Recycle-it

Inspiration

Picture a world where recycling is seamless, waste is minimized, and every step we take moves us closer to a sustainable future. Recycle-it is here to make that vision a reality. Powered by cutting-edge machine learning, it makes waste sorting effortless, reducing contamination and ensuring more accurate recycling that is easily accessible for anyone with minimal effort. Whether you're at home, in a business, or managing a community, Recycle-it empowers you to make a tangible difference—transforming the way we handle waste, and inspiring a greener tomorrow, one smart decision at a time.

What it does

Ultrasonic Sensors and Display Control: Optimizes the user interface for an intuitive and eco-friendly experience by preventing unnecessary energy consumption.
Raspberry Pi and Python for Frontend: Uses a Raspberry Pi as a microprocessor with pre-scripted Python code to open our web interface. The system remains on to bypass booting time, ensuring a seamless user experience.
Machine Learning for Object Classification: Classifies and detects lead contamination, relaying the information to the frontend to guide users on sustainable disposal methods.
User-Friendly Frontend: The key strength is its accessibility with minimal user input effort, making it easy for anyone to adopt and contribute to positive environmental change.

How we built it

Hardware Frameworks: Utilized ultrasonic sensors and a Raspberry Pi for efficient data processing and user interaction.
Machine Learning Model: Trained a custom model to classify waste materials and detect contaminants like lead.
Frontend Development: Built a responsive and intuitive web interface using Python and Flask.
Integration: Combined hardware, machine learning, and frontend components into a cohesive system.

Technologies Used

Backend: Python, Flask, TensorFlow (for machine learning)
Frontend: HTML, CSS, JavaScript
Hardware: Raspberry Pi, ultrasonic sensors
Cloud Platforms: Google Cloud, AWS (for model training and deployment)
Collaboration Tools: GitHub, Discord

Challenges we ran into

Lacking and Biased Dataset: Limited datasets made it challenging to train the machine learning model for accurate classification.
Integration of Machine Learning Model: Allocating classification criteria to predict contaminations required significant fine-tuning.
Hardware Limitations: Ensuring the Raspberry Pi could handle real-time processing without overheating was a hurdle.

Accomplishments that we're proud of

Designed a cost-effective, environmentally-friendly system that aligns with our sustainability goals.
Achieved complete separation of the frontend and backend, simplifying manufacturing and scalability.
Created an accessible system that minimizes user effort, encouraging widespread adoption.
Delivered a functional prototype and effective demo within the hackathon timeline.

What we learned

Machine Learning and Data: Training accurate models requires large, diverse datasets and significant computational resources.
Hardware Design: Proper heat dissipation is critical, even for low-power devices like the Raspberry Pi.
User Experience: Simplifying the interface and minimizing user effort are key to driving adoption.

What's next for Recycle-it

Improve Dataset Quality: Collect more diverse and unbiased data to enhance the machine learning model's accuracy.
Collaboration and Sponsorship: Partner with municipal governments (e.g., City of Edmonton) to make Recycle-it widely accessible.
Expand Classification Categories: Add more waste categories to handle a broader range of materials.
Upgrade Hardware: Use higher-spec servers to handle increased traffic and improve performance.
Interactive Audio Feedback: Add audio cues to make the system more engaging and user-friendly.
Enhance Ventilation: Improve hardware design to prevent overheating during prolonged use.
Community Engagement: Launch awareness campaigns to promote smarter recycling practices.