Monitoring of vegetation chlorophyll content in photovoltaic areas using UAV-mounted multispectral imaging

• Published in: Frontiers in plant science Vol. 16
• Main Authors: Li, Ming, Wang, Weiyi, Li, Haoran, Yang, Zekun, Li, Jianjun
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 13.08.2025
• Subjects: chlorophyll content; deep learning; Plant Science; texture feature; UAV; vegetation index
• ISSN: 1664-462X; 1664-462X
• DOI: 10.3389/fpls.2025.1643945

The rapid and accurate acquisition of vegetation information, particularly chlorophyll content, is essential for effective vegetation management and ensuring the safe operation of photovoltaic power plants. In this study, the vegetation within a photovoltaic power plant served as the research subject, and multispectral images were acquired using unmanned aerial vehicles, while in situ chlorophyll measurements were obtained through ground-based sampling at multiple time points. From these images, twenty vegetation indices and thirty-two texture features were extracted. To reduce feature redundancy and enhance modeling efficiency, feature selection was performed using the minimum redundancy maximum relevance method and Pearson correlation analysis. The selected features were then used in three modeling strategies—vegetation index–based, texture feature–based, and fused index–texture–based—employing three conventional machine-learning regressors (partial least squares regression, random forest, support vector machine regression) and three deep-learning regressors (back propagation neural network, convolutional neural network, multilayer perceptron). Based on the optimal model, a spatiotemporal distribution map of chlorophyll content within the study area was generated. The results indicated that both vegetation indices and texture features exhibited significant correlations with chlorophyll content, with the strongest correlation observed between the green normalized difference vegetation index (GNDVI) and the NIR_Mean (Pearson coefficients of 0.82 and 0.65, respectively). Moreover, the fusion of vegetation indices and texture features effectively improved the accuracy of chlorophyll inversion models; among the six regression algorithms tested, the multilayer perceptron model achieved the highest performance (R² = 0.874, RMSE = 3.725, MAPE = 3.982%). This study provides a novel method for monitoring chlorophyll content in vegetation within photovoltaic power plant regions and offers informational support for refined regional vegetation management.