Real‐Time Forest Fire Detection, Monitoring, and Mitigation to Safeguard Humanity Using YOLO and Federated Learning

Abstract

Wildfires present a significant danger to ecosystems, property, and human life. Early detection and quick action are the most important things to do to lessen their terrible effects. This study introduces ForestGuard, an innovative system that integrates the strengths of You Only Look Once (YOLO) object detection and federated learning (FL) to enhance real‐time detection and response to forest fires. YOLO’s proficiency in swiftly and accurately detecting fire‐related objects (smoke, flames) is combined with FL’s capacity to collaboratively refine models across distributed devices while safeguarding data privacy. The proposed ForestGuard consists of four primary components: data acquisition and preprocessing, model training, real‐time detection, and evaluation and refinement. The ForestGuard system went through a lot of training and testing with a huge dataset of forest fires, which was better than standard methods. With impressive precision, recall, and F 1 scores of 97.92%, 96.1%, and 96.9%, respectively, the system outperformed existing advanced technologies for fire detection. ForestGuard is a game‐changing tool for managing forest fires because it can always get better, which protects both people and the environment. Also, ForestGuard’s ability to learn and change over time shows how useful it could be for managing forest fires and protecting people and the environment.