Eco sorting

Inspiration

We were inspired by the mounting piles of unsegregated waste in our local communities and the realization that human error is the biggest barrier to effective recycling. We noticed that while people want to recycle, the confusion over what goes where often leads to contamination. We wanted to bridge the gap between good intentions and actual impact by automating the most tedious part of the process. Our goal was to use technology to make sustainability effortless.

What it does

Eco Sorting is an intelligent waste management system that uses computer vision to automatically identify and segregate trash. Users simply place an item in the bin, and our AI determines if it is biodegradable, recyclable, or non-recyclable. It then triggers a mechanical sorter to drop the item into the correct compartment. A companion app tracks the user's recycling stats, providing real-time feedback on their carbon footprint reduction.

How we built it

Here is a draft for your Eco Sorting project submission, designed to fit the standard hackathon length (approx. 5 lines per section).

Inspiration

We were inspired by the mounting piles of unsegregated waste in our local communities and the realization that human error is the biggest barrier to effective recycling. We noticed that while people want to recycle, the confusion over what goes where often leads to contamination. We wanted to bridge the gap between good intentions and actual impact by automating the most tedious part of the process. Our goal was to use technology to make sustainability effortless.

What it does

Eco Sorting is an intelligent waste management system that uses computer vision to automatically identify and segregate trash. Users simply place an item in the bin, and our AI determines if it is biodegradable, recyclable, or non-recyclable. It then triggers a mechanical sorter to drop the item into the correct compartment. A companion app tracks the user's recycling stats, providing real-time feedback on their carbon footprint reduction.

How we built it

Shutterstock We trained a custom Convolutional Neural Network (CNN) using TensorFlow and Keras on a dataset of common waste items. The hardware logic is powered by a Raspberry Pi (or Arduino) interacting with servo motors and a camera module. We used OpenCV for image processing to isolate the object before classification. For the user interface, we built a responsive dashboard using React/Flutter to visualize bin capacity.

Challenges we ran into

Our biggest hurdle was lighting variability; the model initially struggled to identify transparent plastics under low light, requiring us to implement extensive image augmentation. We also faced hardware synchronization issues where the servo motors moved faster than the classification signal, leading to jams. Debugging the physical mechanism took hours of trial and error to ensure the sorting flaps opened at the exact right moment.

Accomplishments that we're proud of

We are incredibly proud of achieving a 92% accuracy rate in detecting plastic bottles and aluminum cans in real-time. Successfully integrating the software model with physical hardware to create a fully working prototype was a huge milestone for us. Additionally, we managed to optimize the model to run efficiently on an edge device without significant latency, proving that AI can be accessible and practical.

What we learned

We learned that data quality is often more important than model complexity; cleaning our dataset improved our results more than tweaking hyperparameters. We gained deep insights into IoT integration and the difficulties of bridging the digital and physical worlds. Most importantly, we learned effective team coordination to merge our hardware and software workstreams under a tight hackathon deadline.

What's next for Eco sorting

We plan to introduce gamification elements to reward users with "Eco Points" for every item correctly recycled, which can be redeemed for coupons. We are also looking into replacing the power source with solar panels to make the bin completely self-sufficient. Finally, we aim to partner with local waste management facilities to scale up the bin size for deployment in public parks and campuses.

Built With

• arduino
• arduino-uno
• c++
• cloud
• firebase
• flask
• google
• google-firebase-(realtime-database-&-auth)-tools:-git
• html5/css3-backend-&-cloud:-flask-(python)
• javascript-ai-&-computer-vision:-tensorflow
• keras
• numpy-hardware:-raspberry-pi-4
• opencv
• pi
• pi-camera-module-frontend:-react.js-(or-flutter-for-mobile)
• python
• raspberry
• react
• servo-motors
• tensorflow
• vs-code