Edge-AI Enabled Real-Time Forest Fire Detection and Early Warning Framework Using YOLOv8 and IoT Technologies
TL;DR
The proposed framework provides low-latency processing, improved detection accuracy, reliable early warning, and scalable deployment for intelligent wildfire monitoring, and future work includes integrating thermal imaging, Vision Transformers, Explainable AI, federated learning, autonomous drones, and Digital Twin technology to further enhance wildfire prediction and disaster management.
Abstract
Forest fires are among the most destructive natural disasters, causing significant environmental damage, biodiversity loss, economic disruption, and threats to human life. Conventional fire monitoring techniques, such as watchtowers, satellite imaging, and manual patrols, often suffer from delayed detection, limited coverage, and high operational costs. This paper proposes an Edge-AI Enabled Real-Time Forest Fire Detection and Early Warning Framework using the YOLOv8 object detection model and Internet of Things (IoT) technologies. The proposed framework performs real-time fire and smoke detection directly on edge devices, reducing detection latency, bandwidth consumption, and dependence on cloud connectivity, making it suitable for remote forest environments. Environmental data collected from IoT sensors, including temperature, humidity, smoke concentration, carbon monoxide (CO), and air quality, are integrated through a sensor fusion mechanism to validate visual detections and minimize false alarms. The framework comprises five layers: Data Acquisition, Edge AI Processing, IoT Sensing, Cloud Communication, and Emergency Alert Generation. Detection results, GPS location, confidence score, timestamp, and sensor readings are transmitted to a cloud dashboard, where automated alerts are delivered to forest authorities via SMS, email, mobile applications, or web platforms. The proposed framework provides low-latency processing, improved detection accuracy, reliable early warning, and scalable deployment for intelligent wildfire monitoring. Future work includes integrating thermal imaging, Vision Transformers, Explainable AI, federated learning, autonomous drones, and Digital Twin technology to further enhance wildfire prediction and disaster management.