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

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

doi:10.3389/fpls.2022.820585

Abstract

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: R2 = 0.811, root-mean-square error (RMSE) = 0.137 kg/m2, normalized RMSE (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 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.

Highlights

Intercropping, as the essence of traditional agriculture, has the advantages of increasing yield and quality (Mao et al, 2014; Egesa et al, 2016), promoting the utilization of nutrient resources (Rivest et al, 2010; Crème et al, 2016; Davies et al, 2016), increasing biodiversity (Bainard et al, 2011; Sanaa et al, 2016), and reducing pests and weeds (Brooker et al, 2015; Lopes et al, 2016)
Most of the spectral parameters selected in this study had a strong correlation with the growth parameters, which can be used for the modeling and inversion of Aboveground biomass (AGB) and leaf area index (LAI) of red bean and mung bean
For growth parameters of red bean, these models deteriorated with the test dataset and the explanatory degree for AGB and LAI variation decreased to 52.4% (RMSE = 0.194 kg/m2, normalized RMSE (NRMSE) = 0.187) and 56.3% (RMSE = 0.357, NRMSE = 0.119), respectively (Figures 4A,C)

Summary

Introduction

Intercropping, as the essence of traditional agriculture, has the advantages of increasing yield and quality (Mao et al, 2014; Egesa et al, 2016), promoting the utilization of nutrient resources (Rivest et al, 2010; Crème et al, 2016; Davies et al, 2016), increasing biodiversity (Bainard et al, 2011; Sanaa et al, 2016), and reducing pests and weeds (Brooker et al, 2015; Lopes et al, 2016). The different intercropping patterns of tea plantations, such as tea-fruit and tea-soybean intercropping, will be more in line with the biological characteristics of tea plant growth by improving microenvironment and resource utilization. Previous studies have shown that diverse agroforestry-tea intercropping systems, such as tree/tea and soybean/tea cannot only regulate the ecological environment of tea plantation, improve the soil nutrition, and reduce the occurrence of diseases and insect pests and grass, and achieve high yield and quality (Sedaghathoor and Janatpoor, 2012; Li et al, 2019). The intercropping density and the growth status of intercropping crops have a great influence on the growth of tea plants (Natarajan and Willey, 1980; Huang et al, 2019). A better understanding of the growth and development of intercropping crops is of great significance for guiding young tea plantation intercropping techniques and improving planting benefits

Objectives

Methods

Results

Discussion

Conclusion