International Research Journal of Education and Technology

Wildfires are a serious threat to the ecosystem, humans, and infrastructure and need sophisticated detection systems with the capability of enabling timely avoidance of risk. Ground sensors and visual observations, which are traditional methods of wildfire detection, are slow, expensive, and prone to false alarms. This paper introduces a Smart Wildfire Detection System based on real-time video feeds, the YOLO v8 object detection algorithm, and machine learning algorithms to identify wildfires with maximum accuracy and zero delay. The system combines camera-based surveillance with AI-based image analysis to identify wildfire incidents from natural environmental changes. The system issues real-time alerts to the agencies for quick action after identification. The solution is scalable, low-cost, and implementable in remote and underprivileged locations. Experimental results validate the success of the system in early wildfire detection, minimization of false alarms, and enhancement of response time. The strategy is a landmark achievement in the management of wildfires, presenting an active and viable countermeasure to wildfires.

Keywords: Wildfire detection, YOLO v8, Poweredsensing,realtimemonitoring, machine learning, IOT, early risk mitigation.