ABSTRACT:

Forest fires are one of the most destructive natural hazards, causing irreversible environmental damage, loss of wildlife, property destruction, and posing serious risks to human life. The increasing frequency of wildfires due to climatic changes highlights the urgent need for reliable, automated early detection systems. Traditional fire monitoring methods such as manual observation towers, patrols, or satellite monitoring suffer from limitations including delayed detection, restricted coverage, and dependency on weather conditions. To overcome these challenges, this project proposes an IoT-enabled intelligent fire and smoke detection system using Raspberry Pi, computer vision, and environmental sensing technologies.
The system integrates a Raspberry Pi 4B as the central processing unit, equipped with a Raspberry Pi Camera Module for real-time image acquisition. A YOLO-based deep learning model, trained specifically for fire classification, processes the live video feed to detect visible flame or fire signatures with high accuracy and low latency. In addition to visual detection, the system employs a BME688 environmental gas sensor probe to measure temperature, humidity, pressure, and especially gas resistance, which acts as an indicator of smoke and volatile organic compounds (VOCs). By analysing the rapid drop in gas resistance, the system can detect smoke even before flames become visually noticeable, providing early predictive capability.
To enhance user interaction and monitoring, the system displays real-time fire status, smoke detection results, and environmental parameters on an I2C-based LCD. Furthermore, the design incorporates IoT connectivity by integrating Twilio’s SMS service. When fire, smoke, or both are detected, an automated alert message is instantly delivered to registered mobile numbers, ensuring that concerned authorities can take immediate action. The combined use of AI vision and gas-based sensing provides dual verification, minimizing false alarms and improving detection reliability under diverse environmental conditions.
This project demonstrates a cost-effective, scalable, and robust early warning system suitable for deployment in forests, agricultural zones, industrial environments, residential buildings, and remote monitoring locations. By merging artificial intelligence, IoT communication, and multi-sensor data fusion, the system represents a modern approach toward proactive safety management and environmental protection, significantly contributing to early wildfire prevention and disaster mitigation.

INTRODUCTION:

Fire outbreaks, especially in forest regions, industrial zones, and remote environments, continue to be a major global concern due to their devastating impact on ecological balance, human safety, wildlife, and infrastructure. The early detection of fire and smoke is the most critical factor in minimizing damage, yet conventional methods such as human surveillance, manual patrolling, CCTV monitoring, or satellite-based observation often fail to identify threats at their initial stage. These traditional approaches suffer from delays, limited coverage, environmental dependency, and high operational costs. With the rapid advancements in embedded systems, artificial intelligence, and IoT technologies, there is a growing demand for intelligent, automated, and real-time fire detection systems that can operate continuously with minimal human intervention.
This project introduces an IoT-enabled early fire and smoke detection system using a Raspberry Pi platform, combining computer vision and environmental sensing for highly accurate and reliable results. The system utilizes a Raspberry Pi Camera to continuously capture live video streams, which are processed using a deep learning model based on the YOLO architecture. This AI-based model is trained specifically to detect fire with high precision, even in challenging conditions such as varying lighting, partial visibility, or small flame sizes. Unlike conventional flame sensors that react only to specific wavelengths, the YOLO model analyzes complex image features, making it far more robust and effective in real-world scenarios.
To complement visual detection, the system incorporates the BME688 environmental gas sensor probe, which monitors temperature, humidity, pressure, and especially gas resistance. This gas resistance value provides an accurate indication of smoke, volatile organic compounds (VOCs), and burning particles in the air. The combined approach of using both camera-based AI detection and sensory data fusion greatly reduces false alarms and ensures the system can detect fires or smoke even when flames are not fully visible. This dual-verification technique increases reliability, making the system suitable for outdoor and indoor monitoring applications.
The detected conditions—such as fire, smoke, or safe environment—are displayed locally on an I2C-based LCD for real-time status monitoring. Additionally, the system is integrated with Twilio’s SMS service, allowing immediate alert messages to be sent to designated users or authorities whenever fire or smoke is detected. This instant communication ensures rapid response and timely intervention, which is crucial in preventing small anomalies from turning into large-scale disasters.
The proposed system is cost-effective, power-efficient, portable, and highly scalable. It demonstrates how modern embedded hardware, artificial intelligence, and IoT connectivity can be combined to create a smart, autonomous safety solution for forest regions, agricultural lands, warehouses, homes, and industrial facilities. The design addresses the limitations of traditional systems while offering a highly dependable and technologically advanced method for early fire and smoke detection.

OBJECTIVES:

The primary objective of this project is to design and develop an intelligent, IoT-enabled system capable of detecting fire and smoke at an early stage using a combination of computer vision and environmental sensing. The system aims to overcome the limitations of traditional fire detection mechanisms by incorporating advanced artificial intelligence, precise sensor measurements, and real-time communication technologies. The detailed objectives of this project are as follows:
1. To develop a real-time fire detection system using AI and computer vision
The project aims to implement a Raspberry Pi Camera integrated with a YOLO-based deep learning model to accurately detect fire in live video streams. This objective focuses on enabling fast processing, high detection accuracy, and robustness against environmental challenges such as low light, obstruction, or background confusion.
2. To detect smoke presence using gas sensing technology
This objective involves integrating the BME688 environmental gas sensor probe to monitor gas resistance values that correlate with smoke and VOC levels. The system uses threshold-based analysis to determine whether smoke is present, enabling detection even before flames become visible.
3. To combine vision-based and sensor-based detection for improved accuracy
The project aims to fuse data from both the YOLO fire detection model and the BME688 smoke sensor to minimize false positives and enhance detection reliability. By verifying fire and smoke together, the system achieves a more dependable early warning mechanism.
4. To provide immediate visual feedback through an LCD display
An I2C-based LCD module is used to display the detection status—whether fire, smoke, both, or safe conditions—along with temperature and humidity values. This ensures that even without internet, the system offers clear and continuous local monitoring.
5. To implement an IoT-based notification system
The project integrates Twilio’s SMS API to send real-time alerts to the user’s mobile phone when fire or smoke is detected. This objective ensures rapid communication so that immediate action can be taken to prevent escalation.
6. To design a cost-effective and scalable early warning solution
A major objective is to create a low-cost, efficient, and scalable system suitable for forests, agricultural fields, industrial zones, remote locations, and residential safety applications. The system should be easy to deploy and expand without high infrastructure requirements.
7. To improve environmental safety through proactive detection
The ultimate goal of the project is to enhance safety by enabling early detection and intervention, thereby reducing environmental damage, saving resources, and protecting human and wildlife life from potential fire hazards.

PROBLEM STATEMENT:
Fire accidents, especially in forested regions, industries, and residential environments, continue to cause severe damage due to delayed detection and slow response times. Traditional fire monitoring systems such as manual surveillance, CCTV cameras, satellite imaging, and basic smoke sensors often fail to provide early and reliable warnings. These systems suffer from major limitations such as inaccurate detection, high false-alarm rates, limited environmental sensing capabilities, dependency on human intervention, and delayed communication to authorities. Conventional flame sensors detect only infrared wavelengths and are ineffective in detecting smoke or small flames, while gas sensors alone cannot distinguish between various fire conditions.
In many real scenarios, smoke appears before visible flames, and relying only on visual or only on gas-based detection becomes insufficient. Furthermore, existing fire alarm systems lack intelligent decision-making and do not provide remote notifications, resulting in loss of valuable response time. The absence of a unified system that combines artificial intelligence, real-time environmental sensing, and IoT-based communication leads to slow hazard identification and increased risk of fire escalation.
Therefore, there is a critical need for a smart, automated early warning system that can accurately detect both fire and smoke using advanced technologies. This system must be capable of continuous monitoring, rapid detection, dual verification (camera + sensor), and instant alert delivery to responsible personnel. The problem is to design a reliable, cost-effective, and real-time solution that reduces false alarms, improves detection accuracy, and ensures timely response to prevent large-scale fire outbreaks.