• Lokesh Pagare
• Jyoti Kanjalkar
• Ojal Gupta
• Labhesh Pahade
• Sayee Papade

Forest fires threaten ecosystems, economies, and human safety. In detecting fires, traditional methods are expensive, inefficient, and often delayed in response. This research introduces an exhaustive drone-based forest fire detection system employing a YOLO V8 model that has been trained on properly labeled fire and smoke datasets. The system underlies a robust drone configuration that integrates Pixhawk for flight control; Flysky for communication; GPS navigation along with Wi-Fi cameras and telemetry for the real-time data transmission process. A dedicated website is developed to monitor the drone data concerning live images along with GPS coordinates, altitude, groundspeed, and alerts. Our system ensures reliable detection of almost 93.5% accuracy in early forest fire detection. Future work includes advanced models of detection, automated firefighting mechanisms IoT-based integration for broader environmental monitoring.

Forest fire detection, UAV-based monitoring, YOLO V8, real-time drone information, fire and smoke detection, unmanned aerial vehicle technology, protection of the environment, deep learning algorithms, Internet of Things integration, prevention of wildfires.

"FOREST FIRE DETECTION USING DRONES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.h654-h663, May-2025, Available :http://www.jetir.org/papers/JETIR2505866.pdf

"FOREST FIRE DETECTION USING DRONES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. pph654-h663, May-2025, Available at : http://www.jetir.org/papers/JETIR2505866.pdf