Evaluation of UAV-Based RGB and Multispectral Vegetation Indices for Precision Agriculture in Palm Tree Cultivation

Abstract

Precision farming relies on accurate vegetation monitoring to enhance crop productivity and promote sustainable agricultural practices. This study presents a comprehensive evaluation of Unmanned Aerial Vehicle (UAV)-based imaging for vegetation health assessment in a palm tree cultivation region in Dubai. By comparing multispectral and Red, Green, and Blue (RGB) image data, we demonstrate that RGB-based vegetation indices offer performance comparable to more expensive multispectral indices, providing a cost-effective alternative for large-scale agricultural monitoring. Using UAVs equipped with multispectral sensors, indices such as Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) were computed to categorize vegetation into healthy, moderate, and stressed conditions. Simultaneously, RGB-based indices like Visible Atmospherically Resistant Index (VARI) and Modified Green Red Vegetation Index (MGRVI) delivered similar results in vegetation classification and stress detection. Our findings highlight the practical benefits of integrating RGB imagery into precision farming, reducing operational costs while maintaining accuracy in plant health monitoring. This research underscores the potential of UAV-based RGB imaging as a powerful tool for precision agriculture, enabling broader adoption of data-driven decision-making in crop management. By leveraging the strengths of both multispectral and RGB imaging, this work advances the state of UAV applications in agriculture, paving the way for more efficient and scalable farming solutions.