Real Time Monitoring of Forest Fire Detection and Object Detection Using Node MCU

Abstract

Forest fires pose a severe threat to the ecosystems, wildlife, and human settlements, causing widespread environmental damage through their system and loss of biodiversity. Rapid detection and accurate prediction of fire spread are crucial for effective wildfire management and mitigation. This project aims to develop the function of a real-time forest fire monitoring and detection and prediction system that leverages the Internet of Things (IoT) and machine learning (ML) to enhance early fire detection, prediction, and response mechanisms. The system is built around a Node MCU microcontroller, which acts as the core processing unit, interfacing with multiple environmental sensors to detect fire-related anomalies. These sensors include a temperature sensor for detecting sudden heat surges, an MQ-series gas sensor for identifying smoke and hazardousgases, and ananemometer witha wind vane to measure wind speed and direction—crucial factors in fire spread prediction. Once a fire is detected, the system triggers an immediate response mechanism. Authorities and emergency responders receive real-time alerts via a Python- based messaging service, which includes the precise GPS coordinates of the fire location, enabling a rapid and targeted response. Additionally,a buzzeralarmisactivatedinnearby areas, and it can be alert the system, through their authorities providing an audible warning to facilitate quick evacuation and preventive actions. To ensure durability and reliability in harsh environmental conditions, the entire system is mounted on a custom PCB board. The integration of IoT-based real- time monitoring with ML-driven it means machine learning can be used to real monitoring the forest fire and predictive analytics enhances decision-making capabilities, providing critical insights that aid in fire containment strategies.

Introduction

Forest fires cause severe environmental and economic damage, making early detection and accurate prediction essential for effective disaster management. Traditional methods like manual observation and satellite imaging suffer from delays and weather limitations, reducing response efficiency. To address this, the project proposes a real-time forest fire detection and prediction system integrating IoT-enabled sensors (temperature, gas, smoke, wind) with machine learning (ML) algorithms for monitoring fire-related anomalies, predicting fire spread, and issuing rapid alerts.

The system uses a NodeMCU microcontroller to collect sensor data, which ML models analyze to forecast fire behavior based on environmental factors such as wind speed and direction. It features automated alerts via Python-based messaging to notify authorities with GPS coordinates, and an audible warning system to alert nearby communities. Designed with durable hardware for harsh environments, the system aims to improve firefighting resource allocation and minimize wildfire impact.

Key hardware includes MQ135 gas sensors for smoke and harmful gases, temperature sensors (DHT11/DHT22/LM35) for ambient monitoring, and a NodeMCU ESP8266 microcontroller for data processing and wireless communication. The software stack involves Arduino IDE for programming, Embedded C/C++ for sensor interfacing, SQLite3 for data management, MQTT for efficient IoT messaging, HTTP/HTTPS APIs for cloud communication, and Python libraries (Matplotlib, Seaborn) for data visualization.

Conclusion

The Real-Time Forest Fire Monitoring and Wildlife Protection System represents a crucial step forward in environmental conservation. By integrating IoT technology, machine learning, and automated alert mechanisms, the system provides early warning, real-time monitoring, and actionable insights for disaster prevention and wildlife protection. This system lays the groundwork for future advancements in smart environmental monitoring, offering a scalable and adaptable solution for large-scale conservation projects. With continued innovation and refinement, this technology can significantly contribute to global efforts in reducing forest fire damage, preserving biodiversity, and ensuringasaferenvironmentforboth humans and wildlife.

References

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Copyright

Copyright © 2025 J. Balaji, K. Murugan, V. Ramya, M. Sivaranjani. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.