EYE-RecAIcle

Recycling using Machine Learning and IoT - An intelligent waste sorting system.

Overview

Developed an innovative recycling system that uses computer vision and machine learning to automatically identify and sort recyclable materials. The project combines AI, IoT hardware, and physical computing to create a practical solution for improving recycling efficiency.

Key Features

• AI-Powered Classification: Machine learning model to identify recyclable materials
• Real-time Detection: Computer vision for instant material recognition
• IoT Integration: Arduino-based hardware for automated sorting
• Visual Feedback: LED lights indicate sorting status and material type
• Multi-material Support: Classifies plastics, paper, glass, and metal
• Data Logging: Tracks recycling statistics and patterns

Tech Stack

• AI/ML: Python, TensorFlow/PyTorch, OpenCV
• Hardware: Arduino, C++
• IoT Components: Sensors, LED lights, servo motors
• Additional Tools: Unity, C#, Blender (for visualization)
• Computer Vision: Image classification and object detection

Technical Highlights

• Trained custom CNN model for recyclable material classification
• Achieved 90%+ accuracy in material identification
• Implemented real-time image processing pipeline with OpenCV
• Built Arduino-based sorting mechanism with servo motors
• Created LED feedback system for user interaction
• Developed Unity visualization dashboard for recycling statistics
• Integrated sensors for material detection and bin capacity monitoring

Results

• Successfully demonstrated automated recycling sorting
• Improved sorting accuracy compared to manual methods
• Reduced contamination in recycling streams
• Created scalable solution applicable to various environments
• Published project showcasing AI + IoT integration

Challenges & Solutions

Challenge: Training accurate ML model with limited dataset
Solution: Used data augmentation and transfer learning from pre-trained models

Challenge: Real-time performance on embedded hardware
Solution: Optimized model architecture and used model quantization

Challenge: Reliable material detection under various lighting
Solution: Implemented adaptive thresholding and color normalization

Challenge: Hardware integration with ML system
Solution: Created efficient communication protocol between Python and Arduino

Impact

This project demonstrates the practical application of AI and IoT in solving environmental challenges. By automating the recycling sorting process, EYE-RecAIcle shows how technology can improve sustainability and reduce waste management costs.