AI-Powered Air Quality Monitoring Using ESP32 and BME280 Sensors for Incense Smoke Classification

Abstract

Air quality monitoring is crucial for understanding environmental and health impacts of pollutants. Accurate, low-cost sensors combined with machine learning models offer new possibilities for real-time detection of air quality variations caused by common sources such as incense smoke. However, developing reliable sensor systems that integrate environmental measurements with predictive algorithms remains a challenge, especially when working with limited hardware.

This project started with designing a sensor platform using a BME280 environmental sensor wired to an ESP32 microcontroller to measure temperature, humidity, pressure, and altitude. Initial setup errors caused sensor damage by exposing it to 5 V instead of 3.3 V power, which was subsequently fixed. After successful recovery was ensured, collecting sensor data from multiple types of incense sticks under controlled conditions was the next step. Using this dataset, I trained a TensorFlow Lite machine learning model to classify incense types based on environmental factors, achieving a validation accuracy of approximately 63%. The model was deployed for offline analysis of recorded sensor data in Google Colab, demonstrating real-time prediction potential, showcasing its ability to classify incense smoke.

This approach highlights the practicality of integrating low-cost environmental sensors with AI models to identify pollution sources dynamically. Future work includes improving model accuracy with more diverse data, implementing live prediction directly on the ESP32 using TinyML, and expanding the system to detect a wider range of air quality factors. These advancements could contribute to accessible, portable air quality monitoring tools for personal and community health applications