Multi-Angle Detection of Spatial Differences in Tea Physiological Parameters

Abstract

Chlorophyll and nitrogen contents were used as leaf physiological parameters. Based on multispectral images from multiple detection angles and the stoichiometric data of tea (Camellia sinensis) leaves in different positions on plants, the spatial differences in tea physiological parameters were explored, and the full channel difference vegetation index was established to effectively remove soil and shadow noise. Support vector machine, random forest (RF), partial least square, and back-propagation algorithms from the multispectral images of leaf and canopy scales were then used to train the tea physiological parameter detection model. Finally, the detection effects of the multispectral images obtained from different angles on the physiological parameters of the top, middle, and bottom tea leaves were analysed and compared. The results revealed distinct spatial differences in the physiological parameters of tea leaves in individual plants. Chlorophyll content was lowest at the top and relatively high at the middle and bottom; nitrogen content was the highest at the top and relatively low at the middle and bottom. The horizontal distribution of physiological parameters was similar, i.e., the values in the east and south were high, whereas those in the west and north were low. The multispectral detection accuracy of the physiological parameters at the leaf scale was better than that at the canopy scale; the model trained by the RF algorithm had the highest comprehensive accuracy. The coefficient of determination between the predicted and measured values of the spad-502 plus instrument was (R2) = 0.79, and the root mean square error (RMSE) was 0.11. The predicted result for the nitrogen content and the measured value was R2 = 0.36 and RMSE = 0.03. The detection accuracy of the multispectral image taken at 60° for the physiological parameters of tea was generally superior to those taken at other shooting angles. These results can guide the high-precision remote sensing detection of tea physiological parameters.