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♻️ 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

  1. User places waste into bin
  2. Sensors and camera capture data
  3. Edge AI classifies item (plastic, metal, organic, etc.)
  4. Mechanical sorter moves waste to the correct bin
  5. Data (type, timestamp, volume) sent to Django backend
  6. 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)

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

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