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

TL;DR

This study addresses the high cost barrier of multispectral UAV sensors in precision agriculture by evaluating the performance of affordable RGB vegetation indices against traditional multispectral indices for palm cultivation in Dubai. Using UAV imagery, the authors compute $NDVI$ and $SAVI$ from multispectral data and $VARI$ and $MGRVI$ from RGB data to classify vegetation into healthy, moderate, and stressed categories and to generate health maps. The results show that RGB-based indices produce vegetation-health patterns broadly consistent with multispectral indices, demonstrating a viable, cost-effective alternative for large-scale monitoring and stress detection in arid environments. This work supports broader adoption of UAV-based imaging for data-driven crop management and scalable precision farming, particularly where resources are limited.

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.

Figures (6)

• Figure 1: Flowchart of the methodology for UAV-based vegetation health assessment using RGB and multispectral indices.
• Figure 2: Palm Tree Region
• Figure 3: NDVI and SAVI maps of the selected palm plot
• Figure 4: Visual Representation of Vegetation Index for RGB Image
• Figure 5: Pie chart of vegetation indices (NDVI and SAVI) using multispectral images