Theme Covered:

The themes covered in this project include post pandemic restoration for both the environment, small buisnesses, and personal finance! The app pitched uses an extensivly trained AI system to detect trash and sort it to the proper bin from your smartphone. While using the app, users will be incentivized to use the app and recover the environment through the opportunity to earn points, which will be redeemable in partnering stores.

Problem Statment:

As our actions continue to damage the environment, it is important that we invest in solutions that help restore our community in more sustainable practices. Moreover, an average person creates over 4 pounds of trash a day, and the EPA has found that over 75% of the waste we create are recyclable. As garbage sorting is so niche from town-to-town, students have reportable agreed to the difficulty of accurately sorting garbage, thus causing this significant misplacement of garbage.

Our passion to make our community globally and locally more sustainable has fueled us to use artificial intelligence to develop an app that not only makes sorting garbage as easy as using Snapchat, but also rewards individuals for sorting their garbage properly.

For this reason, we would like to introduce Recoin. This intuitive app allows a person to scan any product and easily find the bin that the trash belongs based off their location. Furthermore, if they attempt to sell their product, or use our app, they will earn points which will be redeemable in partnering stores that advocate for the environment. The more the user uses the app, the more points they receive, resulting in better items to redeem in stores. With this app we will not only help recover the environment, but also increase sales in small businesses which struggled during the pandemic to recover.

About the App:

Incentive Breakdown:

Please note that these expenses are estimated expectations for potential benefit packages but are not defined yet.

We are proposing a $1 discount for participating small businesses when 100 coffee/drink cups are returned to participating restaurants. This will be easy for small companies to uphold financially, while providing a motivation for individuals to use our scanner.

Amazon costs around $0.5 to $2 on packaging, so we are proposing that Amazon provides a $15 gift card per 100 packages returned to Amazon. As the 100 packages can cost from $50 to $200, this incentive will save Amazon resources by 5 to 100 times the amount, while providing positive public perception for reusing.

As recycling plastic for 3D filament is an up-and-coming technology that can revolutionize environment sustainability, we would like to create a system where providing materials for such causes can give the individuals benefits.

Lastly, as metals become more valuable, we hope to provide recyclable metals to companies to reduce their expenses through our platform.

The next steps to this endeavor will be to provide benefits for individuals that provide batteries and electronics with some sort of incentive as well.

User Interface:

Technological Specifics and Next Steps:

Frontend

We used to React.JS to develop components for the webcam footage and capture screen shots. It was also utilized to create the rest of the overall UI design.

Backend

Waste Detection AI:

On Pytorch, we utilized an open-source trash detection AI software and data, to train the trash detection system originally developed by IamAbhinav03. The system utilizes over 2500 images to train, test, and validate the system. To improve the system, we increased the number of epochs to 8 rather than 5 (number of passes the training system has completed). This allowed the accuracy to increase by 4% more than the original system. We also modified the test/train ratio and split amounts to 70%, 10%, and 20% respectively, as more prominent AI studies have found this distribution to receive the best results.

Currently, the system is predicted to have a 94% accuracy, but in the future, we plan on using reinforcement learning in our beta testing to continuously improve our algorithm. Reinforcement learning allows for the data to be more accurate, through learning from user correction. This will allow AI to become more precise as it gains more popularity.

A flask server is used to make contact with the waste detection neural network; an image is sent from the front end as a post request, the Flask server generates a tensor and runs that through the neural net, then sends the response from the algorithm back to the front end. This response is the classification of the waste as either cardboard, glass, plastic, metal, paper or trash.

Possible next steps:

By using Matbox API and the Google Suite/API, we will be creating maps to find recycling locations and an extensively thorough Recoin currency system that can easily be transferred to real time money for consumers and businesses (as shown in the user interface above).

Stakeholders:

After the completion of this project, we intend to continue to pursue the app to improve our communities’ sustainability. After looking at the demographic of interest in our school itself, we know that students will be interested in this app, not only from convenience but also through the reward system. Local cafes and Starbucks already have initiatives to improve public perspective and support the environment (i.e., using paper straws and cups), therefore supporting this new endeavor will be an interest to them. As branding is everything in a business, having a positive public perspective will increase sales.

Amazon:

As Amazon continues to be the leading online marketplace, more packages will continue to be made, which can be detrimental to the world's limited resources. We will be training the UI to track packages that are Amazon based. With such training, we would like to be able to implement a system where the packaging can be sent back to Amazon to be reused for credit. This will allow Amazon to form a more environmentally friendly corporate image, while also saving on resources.

Small Businesses:

As the pandemic has caused a significant decline in small business revenue, we intend to mainly partner with small businesses in this project. The software will also help increase small business sales as by supporting the app, students will be more inclined to go to their store due to a positive public image, and the additive discounts will attract more customers. In the future, we wish to train AI to also detect trash of value (i.e.. Broken smartphones, precious metals), so that consumers can sell it in a bundle to local companies that can benefit from the material (ex: 3D-printing companies that convert used plastic to filament)

Timeline:

The following timeline will be used to ensure that our project will be on the market as soon as possible:

##About the Team: We are first and second year students from Queen's University who are very passionate about sustainbility and designing of innovative solutions to modern day problems. We all have the mindset to give any task our all and obtain the best results. We have a diverse skillset in the team and throughout the hackathon we utlized it to work efficienty. We are first time hackathoners, so even though we all had respective expierence in our own fields, this whole expierence was very new and educationally rewarding for us. We would like to thank the organisers and mentors for all thier support and for organizing the event.