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Prediction of Drought-Induced Components and Evaluation of Drought Damage of Tea Plants Based on Hyperspectral Imaging

Effective evaluation of physiological and biochemical indexes and drought degree of tea plant is an important technology to determine the drought resistance ability of tea plants. At present, the traditional detection method of tea drought stress is mainly based on physiological and biochemical dete...

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Autores principales: Chen, Sizhou, Gao, Yuan, Fan, Kai, Shi, Yujie, Luo, Danni, Shen, Jiazhi, Ding, Zhaotang, Wang, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8417055/
https://www.ncbi.nlm.nih.gov/pubmed/34490000
http://dx.doi.org/10.3389/fpls.2021.695102
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author Chen, Sizhou
Gao, Yuan
Fan, Kai
Shi, Yujie
Luo, Danni
Shen, Jiazhi
Ding, Zhaotang
Wang, Yu
author_facet Chen, Sizhou
Gao, Yuan
Fan, Kai
Shi, Yujie
Luo, Danni
Shen, Jiazhi
Ding, Zhaotang
Wang, Yu
author_sort Chen, Sizhou
collection PubMed
description Effective evaluation of physiological and biochemical indexes and drought degree of tea plant is an important technology to determine the drought resistance ability of tea plants. At present, the traditional detection method of tea drought stress is mainly based on physiological and biochemical detection, which is not only destructive to tea plants, but also time-consuming and laborious. In this study, through simulating drought treatment of tea plant, hyperspectral camera was used to obtain spectral data of tea leaves, and three machine learning models, namely, support vector machine (SVM), random forest (RF), and partial least-squares (PLS) regression, were used to model malondialdehyde (MDA), electrolyte leakage (EL), maximum efficiency of photosystem II (Fv/Fm), soluble saccharide (SS), and drought damage degree (DDD) of tea leaves. The results showed that the competitive adaptive reweighted sampling (CARS)-PLS model of MDA had the best effect among the four physiological and biochemical indexes (Rcal = 0.96, Rp = 0.92, RPD = 3.51). Uninformative variable elimination (UVE)-SVM model was the best in DDD (Rcal = 0.97, Rp = 0.95, RPD = 4.28). Therefore, through the establishment of machine learning model using hyperspectral imaging technology, we can monitor the drought degree of tea seedlings under drought stress. This method is not only non-destructive, but also fast and accurate, which is expected to be widely used in tea garden water regime monitoring.
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spelling pubmed-84170552021-09-05 Prediction of Drought-Induced Components and Evaluation of Drought Damage of Tea Plants Based on Hyperspectral Imaging Chen, Sizhou Gao, Yuan Fan, Kai Shi, Yujie Luo, Danni Shen, Jiazhi Ding, Zhaotang Wang, Yu Front Plant Sci Plant Science Effective evaluation of physiological and biochemical indexes and drought degree of tea plant is an important technology to determine the drought resistance ability of tea plants. At present, the traditional detection method of tea drought stress is mainly based on physiological and biochemical detection, which is not only destructive to tea plants, but also time-consuming and laborious. In this study, through simulating drought treatment of tea plant, hyperspectral camera was used to obtain spectral data of tea leaves, and three machine learning models, namely, support vector machine (SVM), random forest (RF), and partial least-squares (PLS) regression, were used to model malondialdehyde (MDA), electrolyte leakage (EL), maximum efficiency of photosystem II (Fv/Fm), soluble saccharide (SS), and drought damage degree (DDD) of tea leaves. The results showed that the competitive adaptive reweighted sampling (CARS)-PLS model of MDA had the best effect among the four physiological and biochemical indexes (Rcal = 0.96, Rp = 0.92, RPD = 3.51). Uninformative variable elimination (UVE)-SVM model was the best in DDD (Rcal = 0.97, Rp = 0.95, RPD = 4.28). Therefore, through the establishment of machine learning model using hyperspectral imaging technology, we can monitor the drought degree of tea seedlings under drought stress. This method is not only non-destructive, but also fast and accurate, which is expected to be widely used in tea garden water regime monitoring. Frontiers Media S.A. 2021-08-19 /pmc/articles/PMC8417055/ /pubmed/34490000 http://dx.doi.org/10.3389/fpls.2021.695102 Text en Copyright © 2021 Chen, Gao, Fan, Shi, Luo, Shen, Ding and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Chen, Sizhou
Gao, Yuan
Fan, Kai
Shi, Yujie
Luo, Danni
Shen, Jiazhi
Ding, Zhaotang
Wang, Yu
Prediction of Drought-Induced Components and Evaluation of Drought Damage of Tea Plants Based on Hyperspectral Imaging
title Prediction of Drought-Induced Components and Evaluation of Drought Damage of Tea Plants Based on Hyperspectral Imaging
title_full Prediction of Drought-Induced Components and Evaluation of Drought Damage of Tea Plants Based on Hyperspectral Imaging
title_fullStr Prediction of Drought-Induced Components and Evaluation of Drought Damage of Tea Plants Based on Hyperspectral Imaging
title_full_unstemmed Prediction of Drought-Induced Components and Evaluation of Drought Damage of Tea Plants Based on Hyperspectral Imaging
title_short Prediction of Drought-Induced Components and Evaluation of Drought Damage of Tea Plants Based on Hyperspectral Imaging
title_sort prediction of drought-induced components and evaluation of drought damage of tea plants based on hyperspectral imaging
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8417055/
https://www.ncbi.nlm.nih.gov/pubmed/34490000
http://dx.doi.org/10.3389/fpls.2021.695102
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