聚类、相关性等多元分析方法,构建了基于光谱特征的棉花苗期耐涝性鉴定模型。利用主成分分析将 10 个耐涝单项指标简化为 4 个主成分。根据主成分贡献率和权重,将 4 个主成分简化为耐涝综合评价系数 D 值。逐步回归分析建立 D 值与 10 个单项指标耐涝性系数最优线性回归方程:D=0.161+0.220 SD+0.068 LA+0.358 PSSRa+0.404 I1+0.292 NDSI(R2=0.9849,式中 SD 为茎粗,LA 为叶面积,PSSRa 为色素比值指数 a,I1 为红外指数 1,NDSI 为归一化差值胁迫指数)。聚类分析将 27 份供试材料耐涝性划分为 4 个等级,即强耐涝、耐涝、中等耐涝、涝渍敏感。相关性分析结果显示,茎粗与色素比值指数a、色素比值指数 b、植物氮光谱指数、归一化差值胁迫指数、红外指数 1 极显著正相关,与归一化差值水指数相关性差异不显著;总根长与色素比值指数 a、植物氮光谱指数、归一化差值胁迫指数呈显著负相关,与归一化差值水指数、红外指数 1 相关性差异不显著;叶面积和株高均与其余 9 个单项指标相关性差异不显著。利用高光谱成像技术为鉴定、评价棉花耐涝性提供了新方法。

    Abstract:

    Waterlogging seriously affects the yield and quality of cotton,whereas the waterlogging tolerance as a complex agronomic trait is difficult for quantification on a large scale using traditional methods. Seeking an efficient method for identifying cotton genotypes showing waterlogging tolerance might guarantee cotton breeding for quality and yield. Hyperspectral technology is a nondestructive and efficient method for crop detection can quickly identify cotton seedlings showing waterlogging resistance. In this study,the plant height,stem diameter,leaf area,and total root length of 27 cotton varieties were measured under hypoxia stress or normal oxygen conditions. Portable spectrometer was used to obtain its spectral image,analyze the waterlogging characteristic curve and extract six spectral indexes related to pigment,moisture and nitrogen. Multivariate analysis methods such as principal component analysis,stepwise regression,systematic clustering,and correlation analysis were adopted to construct a cotton seedling waterlogging tolerance identification model based on hyperspectral images. Principal component analysis showed that 10 individual indicators of waterlogging tolerance might be simplified into four principal components,which were further simplified into the D value of the comprehensive evaluation coefficient of waterlogging tolerance by considering the contribution rate and weight of the principal component and the normalized feature vector. Stepwise regression analysis was performed to establish the optimal linear regression equation of D value and 10 single index waterlogging tolerance coefficients:D=0.161+0.220 SD+0.068 LA+0.358 PSSRa +0.404 I1+0.292 NDSI (R2=0.9849,SD:stem diameter,LA:leaf area,PSSRa:pigment specific simple ratio a,I1:infrareed index 1,NDSI:normalized diffiferent stress index). Cluster analysis of 27 cotton genotypes suggested four grades of waterlogging tolerance,including extreme waterlogging tolerance,waterlogging tolerance,medium waterlogging tolerance,and waterlogging sensitivity. The correlation analysis showed that stem diameter was significantly positively correlated with pigment ratio index a, pigment ratio index b, plant nitrogen spectral index, normalized difference stress index, and infrared index 1,but had no significant correlation with normalized difference water index. The total root length was significantly negatively correlated with pigment ratio index a, plant nitrogen spectral index, and normalized difference stress index,but had no significant difference with normalized difference water index and infrared index 1. There were no significant correlations between leaf area and plant height and the other 9 individual indicators. Collectively,this study highlighted the hyperspectral imaging technology as a useful method to identify the waterlogging tolerance of cotton.

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冯晓冰,潘 锐,胡爱兵,等.基于光谱特征的棉花苗期耐涝性鉴定[J].植物遗传资源学报,2022,23(2):553-562.

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  • 收稿日期:2021-09-14
  • 最后修改日期:2021-09-21
  • 录用日期:2021-10-08
  • 在线发布日期: 2022-03-10
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