IoT-based AQI Estimation using Image Processing and Learning Methods

Nitin Nilesh, Spanddhana Sara, Om Kathalkar, Shreyash Gujar

Abstract: Air pollution is a concern for the health of all living beings. It is essential to check on the quality of air in the surroundings. This experiment was conducted to provide an IoT-based real-time air quality index (AQI) estimation technique using images and weather sensors in the Indian city of Hyderabad. A mixture of image features, i.e., traffic density, visibility, and sensor features, i.e., temperature and humidity, were used to predict the AQI. Object detection and localization-based Deep Learning (DL) methods, along with image processing techniques, were used to extract image features, while a Machine Learning (ML) model was trained on those features to estimate the AQI. In order to conduct this experiment, a dataset containing 5048 images along with co-located AQI values across different seasons was collected by driving on the roads of Hyderabad city in India. The experimental results report an overall accuracy of 82\% for AQI prediction.

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Repository URL: https://iiitaphyd-my.sharepoint.com/:f:/g/personal/om_kathalkar_research_iiit_ac_in/EqQ7XKoVPwhJqWdgVwVJobABje7oE6jBoMZQGRhtaadspg?e=tAX9xf