Fire Detection and Prediction using Machine Learning for Fireworks Industry

The fire crackers manufacturing industry is inherently associated with potential safety hazards, necessitating advanced monitoring systems to ensure the wellbeing of workers and prevent accidents. This project proposes an Integrated Safety Monitoring System that combines sensor technology and machine learning algorithms to detect and predict unsafe conditions in real time. This study proposes an Integrated Safety Monitoring System for the cracker industry, utilizing a combination of sensors and machine learning. Employing a DHT11 sensor for temperature and humidity, two gas sensors for hazardous gas detection, and an ESP32-CAM module for live streaming, the system provides a comprehensive view of the working environment. The K-Nearest Neighbors (KNN) algorithm analyzes historical sensor data to predict safety hazards early. This real-time system offers swift responses to emerging safety concerns, ensuring a proactive approach to accident prevention and creating a safer workplace for industry personnel.