Project Overview
EcoSort is an intelligent waste sorting system that uses machine learning and IoT technology to automatically classify and sort different types of waste. The system leverages AWS cloud services for real-time processing and provides an efficient solution for waste management and recycling.
Machine Learning
AWS
IoT
Computer Vision
Python
Raspberry Pi
Key Features
- Automated Sorting: Real-time waste classification and sorting
- Cloud Processing: AWS-powered ML inference for accurate classification
- IoT Integration: Connected sensors and actuators for automated operation
- Smart Analytics: Waste composition tracking and reporting
- User Interface: Web dashboard for monitoring and control
- Sustainability Focus: Promotes proper recycling and waste reduction
Technical Implementation
The system combines computer vision, machine learning, and IoT technologies to create an intelligent waste sorting solution. Key components include:
- Computer Vision: High-resolution camera captures images of waste items
- ML Model: Custom-trained CNN for waste classification (plastic, paper, metal, organic)
- AWS Services: SageMaker for model deployment, Lambda for processing, S3 for data storage
- IoT Hardware: Raspberry Pi for local processing, sensors for item detection
- Mechanical System: Automated sorting mechanism with servo motors
- Web Dashboard: Real-time monitoring and analytics interface
Machine Learning Pipeline
The ML pipeline includes:
- Data collection and preprocessing from various waste types
- CNN model training with transfer learning using pre-trained architectures
- Model optimization for edge deployment and real-time inference
- Continuous learning from new data to improve accuracy
- AWS SageMaker for scalable model training and deployment
Project Impact
EcoSort addresses critical environmental challenges by:
- Improving recycling rates through automated sorting
- Reducing contamination in recycling streams
- Providing data insights for waste management optimization
- Educating users about proper waste disposal
- Supporting circular economy initiatives
Future Enhancements
Planned improvements include:
- Integration with smart city waste management systems
- Advanced analytics for waste composition trends
- Mobile app for individual user tracking
- Blockchain integration for waste tracking transparency
- Multi-language support for global deployment