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

编号 040037804

推送时间 20230116

研究领域 森林培育 

年份 2022 

类型 期刊 

语种 英语

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

来源期刊 Frontiers in plant science

期 第378期

发表时间 20221201

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

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 (R2 = 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.

服务人员 孙小满

服务院士 尹伟伦

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