Clean Water AI Using AI to detect dangerous bacteria and harmful particles in the water.
Team Member : priyadharshini
June 13, 2019
Why did we build Clean Water AI?
According to World Health Organization, more than 2 billion people are still being affected by contaminated water. And while we think it’s safe in United States, flint, Michigan water crisis has proven to us that even in first world country like US, we still face water safety issues.
Traditional method
Currently the all of the water sensors are chemical based, the most common on being using chemical test strips that are one time use. Making monitoring contamination extremely difficult and exhausting hence events like Flint, MI has happened in the past.
The method of detection
Clean Water AI is IoT device that classifies and detects dangerous bacteria and harmful particles. The system can run continuously in real time. The cities can install IoT devices across different water sources and they will be able to monitor water quality as well as contamination continuously.
What is AI and how to use it
Deep learning has been a pretty big trend for machine learning lately, and the recent success has paved the way to build project like this. We are going to focus specifically on computer vision and image classification in this sample. To do this, we will be building nevus, melanoma, and seborrheic keratosis image classifier using deep learning algorithm, the Convolution Neural Network (CNN) through Caffe Framework.
In this article we will focus on Supervised learning, it requires training on the server as well as deploying on the edge. Our goal is to build a machine learning algorithm that can detect contamination images in real time, this way you can build your own AI based contamination classification device.
Our application will include 2 parts, the first part is training, which we will be using different sets of cancer image database to train a machine learning algorithm (model) with their corresponding labels. The second part is deploying on the edge, which uses the same model we’ve trained and running it on an Edge device, in this case Movidius Neural Computing Stick.