♻️ SmartBin: Intelligent Waste Sorting System
Welcome to SmartBin, a cutting-edge sustainability project powered by IoT, AI, and Django. SmartBin is designed to detect, classify, and sort waste using intelligent sensors and machine learning models. Our goal: improve recycling efficiency, reduce landfill waste, and enable smart city waste monitoring. 🌍
🚀 Project Overview
SmartBin is an IoT-integrated waste management system that:
- 🧠 Automatically detects and classifies trash (plastic, metal, organic, paper)
- ⚙️ Sorts waste into appropriate bins using mechanical actuators
- ☁️ Syncs data to a Django-powered backend with real-time dashboards
- 📈 Analyzes trends for smarter, greener decisions
Bridging the gap between sustainability and smart technology.
🔧 Tech Stack
| Layer |
Technology Used |
| 💡 Sensors |
IR Sensor, Moisture Sensor, Ultrasonic, Load Cell |
| 📦 Microcontroller |
Raspberry Pi / Arduino |
| 🧠 Intelligence |
TensorFlow Lite / Edge AI models |
| 🌐 Connectivity |
Wi-Fi, MQTT, HTTP (Django REST Framework) |
| ⚙️ Backend |
Django, Django REST Framework |
| 📊 Dashboard |
HTML5, CSS3, Bootstrap, Chart.js |
| ☁️ Cloud |
AWS / Firebase / PostgreSQL |
| 🔌 Power Supply |
Battery / Solar (optional) |
🧠 How It Works
- User places waste into bin
- Sensors and camera capture data
- Edge AI classifies item (plastic, metal, organic, etc.)
- Mechanical sorter moves waste to the correct bin
- Data (type, timestamp, volume) sent to Django backend
- Dashboard displays real-time insights
📸 Features
- 🧠 AI-Based Classification using sensor + image data
- ♻️ Automatic Sorting via servo/motor mechanisms
- 📡 IoT Communication between microcontroller and server
- 🖥 Django Admin Panel to monitor bin health and usage
- 📊 Waste Analytics Dashboard with filtering and export
- 🔔 Alerts & Notifications for full bin, errors, or abnormal data
🌿 Sustainability Impact
- ✅ Increases recycling accuracy
- ✅ Reduces labor in waste sorting
- ✅ Provides data for behavior change
- ✅ Enables smart city waste analytics
🛠️ Setup Instructions
1. Hardware Setup
- Connect sensors and motors to microcontroller (e.g., Raspberry Pi)
- Load AI model (e.g., TensorFlow Lite) onto edge device
- Connect device to Wi-Fi for API communication
2. Backend (Django)
```bash
git clone https://github.com/your-org/smartbin.git
cd sustainability
python -m venv env
source env/bin/activate
pip install -r requirements.txt
python manage.py migrate
python manage.py runserver