Simulation and Surveying of Huge Agriculture Lands with UAV

Abstract

The rapid expansion of agricultural land and the need for precision farming have exposed limitations of conventional surveying methods such as manual inspection and satellite imaging. These methods are slow, costly, and lack high-resolution, real-time data. This project presents a UAV-based system equipped with RGB, multispectral, and thermal sensors for agricultural surveying and crop monitoring. Data is collected using autonomous flights and processed using Open Drone Map and QGIS to generate orthophotos, DEMs, and vegetation indices such as NDVI, NDRE, GNDVI, and NDWI. Results identified crop stress zones covering 18% of the area and reduced data processing time from 48 hours (satellite) to under 4 hours. The study proves UAVs are efficient, accurate, and cost-effective tools for precision agriculture.

Introduction

The study focuses on the use of UAV (drone) technology in precision agriculture to improve crop monitoring, irrigation management, and yield prediction. Traditional methods like manual surveys, satellite imaging, and aircraft surveys are limited by slow speed, low resolution, cost, or environmental constraints, making them less suitable for real-time, high-accuracy agricultural analysis.

UAVs offer high-resolution, multi-sensor data collection (RGB, multispectral, and thermal imaging), enabling rapid and detailed field assessment. They are supported by simulation tools for mission planning and post-flight analysis, improving efficiency and accuracy.

The methodology involves UAV mission planning at 80 m altitude with high image overlap, ground control points for precision, and advanced image processing to generate orthophotos, 3D models, and vegetation maps. Key vegetation indices such as NDVI, NDRE, GNDVI, and NDWI are used to assess crop health, nitrogen levels, and water stress, while thermal imaging identifies irrigation issues using CWSI. Yield prediction is performed using regression models based on NDVI.

Results show high-resolution outputs and effective detection of agricultural stress, with about 18% of crops showing stress and significant water inefficiency identified in 14 hectares. UAV-based yield predictions range from 2100–4800 kg/ha. Compared to traditional methods, UAVs are faster, more accurate, and 50–70% cheaper, with a strong return on investment.

Conclusion

A. Conclusion UAV-based surveying provides: • High accuracy • Fast data collection • Reduced cost • Improved agricultural decision-making It enables precision farming at large scale. B. Limitations • Limited battery life • Processing time • Regulatory approvals C. Future Scope • LiDAR integration • AI-based analysis • IoT integration • Autonomous operations • Multi-season data analytics

References

[1] Zhang & Kovacs (2012) [2] Candiago et al. (2015) [3] Bendig et al. (2014) [4] Maes & Steppe (2019) [5] Jin et al. (2021) [6] Open Drone Map [7] QGIS [8] DGCA (2021)

Copyright

Copyright © 2026 Mohammed Kamaloddin, Godde Madhavi, Ambala Shiva Kumar, Mr. M. Harish kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.