筛选耐旱茶树种质资源的高光谱机器学习模型

摘要：抗旱性和品质稳定性是评价茶树种质资源抗逆性的重要指标，传统的抗旱种质筛选方法主要是通过检测干旱胁迫 下茶树的生理生化指标进行评价，但该方法不仅费时，而且具有破坏性。本研究获取了茶树干旱表型的高光谱图像，并 结合相关生理指标进行建模。结果表明：（1）茶树品种的丙二醛、可溶性糖和总多酚的信息含量分别为 0.21、0.209 和 0.227，各茶树品种的抗旱系数（DTC）指数在 0.069～0.81 之间；（2）不同品种的综合抗旱性由强到弱依次为：QN36、 SCZ、ZC108、JX、JGY、XY10、QN1、MS9、QN38、QN21； (3)利用SVM、RF和PLSR对DTC（（ 耐旱系数）数据进 行建模，筛选出茶树种质资源耐旱性预测效果最优的模型为MSC-2D-UVE-SVM（（R2=0.77，RMSE=0.073，MAPE=0.16）， 即Tea-DTC模型。因此，基于高光谱机器学习技术的Tea-DTC模型可作为评价茶树种质资源耐旱性的一种新筛选方法。

关键词：茶树种质资源、高光谱成像、机器学习、无损检测、耐旱性

期刊：Sec. Plant Biophysics and Modeling

DOI：https://DOI.org/10.3389/fpls.2022.1048442

设备：GaiaSorter

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

Abstract：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.