Inspiration The inspiration for the Smart Recycling Bin came from the ongoing problem of waste mismanagement and contamination in recycling bins. Many people struggle to identify recyclable materials versus non-recyclable waste, which leads to contamination and inefficiency in recycling facilities. We wanted to create a solution that educates and empowers individuals to make sustainable choices in waste disposal.
What It Does The Smart Recycling Bin is a sensor-equipped bin that uses image recognition to identify items placed inside. It categorizes the waste as recyclable, compostable, or general waste and provides immediate feedback on a connected app. The app suggests disposal methods, educates users on the environmental impact of their waste, and tracks waste disposal habits to encourage more sustainable behaviors.
How We Built It Hardware: We used a camera connected to a Raspberry Pi that scans each item as it's thrown into the bin. Additional sensors, like weight sensors, help provide data on the type and quantity of waste. Software: We trained an image recognition model using a dataset of common household waste items, utilizing a pre-trained model for faster deployment. The model runs on the Raspberry Pi, and data is sent to a mobile app via Bluetooth or Wi-Fi. Mobile App: The app was built using Flutter (or React Native) and connects to the bin, displaying item details and recycling suggestions in real time. It tracks user interactions, educates them with waste management tips, and offers rewards for positive recycling behavior. Challenges We Ran Into Image Recognition Accuracy: Achieving high accuracy for recognizing a wide variety of waste items was challenging, especially with objects that are visually similar. Hardware Integration: Integrating the camera and sensors with the Raspberry Pi while maintaining a stable connection to the app required some trial and error. User Engagement: Creating an engaging app experience that doesn’t overwhelm users with information but still promotes behavior change took some fine-tuning. Accomplishments That We're Proud Of High Recognition Accuracy: We were able to achieve over 80% accuracy in identifying common waste items, which is a strong start for a prototype. Functional Prototype: The Smart Recycling Bin successfully recognizes and categorizes waste in real time, providing users with immediate feedback. User-Friendly App: We created a mobile app that’s intuitive and visually appealing, making it easier for users to engage and learn about sustainable waste practices. What We Learned Machine Learning Techniques: Training a machine learning model with a small, specific dataset taught us a lot about optimizing for accuracy in image recognition. User-Centered Design: We learned that user engagement is crucial for an educational tool like this. Iterating on the app design with feedback allowed us to make it more user-friendly and engaging. Hardware-Software Integration: Working with hardware components alongside software development improved our understanding of IoT (Internet of Things) and the challenges of real-time processing. What's Next for Smart Recycling Bin Model Improvement: We plan to expand the image recognition model to include a larger dataset, making the bin more accurate and versatile. Gamification: Adding features like reward points or a leaderboard to further encourage sustainable behavior and friendly competition among users. Community Integration: Creating a community platform where users can share tips, challenges, and ideas related to recycling and sustainability. Smart City Integration: Partnering with local governments or waste management companies to deploy these bins in public spaces like parks, schools, and shopping centers.
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