Hyperspectral machine-learning model for screening tea germplasm resources with drought tolerance

文献类型： 外文期刊

第一作者： Chen, Sizhou

作者： Chen, Sizhou;Shen, Jiazhi;Ding, Zhaotang;Chen, Sizhou;Fan, Kai;Qian, Wenjun;Zhang, Jie;Han, Xiao;Wang, Yu;Gu, Honglian;Li, Yuchen

作者机构：

关键词： tea germplasm resources; hyperspectral imaging; machine learning; nondestructive testing; drought tolerance

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：6.627； 五年影响因子：7.255 ）

ISSN： 1664-462X

年卷期： 2022 年 13 卷

页码：

收录情况： SCI

摘要： Drought tolerance and quality stability are important indicators to evaluate the stress tolerance of tea germplasm resources. The traditional screening method of drought resistant germplasm is mainly to evaluate by detecting physiological and biochemical indicators of tea plants under drought stresses. However, the methods are not only time consuming but also destructive. In this study, hyperspectral images of tea drought phenotypes were obtained and modeled with related physiological indicators. The results showed that: (1) the information contents of malondialdehyde, soluble sugar and total polyphenol were 0.21, 0.209 and 0.227 respectively, and the drought tolerance coefficient (DTC) index of each tea variety was between 0.069 and 0.81; (2) the comprehensive drought tolerance of different varieties were (from strong to weak): QN36, SCZ, ZC108, JX, JGY, XY10, QN1, MS9, QN38, and QN21; (3) by using SVM, RF and PLSR to model DTC (drought tolerance coefficient) data, the best prediction model was selected as MSC-2D-UVE-SVM (R-2 = 0.77, RMSE = 0.073, MAPE = 0.16) for drought tolerance of tea germplasm resources, named Tea-DTC model. Therefore, the Tea-DTC model based on hyperspectral machine-learning technology can be used as a new screening method for evaluating tea germplasm resources with drought tolerance.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序