Inspiration
The growing concern about waste management and environmental impact was a major inspiration for creating EcoSort. The need for more eco-conscious waste disposal methods and user-friendly tools to educate and encourage people to recycle correctly motivated us to develop a solution. By leveraging AI and computer vision, we aim to simplify the recycling process and ensure more materials are properly sorted and disposed of.
What it does
EcoSort allows users to capture images of waste items through their mobile devices or upload images, and it uses an AI-powered model to identify whether the item is recyclable or not. The app provides immediate feedback, informing users how to sort the item properly. This promotes better recycling habits and contributes to reducing contamination in recycling bins.
How we built it
We built EcoSort using React with Tailwind CSS for the front end and integrated a camera functionality for image capture. The AI model, powered by TensorFlow and hosted via an external API, processes the images to determine recyclability. We used Axios for API calls, handling image data in base64 format. The app is mobile-first, ensuring a seamless experience on mobile devices.
Challenges we ran into
One of the main challenges was integrating the camera feature with web technologies in a way that worked across all devices. Additionally, ensuring that the AI model was both fast and accurate required a lot of fine-tuning. Another challenge was handling the large image data and converting it to a format that the model could process efficiently without slowing down the user experience.
Accomplishments that we're proud of
We’re proud of building a fully functioning app that integrates AI in a meaningful way to promote environmental sustainability. Our responsive design ensures a smooth user experience on both mobile and desktop. Successfully deploying and integrating the camera feature was another major win for us, as it enhances the user’s interaction with the app.
What we learned
We learned a lot about integrating AI models into web applications and handling image data efficiently. Additionally, we gained deeper insights into responsive design, camera APIs, and how to optimize the performance of our web app. The importance of user experience when dealing with large datasets was also a key takeaway.
What's next for EcoSort
In the near future, we’re focused on improving the AI model’s accuracy by feeding it more data and expanding its understanding of different types of waste. We also want to build a backend that tracks users' recycling habits, so the app can give them personalized tips and feedback on how they're doing. One feature we're really excited about is introducing a fact system. For example, when you recycle something like metal, the app will tell you how recycling that specific material helps the environment, or how it’s reused in new products. This will give users a better understanding of their impact.
Looking further ahead, we want to explore building smart hardware, like cameras in trash bins, that could automatically detect and sort waste as it’s thrown away. It’s a long-term goal, but for now, our focus is on making the app a tool that helps users understand how their everyday actions contribute to a healthier planet.
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