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

TL;DR

This study evaluates UAV-based RGB and multispectral imaging for vegetation health assessment in palm cultivation, demonstrating that RGB indices can effectively replace multispectral ones, reducing costs while maintaining accuracy.

Contribution

It provides a comprehensive comparison showing RGB-based vegetation indices are a cost-effective alternative to multispectral indices for UAV-based crop monitoring.

Findings

RGB indices perform comparably to multispectral indices in vegetation classification.

RGB imaging reduces operational costs without sacrificing accuracy.

UAV-based RGB and multispectral indices effectively categorize plant health conditions.

Abstract

Precision farming relies on accurate vegetation monitoring to enhance crop productivity and promote sustainable agricultural practices. This study presents a comprehensive evaluation of UAV-based imaging for vegetation health assessment in a palm tree cultivation region in Dubai. By comparing multispectral and RGB image data, we demonstrate that RGBbased 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 NDVI and SAVI were computed to categorize vegetation into healthy, moderate, and stressed conditions. Simultaneously, RGB-based indices like VARI and 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 UAVbased 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.