Elevating Large-scale Forest Surveillance: A Deep Learning Analysis of Inception V3 and EfficientNet for IoT-Driven Fire Detection

This research introduces an innovative forest fire detection system, leveraging the Inception V3 deep learning model, fine-tuned for early detection in remote sensing imagery. The system combines IoT sensors, including DHT11 and MQ7, for real-time data collection, enabling early prediction and management of forest fires. A comprehensive comparison with EfficientNetB0 and EfficientNetB7 models is conducted for computational efficiency and precision. The integration of IoT, machine learning, and real-time aerial surveillance enhances forest fire management on a large scale, showcasing scalability and real-time processing capabilities. The system's effectiveness is demonstrated through empirical data analysis and performance metrics.