Forest Fire Prediction and Detection Using Deep Neural Networks

This project focuses on the prediction and detection of forest fires using Deep Neural Networks (DNN). Forest fires cause significant environmental damage and threaten ecosystems, making early detection crucial for minimizing their impact. The system is developed by collecting a comprehensive dataset consisting of fire and non-fire images, which are used to train the deep learning model to accurately differentiate between the two classes. After training, the model is deployed to test real-time scenarios using a camera-based setup, enabling continuous monitoring and timely identification of potential fire outbreaks. The proposed DNN-based approach enhances detection accuracy, reduces false alarms, and provides an efficient automated solution for forest fire management. This technology aims to support faster response times and improve overall forest safety through advanced image analysis and machine learning techniques.