Smart Waste Sorting bin

Inspiration Everyday, millions of tons of waste ends up in landfills because of improper segregation at source. Even in advanced cities and university campuses, bins are often contaminated with mixed materials, making recycling inefficient or impossible. As engineers passionate about system we care about circular material economy and sustainability. We think intelligent solutions at source an contribute to solving the large scale problem.

What it does Smart Waste Sorting Bin is an AI-driven waste segregation and data analytics system designed for cities, campuses, and factories. A camera module captures images of waste items as users discard them. Our AI model (built on Google Vertex AI) classifies the waste into key categories — plastic, metal, paper, organic, and e-waste. The bin then guides users via LED indicators or screen prompts to the correct compartment, reducing contamination. The system uploads anonymized data to Google Cloud Storage + BigQuery, where dashboards show: Fill levels per bin Real-time recycling accuracy

How we built it AI Model: Trained a custom computer vision classifier using open waste datasets (TrashNet, TACO) on Vertex AI. Edge Deployment: Optimized the model with TensorFlow Lite to run efficiently on a Raspberry Pi / Jetson Nano with a low-cost camera. Hardware Prototype: Designed a modular bin with servo-controlled flaps for automated redirection and a load cell for weight tracking. Cloud Backend: Built REST APIs with Google Cloud Run and stored data in BigQuery for analysis. Frontend Dashboard: Created a web dashboard with Flask + Google Maps API showing bin locations, status, and segregation metrics. Sustainability Simulation: Used an operations model to estimate waste-stream purity and CO₂ savings per deployment

Challenges we ran into Training of data Achieving real-time inference on low-power edge hardware required quantization and model compression. Waste materials with similar textures (paper vs cardboard) confused the model, so we added a confidence-based reclassification step. Integration between IoT hardware, APIs, and cloud dashboards required careful sync logic to prevent data lag.

What we learned The importance of human factors and UX users trust the bin more when feedback is instant and visual. Collaboration between hardware, software, and industrial systems can lead to scalable urban solutions.

What's next for Smart Waste Sorting bin Expand AI classes to include composite materials, hazardous waste, and region-specific recyclables. Develop a mobile companion app for users to get recycling scores and sustainability insights. Add IoT-based fill sensors to automatically schedule optimal collection routes using Google Maps APIs. Pilot on university campuses and manufacturing plants to gather large-scale data for model improvement.