Multispectral Remote Sensing-Driven Evaluation of Chlorophyll in Tea Plant Canopies

This article introduces an uncrewed aerial vehicle (UAV)-based multispectral model, YOLO-SPAD, for rapid, nondestructive estimation of the relative chlorophyll content of tea leaves. This is crucial for predicting growth conditions, implementing precise irrigation and fertilization, and increasing tea yields. The model leverages the spectral separation of the tea tree canopy and the YOLOv8 neural network architecture for image segmentation. Multispectral imagery of the standardized tea plantation was captured by a UAV equipped with a five-channel camera, while corresponding soil plant analysis development (SPAD) values were measured using a SPAD-502Plus device. Based on the YOLOv8 model, it is proposed to incorporate a spectral fusion (SF) module to better adapt to multispectral characteristics. Four deep learning models for image segmentation were further compared. The improved YOLOv8 model achieved a segmentation mIoU of 91.61% for the tea tree canopy, outperforming the YOLOv5 (90.65%) and DeepLabv3+ (86.14%), which were improved using the same method. Five different band combinations, 43 vegetation indexes, and 40 texture features were analyzed to construct mapping transformation pairs for reflectance in the segmented area. This was applied to the SPAD prediction head to implement YOLO-SPAD. The YOLO-SPAD model was used to predict tea canopy SPAD using UAV multispectral imagery with a coefficient of determination ( $R^{2}$ ) of 0.85 and a low error (root mean square error (RMSE) =2.14, mean absolute error (MAE) =1.79), providing accurate and stable predictions. This model supports dynamic monitoring of tea tree growth via UAV remote sensing, aiding crop nutrition improvement and precision agriculture implementation.