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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67908
Volume:
10 |
10 |
Oct 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Electronics & Telecommunication Engineering
JSPM Bhivrabai Sawant Polytechnic Wagholi

Electronics & Telecommunication Engineering
JSPM Bhivrabai Sawant Polytechnic Wagholi

Electronics & Telecommunication Engineering
JSPM Bhivrabai Sawant Polytechnic Wagholi

Electronics & Telecommunication Engineering
JSPM Bhivrabai Sawant Polytechnic Wagholi

Electronics & Telecommunication Engineering
JSPM Bhivrabai Sawant Polytechnic Wagholi
Drone-Based Crop Monitoring System is an Artificial Intelligence (AI) and Unmanned Aerial Vehicle (UAV)-based agricultural monitoring system designed to improve crop health assessment, field surveillance, and farming efficiency. The proposed system uses a drone equipped with a high-resolution camera, GPS module, and optional environmental sensors to monitor agricultural fields and collect real-time crop information. The captured images and sensor data are processed using image processing and AI-based techniques to identify crop diseases, pest infestations, nutrient deficiencies, water stress, and variations in crop growth. GPS technology helps determine the location of affected areas, enabling farmers to take timely corrective actions. The collected data can be transmitted wirelessly to a computer or mobile application for visualization, analysis, and monitoring. The system helps reduce manual field inspections, save time, optimize the use of water and fertilizers, and support early detection of crop-related problems. The proposed system provides an efficient and scalable approach to precision agriculture, smart farming, resource management, and sustainable agricultural development.
KEYWORDS
The Drone-Based Crop Monitoring System provides an efficient and modern approach to agricultural monitoring by integrating drone technology, cameras, GPS, and image-processing techniques. The system helps farmers monitor large agricultural fields, observe crop growth, and identify potential problems such as diseases, pest attacks, and unhealthy plant conditions at an early stage. By reducing manual inspection and providing location-based crop information, the proposed system supports timely agricultural decision-making and more efficient utilisation of resources. The integration of Artificial Intelligence (AI) and Machine Learning (ML) can further improve crop analysis and automated problem detection. In conclusion, the Drone-Based Crop Monitoring System has the potential to improve agricultural productivity, reduce crop losses, save time, and promote precision agriculture. It represents a valuable step towards smart farming and sustainable agricultur development
1. DJI. Agricultural Drones and Precision Agriculture Solutions. Available at: https://www.dji.com 2. Pixhawk. Pixhawk Autopilot Documentation. Available at: https://pixhawk.org 3. ArduPilot. ArduPilot Documentation: Open-Source Autopilot Software. Available at: https://ardupilot.org 4. Raspberry Pi Foundation. Raspberry Pi Documentation. Available at: https://www.raspberrypi.com/documentation/ 5. OpenCV. OpenCV Documentation: Computer Vision Library. Available at: https://docs.opencv.org 6. Google. Google Earth Engine Documentation. Available at: https://developers.google.com/earth-engine 7. Food and Agriculture Organization of the United Nations. Digital Agriculture and Precision Farming. Available at: https://www.fao.org 8. Ministry of Agriculture and Farmers Welfare, Government of India. Digital Agriculture Mission. Available at: https://agriwelfare.gov.in 9. Colomina, I., and Molina, P. “Unmanned Aerial Systems for Photogrammetry and Remote Sensing: A Review.” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 92, 2014, pp. 79–97. 10. Zhang, C., and Kovacs, J. M. “The Application of Small Unmanned Aerial Systems for Precision Agriculture: A Review.” Precision Agriculture, vol. 13, 2012, pp. 693–712.
Publication Date
10/13/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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