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Tutorials

Getting Started with Smart Backpack Deployment​

Overview​

This tutorial guides you through deploying the complete Smart Backpack system from camera-trap configuration to autonomous drone operations.

Prerequisites​

Hardware Requirements​

  • Raspberry Pi 4B: 64GB Storage, 4GB RAM minimum
  • Webcam: USB-compatible camera for animal detection
  • WiFi Router: Local network infrastructure
  • Power Station: Portable power supply for field deployment
  • Parrot ANAFI Drone (optional): For full autonomous pipeline

Software Requirements​

  • Docker Engine (v20.10+)
  • Docker Compose (v2.0+)
  • Python 3.9+
  • Linux-based OS (tested on Ubuntu 20.04+)

Step-by-Step Deployment​

Step 1: Camera-Trap Configuration​

  1. Clone the repository:

    git clone <repository-url>
    cd smartfield

  2. Navigate to the camera-trap configuration directory:

    cd ct-config

  3. Edit cameratrap-config.py to set your controller IP:

    controller_ip = 'http://icicle-ct1.local:8080'

  4. Configure the camera-trap by running each section sequentially:

    Health Check:

    response = requests.get(f'{controller_ip}/health')
    print(response.json())

    System Startup:

    response = requests.post(f'{controller_ip}/startup', json={},
                            headers={'Content-Type': 'application/json'})

    Configure Detection Parameters:

    payload = {
        "gpu": "false",
        "ckn_mqtt_broker": "192.168.0.122",  # Your MQTT broker IP
        "ct_version": "test",
        "mode": "demo",
        "min_seconds_between_images": "5",
        "model": "yolov5nu_ep120_bs32_lr0.001_0cfb1c03.pt",
        "inference_server": "false",
        "detection_thresholds": "{\"animal\": \"0.4\", \"image_store_save_threshold\": \"0\", \"image_store_reduce_save_threshold\": \"0\"}"
    }
    response = requests.post(f'{controller_ip}/configure', json=payload)

    Start Detection: Note: Run the file everytime when you comment and uncomment the piece of code. Dont run the entire file. Run every section of code one by one.

    response = requests.post(f'{controller_ip}/run')

  5. Run the configuration:

    python3 cameratrap-config.py

Step 2: System Installation​

  1. Create required directories:

    mkdir -p logs mission AnafiMedia

  2. Configure system settings in config.toml:

    nano config.toml

    IMPORTANT: Update the camera-trap location coordinates precisely. The drone uses these coordinates to navigate to detection sites. Incorrect values will cause the drone to fly to the wrong location.

    # MQTT topic mapping for each Pi
    [mqtt_topics."cameratrap/events"]
    lat = 40.008278960212      # Replace with your camera-trap's exact latitude
    lon = -83.0175149068236    # Replace with your camera-trap's exact longitude
    camid = "pi-001"           # Unique identifier for this camera-trap

    Also update MQTT broker addresses, network settings, and service endpoints as needed.

  3. Build and start services:

    docker-compose up -d

  4. Verify deployment:

    docker-compose ps

Step 3: Verification​

Check each service endpoint:

# OpenPassLite (Mission Planning)
curl http://localhost:2177/

# SmartField (Event Coordination)
curl http://localhost:2188/

# WildWings (Drone Control)
curl http://localhost:2199/

Step 4: Monitoring Dashboard​

Access Grafana at http://localhost:3000:

  • Username: admin
  • Password: admin

End Result​

Upon successful deployment, you will have:

  • ✓ Autonomous camera-trap detecting animals in real-time
  • ✓ MQTT-based event communication system
  • ✓ Drone coordination ready for autonomous missions
  • ✓ Real-time monitoring dashboard
  • ✓ Centralized logging infrastructure