Early Forest Fire Detection and Prevention using Ultra-Low-Power Co-Processors with Convolutional Neural Networks

In recent years, wildfires pose a critical threat to ecosystems, human life, and infrastructure, with early detection being vital for effective mitigation. Traditional fire monitoring systems often suffer from limited coverage, and deep sleep issues, especially in remote forested regions. To overcome these limitations, this research presents a novel low-power wildfire detection framework that combines Ultra-Low-Power coprocessors with Convolutional Neural Networks (ULP-CNN) based aerial video analysis from autonomous drones. The integration of ULP processors continuously monitoring and real-time Artificial Intelligence (AI) video analysis offers a full coverage quick response solution for wildfire prevention. The proposed system begins with the deployment of Long-Range Wide Area Network (LoRaWAN)-enabled sensing nodes equipped with ULP processors for efficient environmental data collection and minimal power draw during sleep cycles. Further, based on detecting abnormal air quality changes, an autonomous drone is dispatched to the suspected fire location. Then, the onboard CNN model analyzes real-time images and video frames to classify fire occurrences and initiate particle filter-based tracking. Experimental evaluation demonstrates that the system achieves high detection accuracy (99.60%), precision (99.89R%), recall (99.74%), and F1 score (99.83%) making it a highly effective solution for real-time detection of wildfires.