A revolutionary IoT-based waste management solution that leverages ESP-32 CAM modules and computer vision to automatically detect and sort waste with 80% accuracy, ensuring proper segregation and reducing environmental impact.
This intelligent waste management system combines computer vision, IoT communication, and automated actuation to solve the critical problem of improper waste segregation. The system automatically detects waste type anomalies (e.g., plastic in organic waste bins) and triggers appropriate responses to maintain proper waste sorting.
- 🎯 Smart Detection: 80% accuracy in identifying incorrect waste placement
- 🔄 Real-time Communication: ESP-NOW protocol for instant data transmission
- 🤖 Automated Response: Servo-controlled bin lids for smart waste sorting
- 📊 Environmental Monitoring: Temperature, humidity, and methane gas detection
- 👤 Proximity Detection: Ultrasonic sensor detects user approach for automatic operation
- 📏 Bin Level Monitoring: Real-time waste level tracking with ultrasonic measurement
- ☁️ Cloud Integration: Real-time data sync with Firebase for remote monitoring
- 📱 React Web App: Comprehensive dashboard for system monitoring and analytics
ESP-32 CAM Module (Sender) → ESP-32 Main Board (Receiver)
↓ ↓
Object Detection → Decision Making
↓ ↓
Data Transmission → Servo Control
- Detection Layer: ESP-32 CAM continuously monitors waste bins
- Communication Layer: ESP-NOW protocol ensures reliable data transfer
- Decision Layer: Main board processes detection data with sensor inputs
- Action Layer: Servo motors actuate appropriate bin lids
| Component | Quantity | Purpose |
|---|---|---|
| ESP-32 CAM Module | 1+ | Object detection and image processing |
| ESP-32 Main Board | 1 | Central processing and control |
| Servo Motors | 2+ | Automated bin lid control |
| DHT22 Sensor | 1 | Temperature & humidity monitoring |
| TGS2611 Gas Sensor | 1 | Methane gas detection |
| Ultrasonic Sensor (HC-SR04) | 2 | Proximity detection & bin level monitoring |
- Power Supply: 5V/3.3V power adapters
- Connecting Wires: Jumper wires and breadboard
- Mounting Hardware: Enclosures and mounting brackets
- MicroSD Card: For ESP-32 CAM (optional, for data logging)
- Arduino IDE (v1.8.19 or later)
- ESP32 Board Package (v2.0.0 or later)
ESP32 Camera Library
ESP-NOW Communication Library
Servo Library
DHT Sensor Library
NewPing Library (for ultrasonic sensors)
Firebase ESP Client Library
WiFi Library
# Install ESP32 board package in Arduino IDE
# Go to File → Preferences → Additional Board Manager URLs
# Add: https://dl.espressif.com/dl/package_esp32_index.json-
Connect ESP-32 CAM Module
- Power: 5V and GND
- Programming: Connect FTDI programmer for code upload
-
Connect ESP-32 Main Board
- Servo Motors: Connect to designated GPIO pins
- Sensors: DHT22 (data pin), TGS2611 (analog pin)
- Ultrasonic Sensors: HC-SR04 for proximity and bin level detection
- Power all components
// Upload MAC_Address code to Main Board
// Note down the MAC address from Serial Monitor
// Example: C0:49:EF:D2:B7:E8
// In Sender_main code, update broadcast address:
uint8_t broadcastAddress[] = {0xC0, 0x49, 0xEF, 0xD2, 0xB7, 0xE8};
- Flash
Sender_main.inoto ESP-32 CAM - Flash
Receiver_main.inoto ESP-32 Main Board
// Configure WiFi and Firebase in Receiver_main
#define WIFI_SSID "your-wifi-network"
#define WIFI_PASSWORD "your-wifi-password"
#define FIREBASE_HOST "your-project-default-rtdb.firebaseio.com"
#define FIREBASE_AUTH "your-database-secret-key"
# Navigate to react-dashboard folder
cd react-dashboard
# Install dependencies
npm install
# Configure Firebase credentials in src/config/firebase.js
# Start development server
npm start- Power on both ESP-32 modules
- Verify WiFi connection and Firebase connectivity
- Place test objects in camera view
- Monitor serial output for detection results
- Check Firebase database for real-time data updates
- Access React dashboard to view live system data
- Verify servo motor responses and sensor readings
Waste_Management_System/
├── Sender_main/
│ ├── Sender_main.ino # ESP-32 CAM detection code
│ └── camera_config.h # Camera configuration
├── Receiver_main/
│ ├── Receiver_main.ino # Main board control code
│ └── sensor_config.h # Sensor configurations
├── MAC_Address/
│ └── MAC_Address.ino # MAC address utility
├── firebase_integration/
│ ├── firebase_config.h # Firebase configuration
│ └── data_uploader.ino # Cloud data upload functions
├── react-dashboard/
│ ├── src/
│ │ ├── components/ # React components
│ │ ├── pages/ # Dashboard pages
│ │ ├── config/firebase.js # Firebase config
│ │ └── App.js # Main app component
│ ├── package.json # Dependencies
│ └── README.md # React app setup guide
└── README.md # This file
ESP-32 CAM → Component
VCC (5V) → Power Supply +
GND → Power Supply -
IO0 → GND (for programming)
GPIO Pin → Component
GPIO 18 → Servo Motor 1 (Signal)
GPIO 19 → Servo Motor 2 (Signal)
GPIO 4 → DHT22 (Data)
GPIO 34 → TGS2611 (Analog Out)
GPIO 5 → Ultrasonic Sensor 1 (Trig) - Proximity Detection
GPIO 16 → Ultrasonic Sensor 1 (Echo) - Proximity Detection
GPIO 17 → Ultrasonic Sensor 2 (Trig) - Bin Level Monitoring
GPIO 21 → Ultrasonic Sensor 2 (Echo) - Bin Level Monitoring
// Adjust detection sensitivity
#define DETECTION_THRESHOLD 0.8 // 80% confidence
#define FRAME_RATE 5 // FPS for processing
// TGS2611 Methane Detection
#define METHANE_THRESHOLD 400 // PPM threshold for alert
#define GAS_SAMPLE_RATE 1000 // Sample every 1 second
// Ultrasonic Sensor Settings
#define PROXIMITY_THRESHOLD 50 // cm - user detection range
#define BIN_FULL_THRESHOLD 10 // cm - bin considered full
#define MAX_BIN_DEPTH 80 // cm - total bin depth
// ESP-NOW Configuration
#define WIFI_CHANNEL 1
#define MAX_RETRY_COUNT 3
// Servo positions
#define SERVO_CLOSED 0 // Closed position
#define SERVO_OPEN 90 // Open position
#define RESPONSE_DELAY 2000 // 2 seconds open time
- Image Capture: ESP-32 CAM captures frames at regular intervals
- Object Recognition: Built-in AI model identifies waste types
- Confidence Check: Only processes detections above 80% confidence
- Data Packaging: Creates data packet with detection results
- ESP-NOW Setup: Establishes peer-to-peer communication
- Data Transmission: Sends detection data to main board
- Acknowledgment: Confirms successful data reception
- Error Handling: Retries failed transmissions
- Data Analysis: Processes detection data with sensor readings
- Proximity Check: Monitors user approach with ultrasonic sensor
- Bin Level Assessment: Checks waste level before operation
- Anomaly Detection: Identifies incorrect waste placement
- Safety Monitoring: Checks methane levels for safety alerts
- Action Trigger: Determines appropriate response based on all inputs
- Servo Control: Actuates corresponding bin lid when safe and appropriate
Solution:
- Check camera focus and positioning
- Verify lighting conditions
- Adjust detection threshold in code
- Ensure objects are within detection range
Solution:
- Verify MAC addresses are correct
- Check ESP-NOW channel settings
- Ensure both devices are powered
- Reduce distance between modules
Solution:
- Check VCC (5V) and GND connections
- Verify Trig and Echo pin assignments
- Ensure sensors are mounted properly (no obstructions)
- Test with simple distance measurement code
- Check for electromagnetic interference
Solution:
- Check WiFi credentials and network connectivity
- Verify Firebase database URL and authentication key
- Ensure Firebase project has Realtime Database enabled
- Check firewall settings for outbound connections
- Monitor Firebase usage quotas and limits
Solution:
- Verify Firebase configuration in React app
- Check browser console for JavaScript errors
- Ensure Firebase security rules allow read access
- Test Firebase connection with simple queries
- Clear browser cache and reload application
Solution:
- Check servo connections and power supply
- Verify GPIO pin assignments
- Test servos with simple sweep code
- Ensure adequate power supply (5V, 2A minimum)
Solution:
- Allow TGS2611 warm-up time (24-48 hours for accuracy)
- Check analog pin connection (3.3V compatible)
- Verify sensor calibration in clean air
- Ensure proper ventilation around sensor
// Enable debug mode in both modules
#define DEBUG_MODE 1
// Serial monitor will show:
// - Detection confidence scores
// - Communication status
// - Servo position feedback
// - All sensor readings (DHT22, TGS2611, Ultrasonic)
// - Proximity detection status
// - Bin fill level percentage
// - Firebase connection status
// - Data upload success/failure messages
| Metric | Value | Target |
|---|---|---|
| Detection Accuracy | 80% | 85% |
| Response Time | <2 seconds | <1 second |
| Communication Range | 50 meters | 100 meters |
| Battery Life | 8 hours | 12 hours |
| False Positive Rate | 15% | <10% |
| Proximity Detection Range | 50 cm | 100 cm |
| Bin Level Accuracy | ±2 cm | ±1 cm |
| Methane Detection Range | 0-5000 PPM | 0-10000 PPM |
| Cloud Data Sync | 5 seconds | 2 seconds |
| Dashboard Load Time | 3 seconds | 1 second |
- 🧠 Advanced AI Models: Custom-trained neural networks for 95% accuracy
- 🔄 Multi-bin Synchronization: Coordinate multiple waste management units
- 📊 Advanced Analytics: Machine learning for waste pattern prediction
- 🌡️ Environmental Adaptation: Dynamic thresholds based on weather conditions
- 📱 Mobile Application: iOS and Android apps for remote monitoring
- 🔊 Voice Integration: Alexa/Google Assistant compatibility
- 🤖 Automated Maintenance: Self-diagnostic and maintenance scheduling
- 🌐 Mesh Network: ESP-32 mesh networking for large-scale deployment
- 🏙️ City-wide Deployment: Integration with municipal waste management
- 🚛 Route Optimization: AI-powered collection route planning
- 💰 Cost Analytics: ROI tracking and operational cost optimization
- 📈 Predictive Maintenance: AI-based component failure prediction
- ☀️ Solar Power Integration: Complete renewable energy operation
- ♻️ Circular Economy Metrics: Track waste recycling effectiveness
- 🌱 Carbon Footprint Tracking: Environmental impact monitoring
- 🏆 Gamification: Community engagement through waste sorting competitions
- Edge AI processing for offline operation
- Multi-camera support for 360° monitoring
- Advanced sensor fusion algorithms
- Real-time video streaming to dashboard
- Automated calibration procedures
- Blockchain integration for waste tracking
- Computer vision for waste volume estimation
- Integration with existing smart city infrastructure
- Advanced security and encryption protocols
- Multi-language support for global deployment
- Autonomous waste collection robot coordination
- Integration with circular economy platforms
- AI-powered waste reduction recommendations
- Global waste management network connectivity
- Carbon credit integration and tracking
- Custom object detection models for regional waste types
- Real-time image segmentation for mixed waste identification
- 3D depth sensing for accurate volume measurements
- Low-light and weather-resistant detection algorithms
- LoRaWAN integration for long-range communication
- Edge computing for reduced cloud dependency
- Mesh networking protocols for urban deployment
- Energy-efficient communication strategies
- Predictive modeling for waste generation patterns
- Anomaly detection for system health monitoring
- Optimization algorithms for collection scheduling
- Environmental impact assessment models
- Life cycle assessment of system components
- Renewable energy optimization algorithms
- Biodegradable material detection capabilities
- Integration with recycling facility systems
Made with ❤️ for a cleaner, smarter future
Last Updated: June 2025