Android app to Predicting Air Quality using computer vision and machine learning

Project Title

Android app to Predicting Air Quality using computer vision and machine learning

Project Area of Specialization Artificial IntelligenceProject Summary

What:

With the economic and technological development of cities, environmental pollution problems are arising, such as water, noise, and air pollution.

In particular, air pollution has a direct impact on human health through the exposure of pollutants and particulates, which has increased the interest in air pollution and its impacts among the scientific community. The main causes associated with air pollution are the burning of fossil fuels, agriculture, exhaust from factories and industries, residential heating, and natural disasters.

How:

We will be using computer vision and machine to solve this problem by creating three models

1)image classifier (We predict the AQI from user photos using the following features. These features are extracted by traditional image processing techniques, and combined by a linear model)

2)meteorological model (. Our second model works with images directly as is common in deep learning. Transmission: This describes scene attenuation and the amount of light entering the phone camera after being reflected by air particles)

3)custom image-based model (by combining the result of the above two models we will able to predict the air quality index)

Objectives:

We will predict air quality with the cheapest way possible and by using it people will take care of themselves better than before.

Project Objectives

our system has the following objectives:

• Predict air quality
• In the Cheapest way
• Health precautions
• Free for all
• Simple layout
• AQI focuses on health effects

Project Implementation Method

Following is the product functionality of our app:

• The Mobile Application. This is used to capture images and predict AQI levels. The application processes images on-device.
• TensorFlow Lite is used to power on-device inference, in a small binary size (which is important for download speed, when bandwidth is limited) for the trained machine learning model.
• Firebase. Parameters extracted from the images (described below) are sent to Firebase. Whenever a new user uses the app, a unique ID is created for them. This can be used later to customize the machine-learning model for different geo-locations.
• We train our models here, using these parameters and the PM values from the geo-location.
• ML Kit. Trained models are hosted on ML Kit, and automatically on to the device, then run with TensorFlow Lite.

Benefits of the Project

Portable air quality index metres are very expensive and inconvenient to carry around. Nowadays everyone has a phone and camera in it. We will make it cheaper to check the air quality and this will help to solve this ever-growing world problem.

Technical Details of Final Deliverable

This could be done with pollution sensors — although they can be expensive to deploy at scale. Our goal was to design a reliable and inexpensive air quality estimation solution, accessible to everyone with a smartphone. Our goal is to develop an Android-based mobile application to provide local, real-time air quality estimation using smartphone camera images. 

Final Deliverable of the Project HW/SW integrated systemCore Industry ITOther IndustriesCore Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Good Health and Well-Being for People, Climate ActionRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	70000
mobile	Equipment	2	35000	70000