Inspiration
Knowing that less than 17 % of the total e-waste generated is efficiently recycled, inspired us to tackle this issue primarily as we wanted to develop a solution catered to the problem of sustainability.
What it does
Autonomously recognises and sorts e-waste, and makes the data public
How we built it
We used an ML model for image recognition and built a web server based on the output. Modeled the solution on CAD, and gave code for the integration using Python and Arduino.
Challenges we ran into
Mechanical Solution which is viable. Most were cost-ineffective. this was a challenge
Accomplishments that we're proud of
Our mechanical solution and accuracy of our training model.
What we learned
There are a lot more problems we run into than at the ideation state.
What's next for Eco-Sort
Scaling into more locations and making the model more accurate. The mechanical solution to be more efficient if possible.
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