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

Adari Rama Sukanya, Puvvula Roopesh Naga Sri Sai, Kota Moses, Rimalapudi Sarvendranath

TL;DR

This paper addresses the lack of comprehensive, region-specific UAV datasets for paddy in India by delivering a large-scale RGB and multispectral dataset collected over 5 acres in Vijayawada, with six growth stages from nursery to harvest. It introduces a detailed SOP and flight-checklist framework to ensure reproducible data capture, paired with high-resolution imagery (1 cm/pixel GSD) and four-band multispectral data (Red, Green, Red-Edge, NIR) alongside rich metadata. Validation via Pix4D Fields yields orthomosaic maps and vegetation indices (NDVI, NDRE), supporting applications such as targeted spraying, disease analysis, and yield estimation, and the dataset is publicly released on IEEE DataPort. The work fills a critical gap for Indian paddy research by enabling precise, repeatable, and scalable crop monitoring and modeling under tropical agro-climatic conditions, with potential for transfer learning to related crops and regions.

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.

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

TL;DR

This paper addresses the lack of comprehensive, region-specific UAV datasets for paddy in India by delivering a large-scale RGB and multispectral dataset collected over 5 acres in Vijayawada, with six growth stages from nursery to harvest. It introduces a detailed SOP and flight-checklist framework to ensure reproducible data capture, paired with high-resolution imagery (1 cm/pixel GSD) and four-band multispectral data (Red, Green, Red-Edge, NIR) alongside rich metadata. Validation via Pix4D Fields yields orthomosaic maps and vegetation indices (NDVI, NDRE), supporting applications such as targeted spraying, disease analysis, and yield estimation, and the dataset is publicly released on IEEE DataPort. The work fills a critical gap for Indian paddy research by enabling precise, repeatable, and scalable crop monitoring and modeling under tropical agro-climatic conditions, with potential for transfer learning to related crops and regions.

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.
Paper Structure (33 sections, 6 figures, 9 tables)

This paper contains 33 sections, 6 figures, 9 tables.

Table of Contents

  1. Introduction
  2. RGB and Multispectral Imaging Datasets
  3. Our Contributions
  4. Uniqueness of Our Dataset
  5. Outline
  6. SOP and Checklists
  7. Step 1: Check the Airspace Zones
  8. Step 2: Check Weather Data
  9. Step 3: Select Optimal Flight Timing
  10. Step 4: Perform Pre-Flight Checks
  11. Step 5: Plan the Mission
  12. Step 6: Flight Monitoring and Control
  13. Step-7 : Conduct Post-Flight Inspection
  14. Step 8: Process and Analyze Data
  15. Our Procedure
  16. ...and 18 more sections

Figures (6)

  • Figure 1: Different stages of Paddy Crop
  • Figure 2: Airspace Map of our field Chodavaram, Vijayawada, Andhra Pradesh in green zone
  • Figure 3: Measurement Instruments
  • Figure 4: RGB and multispectral images: (a) RGB, (b) Green, (c) NIR, (d) Red, (e) RE.
  • Figure 5: Folder Structure
  • ...and 1 more figures