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Using Unmanned Aerial Vehicle-Based Multispectral Image Data to Monitor the Growth of Intercropping Crops in Tea Plantation

Aboveground biomass (AGB) and leaf area index (LAI) are important indicators to measure crop growth and development. Rapid estimation of AGB and LAI is of great significance for monitoring crop growth and agricultural site-specific management decision-making. As a fast and non-destructive detection...

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Autores principales: Shi, Yujie, Gao, Yuan, Wang, Yu, Luo, Danni, Chen, Sizhou, Ding, Zhaotang, Fan, Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914207/
https://www.ncbi.nlm.nih.gov/pubmed/35283919
http://dx.doi.org/10.3389/fpls.2022.820585
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author Shi, Yujie
Gao, Yuan
Wang, Yu
Luo, Danni
Chen, Sizhou
Ding, Zhaotang
Fan, Kai
author_facet Shi, Yujie
Gao, Yuan
Wang, Yu
Luo, Danni
Chen, Sizhou
Ding, Zhaotang
Fan, Kai
author_sort Shi, Yujie
collection PubMed
description Aboveground biomass (AGB) and leaf area index (LAI) are important indicators to measure crop growth and development. Rapid estimation of AGB and LAI is of great significance for monitoring crop growth and agricultural site-specific management decision-making. As a fast and non-destructive detection method, unmanned aerial vehicle (UAV)-based imaging technologies provide a new way for crop growth monitoring. This study is aimed at exploring the feasibility of estimating AGB and LAI of mung bean and red bean in tea plantations by using UAV multispectral image data. The spectral parameters with high correlation with growth parameters were selected using correlation analysis. It was found that the red and near-infrared bands were sensitive bands for LAI and AGB. In addition, this study compared the performance of five machine learning methods in estimating AGB and LAI. The results showed that the support vector machine (SVM) and backpropagation neural network (BPNN) models, which can simulate non-linear relationships, had higher accuracy in estimating AGB and LAI compared with simple linear regression (LR), stepwise multiple linear regression (SMLR), and partial least-squares regression (PLSR) models. Moreover, the SVM models were better than other models in terms of fitting, consistency, and estimation accuracy, which provides higher performance for AGB (red bean: R(2) = 0.811, root-mean-square error (RMSE) = 0.137 kg/m(2), normalized RMSE (NRMSE) = 0.134; mung bean: R(2) = 0.751, RMSE = 0.078 kg/m(2), NRMSE = 0.100) and LAI (red bean: R(2) = 0.649, RMSE = 0.36, NRMSE = 0.123; mung bean: R(2) = 0.706, RMSE = 0.225, NRMSE = 0.081) estimation. Therefore, the crop growth parameters can be estimated quickly and accurately using the models established by combining the crop spectral information obtained by the UAV multispectral system using the SVM method. The results of this study provide valuable practical guidelines for site-specific tea plantations and the improvement of their ecological and environmental benefits.
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spelling pubmed-89142072022-03-12 Using Unmanned Aerial Vehicle-Based Multispectral Image Data to Monitor the Growth of Intercropping Crops in Tea Plantation Shi, Yujie Gao, Yuan Wang, Yu Luo, Danni Chen, Sizhou Ding, Zhaotang Fan, Kai Front Plant Sci Plant Science Aboveground biomass (AGB) and leaf area index (LAI) are important indicators to measure crop growth and development. Rapid estimation of AGB and LAI is of great significance for monitoring crop growth and agricultural site-specific management decision-making. As a fast and non-destructive detection method, unmanned aerial vehicle (UAV)-based imaging technologies provide a new way for crop growth monitoring. This study is aimed at exploring the feasibility of estimating AGB and LAI of mung bean and red bean in tea plantations by using UAV multispectral image data. The spectral parameters with high correlation with growth parameters were selected using correlation analysis. It was found that the red and near-infrared bands were sensitive bands for LAI and AGB. In addition, this study compared the performance of five machine learning methods in estimating AGB and LAI. The results showed that the support vector machine (SVM) and backpropagation neural network (BPNN) models, which can simulate non-linear relationships, had higher accuracy in estimating AGB and LAI compared with simple linear regression (LR), stepwise multiple linear regression (SMLR), and partial least-squares regression (PLSR) models. Moreover, the SVM models were better than other models in terms of fitting, consistency, and estimation accuracy, which provides higher performance for AGB (red bean: R(2) = 0.811, root-mean-square error (RMSE) = 0.137 kg/m(2), normalized RMSE (NRMSE) = 0.134; mung bean: R(2) = 0.751, RMSE = 0.078 kg/m(2), NRMSE = 0.100) and LAI (red bean: R(2) = 0.649, RMSE = 0.36, NRMSE = 0.123; mung bean: R(2) = 0.706, RMSE = 0.225, NRMSE = 0.081) estimation. Therefore, the crop growth parameters can be estimated quickly and accurately using the models established by combining the crop spectral information obtained by the UAV multispectral system using the SVM method. The results of this study provide valuable practical guidelines for site-specific tea plantations and the improvement of their ecological and environmental benefits. Frontiers Media S.A. 2022-02-25 /pmc/articles/PMC8914207/ /pubmed/35283919 http://dx.doi.org/10.3389/fpls.2022.820585 Text en Copyright © 2022 Shi, Gao, Wang, Luo, Chen, Ding and Fan. 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
Shi, Yujie
Gao, Yuan
Wang, Yu
Luo, Danni
Chen, Sizhou
Ding, Zhaotang
Fan, Kai
Using Unmanned Aerial Vehicle-Based Multispectral Image Data to Monitor the Growth of Intercropping Crops in Tea Plantation
title Using Unmanned Aerial Vehicle-Based Multispectral Image Data to Monitor the Growth of Intercropping Crops in Tea Plantation
title_full Using Unmanned Aerial Vehicle-Based Multispectral Image Data to Monitor the Growth of Intercropping Crops in Tea Plantation
title_fullStr Using Unmanned Aerial Vehicle-Based Multispectral Image Data to Monitor the Growth of Intercropping Crops in Tea Plantation
title_full_unstemmed Using Unmanned Aerial Vehicle-Based Multispectral Image Data to Monitor the Growth of Intercropping Crops in Tea Plantation
title_short Using Unmanned Aerial Vehicle-Based Multispectral Image Data to Monitor the Growth of Intercropping Crops in Tea Plantation
title_sort using unmanned aerial vehicle-based multispectral image data to monitor the growth of intercropping crops in tea plantation
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914207/
https://www.ncbi.nlm.nih.gov/pubmed/35283919
http://dx.doi.org/10.3389/fpls.2022.820585
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