## Inspiration

Let's be honest. Sometimes, we have a paper cup, and we look at both the trash can and the recycling bin. We might throw the paper cup away in the trash because the recycling is just a little further. I'm definitely a culprit.

Our team set out to invent a fun perception of recycling by creating a digital pet that can only be cared for through recycling verified by Gemini's ML image recognition.

Something as simple as a tiny pet, backed by the complexity of Gemini, makes me take that extra step to throw away that paper cup into the recycling bin—to make sure my pet survives and keeps the world green.

## What it does

Take a photo of yourself recycling an item, and using image recognition, Gemini checks if it is a valid photo. After a successful photo, you can feed your digital pet a ton of snacks making your pet get progressively get bigger and bigger...

## How we built it

We began sketching each page to track the user's potential dopamine flow. Usually, recycling is seen as an inconvenience, especially since they are outnumbered by regular trashcans 2-to-1. We wanted the users to associate with recycling positively, so we created a pet for them to take care of. This emotional investment changes the idea of recycling from a burden to an opportunity to care for your digital pet.

## Challenges we ran into

Integrating Gemini API was difficult at the start, but it was smooth the second time we tried. Easily viewable color scheme. Staying awake.

## Accomplishments that we're proud of

'Baby Chester' turning out as a cute pet
The quick start to building SnackSnap making the rest of the days less stressful.

## What we learned

Shipping Fast!!! Pushing the limits of getting deep work done. The excitement of working with Gemini. Working with each other's strengths. Trust in each other.

## What's next for SnackSnap

Your pet can have babies. Discover new verticals. Future integration into spatial computing where there is virtually no friction for the user and we can auto-track their recycling activity.
## Inspiration

Americans waste about 425 beverage containers per capita per year in landfill, litter, etc. Bottles are usually replaced with cans and bottles made from virgin materials which are more energy-intensive than recycled materials. This causes emissions of a host of toxics to the air and water and increases greenhouse gas emissions.

The US recycling rate is about 33% while that in stats what have container deposit laws have a 70% average rate of beverage recycling rate. This is a significant change in the amount of harm we do to the planet.

While some states already have a program for exchanging cans for cash, EarthCent brings some incentive to states to make this a program and something available. Eventually when this software gets accurate enough, there will not be as much labor needed to make this happen.

## What it does

The webapp allows a GUI for the user to capture an image of their item in real time. The EarthCents image recognizer recognizes the users bottles and dispenses physical change through our change dispenser. The webapp then prints a success or failure message to the user.

## How we built it

Convolutional Neural Networks were used to scan the image to recognize cans and bottles.
Frontend and Flask presents a UI as well as processes user data.
The Arduino is connected up to the Flask backend and responds with a pair of angle controlled servos to spit out coins.
Change dispenser: The change dispenser is built from a cardboard box with multiple structural layers to keep the Servos in place. The Arduino board is attached to the back and is connected to the Servos by a hole in the cardboard box.

## Challenges we ran into

Software: Our biggest challenge was connect the image file from the HTML page to the Flask backend for processing through a TensoFlow model. Flask was also a challenge since complex use of it was new to us.
Hardware: Building the cardboard box for the coin dispenser was quite difficult. We also had to adapt the Servos with the Arduino so that the coins can be successfully spit out.

## Accomplishments that we're proud of

With very little tools, we could build with hardware a container for coins, a web app, and artificial intelligence all within 36 hours. This project is also very well rounded (hardware, software, design, web development) and let us learn a lot about connecting everything together.

## What we learned

We learned about Arduino/hardware hacking. We learned about the pros/cons of Flask vs. using something like Node.js. In general, there was a lot of light shed on the connectivity of all the elements in this project. We both had skills here and there, but this project brought it all together. Learned how to better work together and manage our time effectively through the weekend to achieve as much as possible without being too overly ambitious.

## What's next for EarthCents

EarthCents could deposit Cryptocurrency/venmo etc and hold more coins. If this will be used, we would want to connect it to a weight to ensure that the user exchanged their can/bottle. More precise recognition.
## Inspiration

All of our parents like to recycle plastic bottles and cans to make some extra money, but we always thought it was a hassle. After joining this competition and seeing sustainability as one of the prize tracks, we realized it would be interesting to create something that makes the recycling process more engaging and incentivized on a larger scale.

## What it does

We gamify recycling. People can either compete against friends to see who recycles the most, or compete against others for a prize pool given by sponsors (similar to how Kaggle competitions work). To verify if a person recycles, there's a camera section where it uses an object detection model to check if a valid bottle and recycling bin are in sight.

## How we built it

We split the project into 3 major parts. The app itself, the object detection model, and another ML model that predicted how trash in a city would move so users can move with it to pick up the most amount of trash. We implemented an object detection model, where we created our own dataset of cans and bottles at PennApps with pictures around the building, and used Roboflow to create the dataset. Our app was created using Swift, and it was inspired by a previous GitHub that deployed a model of the same type as ours onto IOS. The UI was designed using Figma. The ML model that predicted the movement of trash concentration was a CNN that had a differential equation as a loss function which had better results than just the vanilla loss functions.

## Challenges we ran into

None of us had coded an app before, so it was difficult doing anything with Swift. It actually took us 2 hours just to get things set up and get the build running, so this was for sure the hardest part of the project. We also ran into problems finding good datasets for both of the models, as they were either poor quality or didn't have the aspects that we wanted.

## Accomplishments that we're proud of

Everyone on our team specializes in backend, so with limited initial experience in frontend, we're especially proud of the app we’ve created—it's our first time working on such a project. Integrating all the components posed significant challenges too. Getting everything to work seamlessly, including the CNN model and object detection camera within the same app, required countless attempts. Despite the challenges, we've learned a great amount throughout the process and are incredibly proud of what we've achieved so far.

## What we learned

How to create an IOS app, finding datasets, integrating models into apps.

## What's next for EcoRush

A possible quality change to the app would be to find a way to differentiate bottles from each other so people can't "hack" the system. We are also looking for more ways to incentivize people to recycle litter they see everyday other than with money. After all, our planet would be a whole lot greener if every citizen of Earth does just a small part!