Forest Fire Detection using Next-Gen Technologies for Ecosystem Protection

We present our research work. Forest fire detection framework that leverages multiple deep learning models—YOLOv8, Keras SNN, MobileNetV2, ResNet50, and EfficientNetB0—and some data analysis methods. To propose a robust and comprehensive framework using the real-time capability for object detection enabled by YOLOv8, in our system, two main signs of a forest fire would quickly and accurately detect fire and smoke. Along with that, the characteristic extraction and classification accuracy were augmented by CNN, ResNet50, MobileNetV2, and EfficientNetB0, among others. Keras SNN provided improved performance with respect to dynamic environments. It will notify by email the department associated with and in charge of firefighting and detection of a fire so that action is taken accordingly. The data analysis would also help in identifying risk zones prone to catching fire and working on structural requirements after analysing environmental parameters like temperature, humidity, and wind. A central dashboard gives real-time streaming along with alerts. Combining the efficiency of YOLOv8 in predicting bounding boxes with the featureextraction strengths of CNN-based models gives one chance for quick and reliable detection of forest fires. This combination of advanced machine learning provides a scalable and adaptable solution that constitutes a tremendously effective early warning system used in forest fire prevention and mitigation efforts.