Estimation of Canopy Chlorophyll Content of Apple Trees Based on UAV Multispectral Remote Sensing Images

Juxia Wang (), Yu Zhang, Fei Han, Zhenpeng Shi, Fu Zhao, Fengzi Zhang, Weizheng Pan, Zhiyong Zhang and Qingliang Cui
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Juxia Wang: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
Yu Zhang: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
Fei Han: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
Zhenpeng Shi: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
Fu Zhao: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
Fengzi Zhang: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
Weizheng Pan: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
Zhiyong Zhang: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
Qingliang Cui: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China

Agriculture, 2025, vol. 15, issue 12, 1-24

Abstract: The chlorophyll content is an important index reflecting the growth status and nutritional level of plants. The rapid, accurate and nondestructive monitoring of the SPAD content of apple trees can provide a basis for large-scale monitoring and scientific management of the growth status of apple trees. In this study, the canopy leaves of apple trees at different growth stages in the same year were taken as the research object, and remote sensing images of fruit trees in different growth stages (flower-falling stage, fruit-setting stage, fruit expansion stage, fruit-coloring stage and fruit-maturing stage) were acquired via a DJI MAVIC 3 multispectral unmanned aerial vehicle (UAV). Then, the spectral reflectance was extracted to calculate 15 common vegetation indexes as eigenvalues, the 5 vegetation indexes with the highest correlation were screened out through Pearson correlation analysis as the feature combination, and the measured SPAD values in the leaves of the fruit trees were gained using a handheld chlorophyll meter in the same stages. The estimation models for the SPAD values in different growth stages were, respectively, established through five machine learning algorithms: multiple linear regression (MLR), partial least squares regression (PLSR), support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost). Additionally, the model performance was assessed by selecting the coefficient of determination (R 2 ), root mean square error (RMSE) and mean absolute error (MAE). The results show that the SPAD estimation results vary from stage to stage, where the best estimation model for the flower-falling stage, fruit-setting stage and fruit-maturing stage is RF and those for the fruit expansion stage and fruit-coloring stage are PLSR and MLR, respectively. Among the estimation models in the different growth stages, the model accuracy for the fruit expansion stage is the highest, with R 2 = 0.787, RMSE = 0.87 and MAE = 0.644. The RF model, which outperforms the other models in terms of the prediction effect in multiple growth stages, can effectively predict the SPAD value in the leaves of apple trees and provide a reference for the growth status monitoring and precise management of orchards.

Keywords: apple trees; SPAD; multispectral; vegetation indices; machine learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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