Evaluation of important phenotypic parameters of tea plantations using multi-source remote sensing data - Li, He; Wang, Yu; Fan, Kai; Mao, Yilin; Shen, Yaozong; Ding, Zhaotang (Frontiers in Plant Science, 2022)
1 Tea height, leaf area index, canopy water content, leaf chlorophyll and nitrogen and nitrogen concentrations are important phenotypic parameters to reflect the status of tea growth and guide the management of tea plantation. UAV multi-source remote sensing is an emerging technology, which can obtain more abundant multi-source information and enhance dynamic monitoring ability of crops. In order to monitor the phenotypic parameters of tea canopy more efficiently, we first deploy UAVs equipped with multispectral, thermal infrared, RGB, LiDAR, and tilt photography sensors to acquire phenotypic remote sensing data of tea canopy, and then we utilize four machine learning algorithms to model the single source and multi-source data, respectively. The results show that, on the one hand, using multi-source data sets to evaluate H, LAI, W and LCC can greatly improve the accuracy and robustness of the model. LiDAR + TC data sets are suggested for assessing H, and the SVM model delivers the best estimation (Rp2=0.82, RMSEP=0.078). LiDAR + TC + MS data sets are suggested for LAI assessment, and the SVM model delivers the best estimation (Rp2=0.90, RMSEP=0.40). RGB + TM data sets are recommended for evaluating W, and the SVM model delivers the best estimation (Rp2=0.62, RMSEP=1.80). The MS +RGB data set is suggested for studying LCC, and the RF model offers the best estimation (Rp2=0.87, RMSEP=1.80). On the other hand, using single source data sets to evaluate LNC can greatly improve the accuracy and robustness of the model. MS data set are suggested for assessing LNC, and the RF model delivers the best estimation (Rp2=0.65, RMSEP=0.85). The work revealed an effective technique for obtaining high-throughput tea crown phenotypic information and the best model for the joint analysis of diverse phenotypes, it has significant importance as a guiding principle for the future use of artificial intelligence in the management of tea plantations.