AUTHOR=Shi Yujie , Gao Yuan , Wang Yu , Luo Danni , Chen Sizhou , Ding Zhaotang , Fan Kai 

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

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2022.820585

DOI=10.3389/fpls.2022.820585

ISSN=1664-462X

ABSTRACT=Above-ground 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 nondestructive detection method, UAV-based imaging technologies provides 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 plantation by using UAV multispectral image data. The spectral parameters with high correlation with growth parameters were selected by correlation analysis. It was found that the red and near-red bands were sensitive bands for LAI and AGB. In addition, this paper compared the performance of five machine learning methods in estimating AGB and LAI. The results showed that the SVM (support vector machine) and back propagation neural network (BPNN) models which can simulate nonlinear relationship 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: R2=0.811, RMSE=0.137 kg/m2, NRMSE=0.134; mung bean: R2=0.751, RMSE=0.078 kg/m2, NRMSE=0.100) and LAI (red bean: R2=0.649, RMSE=0.36, NRMSE=0.123; mung bean: R2=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 UAV multispectral system with SVM method. The results of this study provide valuable practical guideline for site-specific tea plantation and its ecological environment benefits improvement.