Authors: Sahil Thange, Karan Shinde, Rushikesh Pingal, Shailesh Mogal, Vishal Chaudhari
Abstract: The project titled “Development of a Smart Agro AI Drone” focuses on designing a cost-effective and intelligent aerial spraying system aimed at improving agricultural productivity through automation. Indian farmers often encounter labour shortages, uneven pesticide application and rising operational costs. To address these challenges, the proposed system integrates Artificial Intelligence (AI) and GPS-based autonomous navigation within a quadcopter platform equipped with a liquid tank, pump, and atomising nozzles for precise and uniform spraying. AI algorithms support crop recognition, optimised flight-path generation, and obstacle avoidance, ensuring safe and efficient field operations. An embedded microcontroller with a flight controller enables stable flight, real-time data transmission, and improved system reliability, while lightweight structural materials enhance endurance and payload capacity. This work also develops a cost-efficient agricultural drone platform by combining low-cost hardware components, open source flight control architecture, lightweight mechanical design, and optimised edge AI processing. The prototype is evaluated based on spray coverage, flight time, payload capacity, endurance, and detection accuracy under varying field conditions and cost-per-hectare performance is compared against existing commercial drone systems. Results demonstrate that strategic component selection, modular mechanical design, and computational model optimisation significantly reduce overall system cost while maintaining effective spraying and monitoring performance. Overall, the Smart Agro AI Drone provides an affordable, intelligent, and practical solution that supports sustainable precision farming, particularly for small and medium scale farmers
DOI: http://doi.org/10.5281/zenodo.20765542
