ThreeR

Inspiration

A survey conducted in USA tell us that only half of Americans claim to recycle everyday, while 13 percent are willing to admit that they never do.Of those who recycle ,their diligence and consistency is unknown.Motivating consumers to act on their increasing sense of responsibility toward the environment will require sustained, combined efforts tailored to different communities taking into account demographics, social mores, geographic conditions and other factors.

Surprisingly, there is some agreement about how to increase recycling participation. In a survey of recycling coordinators across the country the Government Accounting Office (GAO) discovered three practices most effective: 1) make recycling convenient and easy, 2) offer financial incentives, and 3) conduct public education and outreach.

What it does

And so comes our application focusing on these three major factors. It’s an android application through which you can recycle item from your house with just click of a button make the recycling process more easier than ever. Not only that It also has data and machine learning model to educate people on different types of recycling materials.The machine learning model made by “Amelia” takes an image from the user and then gives the result whether the material can be recycled or not. It also has the financial incentives provided on recycling stuff based on the type of recycling material.The incentives are given in form of virtual coins which can be used by the user to buy products on the application hence making the application great source to generate revenue.

It also has its on community where ppl can sell there old usable item to others hence reusing the stuff again.

How we built it

The application is build with Java and kotlin .The machine learning model is tflite .For database we have used Firebase.Authentication is achieved with auth0 api.

Challenges we ran into

Being as huge concept as it is we had to compress and stuck to just simple implementation with the given time frame.The 24 hour time frame was the biggest challenge we had.And not only that one of the team member's laptop broke in mist of the hackathon so it created some hard time for us to complete it in time.

Accomplishments that we're proud of

Being able to complete the whole project in almost 24 hours with all the functionalities we wanted ,It was the greatest achievement.

What we learned

We learned a lot through this project from team work to machine learning implementation on android. It was a roller coster ride from a moment where we totally lost hopes of being able to finish it to being finally able to present it .

What's next for ThreeR

Application still needs lot of improvement, machine learning can also use some improvement to make it give better user experience.

Built With

• auth0
• firebase
• java
• kotlin
• machine-learning
• tenserflow