Paper Contents
Abstract
Forest fires have emerged as one of the most devastating natural calamities, resulting in large-scale deforestation, loss of biodiversity, and significant ecological imbalance. Existing methods of forest fire detection, such as satellite surveillance and manual monitoring, often suffer from latency and lack of precision. In this study, we propose a realtime forest fire detection and alert system using a Convolutional Neural Network (CNN) to distinguish fire and non-fire imagery. Once fire is detected, the system activates a buzzer using an ESP32 microcontroller connected via USB, while simultaneously sending a notification to a mobile device through the Blynk IoT platform. The system is cost-effective, scalable, and suitable for remote deployment in forest areas, significantly reducing the delay between fire outbreak and emergency response.
Copyright
Copyright © 2025 Vidya KM. This is an open access article distributed under the Creative Commons Attribution License.
