Evaluation of UAV-Based RGB and Multispectral Vegetation Indices for Precision Agriculture in Palm Tree Cultivation
To address the high cost of vegetation health monitoring in date palm precision agriculture in Dubai, this study proposes a low-cost alternative using UAV-acquired RGB imagery. By synchronously collecting RGB and multispectral imagery, we systematically evaluate the performance of RGB-based vegetation indices—including VARI and MGRVI—against multispectral indices such as NDVI and SAVI for stress detection and three-tier health classification (healthy, moderately stressed, stressed). This work presents the first empirical validation in palm crops demonstrating that RGB indices achieve classification accuracy comparable to multispectral indices (mean accuracy difference <2.3%), while substantially reducing hardware acquisition and operational costs. The findings establish a scalable, cost-effective technical pathway and methodological framework for large-scale remote sensing monitoring of tropical fruit trees.