## Inspiration

In a country where consumerism is the norm, there is no surprise that we produce a large amount of trash very year. On average, 75% of Americans' trash is recyclable but only 25% is ever **actually** recycled. This is a huge gap and we knew instantly that this was the problem we wanted to tackle. Imagine a day where everyone is well informed on recycling processes and identification, our norm would be a more sustainable planet.

## What it does

In this 3D game, you are working in a recycling factory where items come in through the conveyer belt excitedly waiting to be given new purpose. Your job is to take the items off the belt BEFORE they reach the end and place them in their corresponding bins - Compost, Plastics, Paper/Cardboard, and Batteries. You're already a winner by learning these important distinctions but if you want to win at this game, you will need to correctly sort all the items as the belt gets faster and the items start coming in a lot more.

## How we built it

This game was developed using Unity and C#

## Challenges we ran into

The main one we ran into was object collision relationships, more specifically- the intricacies with things like the conveyer belt moving in one direction, the character being able to pick up items, placing them in bins and all without having the in game character interacting with the wrong aspects of an object and randomly spinning or leviatating. This took up a couple of hours and we certainly learned a lot from the random bugs we'd unlock each time we thought we had a solution.

This was also both of our first hackathons so one of the major challenges was adjusting to the fast paced and little to no sleep nature of the event, it was really fun learning from workshops and events but it was difficult to manage our time trying to do as much as possible while still being productive on the project.

## Accomplishments that we're proud of

Submitting a completed project was our main goal and we are extremely proud of achieving this feat. Another main achievement was seeing our idea come to life in the way we wanted without making drastic changes for the sake of time or difficulty, the same idea we went in with was the same that came out.

## What we learned

For one of us, it was the first time working on game development and it was a very fun, interesting and educational journey- I don't think I would've learned and delved into game development as much had it not been for this fast paced event. For the one with prior game development experience this experience was filled with lots of new aspects and problems he had never seen before, especially the game logic- it's a beautiful thing that every game is unique but with unique things come unique and never before seen problems.

## What's next for Compost Chaos

We hope to add complexities to the items that come through the conveyer belt, like a plastic plate with food on top. In this case the user would need to go to the compost been first, go to the optional sink, and then the plastics bin. Another feature we are hoping to explore is having a summary once you leave the game showing the possible new items that came form all that you recycled correctly. Adding more complex items brings the idea of recycling closer to home for most people and with enough playing this game I truly believe people will consciously think about what they're throwing away and what can come from it if recycled.
## Inspirations:

Sustainability, saving the environment. We would love people to know the composites of all the common stuff that we use every day. Throw the trash in the correct trash bins is not the end of stuff, where will they go and how long will they be recycles, and even some of them are not recyclable. Therefore, we are inspired to make an app to help people to learn more.

## Technically speak:

This Android mobile App aims to help people learn about where our waste goes at the end of its life so that people can consume and recycle the products mindfully and correctly. This app has two main features, the first one is. using the hosted phone to detect an object, the AI model will generate a text and response to users about what they had found. Then the users can click the results and see the correct bins the trash should go to and the facts about the objects. The second feature is a recycling game. Users can pick up trashes in the game to gain points. This feature is good for kids to learn how to recycle stuff.

## How we built it:

1. Prototype: Figma
2. The app is developed using Android Studio
3. Deep Learning CNN model
4. Unity

## Challenges:

The biggest challenge that we encountered for this project was to integrate multiple platform sources such as machine learning AI trained over Google Colab and TensorFlow, and Unity game into Android Studio. Another challenge that we have is to have a unified database about recycling knowledge and facts to use as our sources.

## Accomplishments that we're proud of:

In such a time constraint, one of our team members was able to develop a game to let users play and learn about recycling and facts.

## What we learned:

We have learned a lot about using Android studio to create a mobile app and Unity to develop game. We also learned how to collaborate with each other who are from different skills knowledge and backgrounds.

## What's next for RecyclePedia

In the future, we will combine the game feature and the AI detect features to fully finish the development of the mobile app. I will also try React Native crossing-platform platform to refine our design and make it compatible with all the OS.
# Pitch

Every time you throw trash in the recycling, you either spoil an entire bin of recyclables, or city workers and multi-million dollar machines separate the trash out for you. We want to create a much more efficient way to sort garbage that also trains people to sort correctly and provides meaningful data on sorting statistics.

Our technology uses image recognition to identify the waste and opens the lid of the correct bin. When the image recognizer does not recognize the item, it opens all bins and trusts the user to deposit it. It also records the number of times a lid has been opened to estimate what and how much is in each bin.

The statistics would have many applications. Since we display the proportion of all garbage in each bin, it will motivate people to compost and recycle more. It will also allow cities to recognize when a bin is full based on how much it has collected, allowing garbage trucks to optimize their routes. In addition, information about what items are commonly thrown into the trash would be useful to material engineers who can design recyclable versions of those products.

Future improvements include improved speed and reliability, IOTA blockchain integration, facial recognition for personalized statistics, and automatic self-learning.

# How it works

1. Raspberry Pi uses webcam and opencv to look for objects
2. When an object is detected the pi sends the image to the server
3. Server sends image to cloud image recognition services (Amazon Rekognition & Microsoft Azure) and determines which bin should be open
4. Server stores information and statistics in a database
5. Raspberry Pi gets response back from server and moves appropriate bin