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Estimating rice chlorophyll status from UAV RGB imagery: effects of image resolution and texture feature parameterization
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DOI：10.1080/01431161.2026.2695948.png)
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Abstract
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Accurate estimation of rice chlorophyll status is essential for precision nitrogen management. Although multispectral and hyperspectral sensors are effective, their high cost and data-processing complexity limit widespread adoption. This study investigated the efficacy of low-cost UAV-based RGB imagery for estimating rice chlorophyll status, represented by SPAD measurements, focusing on the effects of image resolution, feature integration of vegetation indices (VI) and texture features (TF), and TF parameterization. UAV RGB imagery with three image resolutions (3.8, 9.6, and 25 mm) was analysed, and six regression algorithms, including multiple linear regression (MLR), support vector machine (SVM), random forest (RF), Gaussian process (GP), light gradient boosting machine (LGBM), and categorical boosting (CAT), were used to establish estimation models. Results demonstrated that 1) the integration of VIs and TFs improved model performance compared with VIs alone, and the best results were obtained at the 25 mm resolution; 2) TF parameterization also affected model performance, and the best results were obtained with a window size of 11 × 11 and a direction of 90°. Validation on an independent test dataset confirmed the model’s reliability (R2 = 0.84, RMSE = 2.35). Overall, these findings provide a high-efficiency, cost-effective framework for rice chlorophyll status monitoring and RGB-based decision making in precision nutrient management.
Keywords:
Rice chlorophyll status
UAV RGB imagery
precision agriculture
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