A UAV-Based Multispectral and RGB Dataset for Multi-Stage Paddy Crop Monitoring in Indian Agricultural Fields

TL;DR

This paper introduces a comprehensive UAV-based RGB and multispectral image dataset of Indian paddy fields, covering all growth stages, to facilitate research in crop monitoring, disease detection, and yield prediction.

Contribution

The creation of a large-scale, high-resolution UAV dataset with detailed metadata covering all paddy growth stages in India is a novel resource for agricultural research.

Findings

Dataset includes 42,430 images over 5 acres with 1 cm/pixel GSD.

Validated images using orthomosaic and vegetation index maps.

Dataset supports targeted spraying, disease analysis, and yield estimation.

Abstract

We present a large-scale unmanned aerial vehicle (UAV)-based RGB and multispectral image dataset collected over paddy fields in the Vijayawada region, Andhra Pradesh, India, covering nursery to harvesting stages. We used a 20-megapixel RGB camera and a 5-megapixel four-band multispectral camera capturing red, green, red-edge, and near-infrared bands. Standardised operating procedure (SOP) and checklists were developed to ensure repeatable data acquisition. Our dataset comprises of 42,430 raw images (415 GB) captured over 5 acres with 1 cm/pixel ground sampling distance (GSD) with associated metadata such as GPS coordinates, flight altitude, and environmental conditions. Captured images were validated using Pix4D Fields to generate orthomosaic maps and vegetation index maps, such as normalised difference vegetation index (NDVI) and normalised difference red-edge (NDRE) index. Our dataset is one of the few datasets that provide high-resolution images with rich metadata that cover all growth stages of Indian paddy crops. The dataset is available on IEEE DataPort with DOI, . It can support studies on targeted spraying, disease analysis, and yield estimation.