https://github.com/jayclifford345/carnivorous-plant-health
A full-stack IoT solution for monitoring and maintaining the health of carnivorous plants in a greenhouse environment. This system uses a network of sensors and cameras with a dual Raspberry Pi setup to collect data, analyze plant health, and provide actionable insights.
https://github.com/jayclifford345/carnivorous-plant-health
health iot plants sensors
Last synced: about 2 months ago
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A full-stack IoT solution for monitoring and maintaining the health of carnivorous plants in a greenhouse environment. This system uses a network of sensors and cameras with a dual Raspberry Pi setup to collect data, analyze plant health, and provide actionable insights.
- Host: GitHub
- URL: https://github.com/jayclifford345/carnivorous-plant-health
- Owner: Jayclifford345
- Created: 2025-03-10T16:22:15.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-05-02T10:53:09.000Z (6 months ago)
- Last Synced: 2025-05-02T11:47:06.318Z (6 months ago)
- Topics: health, iot, plants, sensors
- Language: Python
- Homepage:
- Size: 423 KB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Carnivorous Plant Greenhouse Monitoring System
A full-stack IoT solution for monitoring and maintaining the health of carnivorous plants in a greenhouse environment. This system uses a network of sensors and cameras with a dual Raspberry Pi setup to collect data, analyze plant health, and provide actionable insights.
## System Architecture
### Hardware Components
#### Raspberry Pi Setup
- **plant-hub**: Central server that collects, stores, and visualizes data
- Runs Docker containers for data storage, analysis, and visualization
- Hosts the web interface for monitoring plant health
- **plant**: Sensor hub that collects environmental data
- Connected to physical sensors via I2C
- Transmits data to plant-hub for processing and storage
#### Sensors
- **SHT-30 Temperature & Humidity Sensor**: Weather-proof sensor for accurate climate monitoring
- **Ambient Light Sensor (0-200klx)**: Monitors light levels in the greenhouse *(planned implementation)*
- **Web Camera**: Used for visual plant health assessments via the plant-doctor AI
#### Optional Hardware
- **Smart Plug for Humidifier**: Controllable via Node-RED to maintain optimal humidity levels
### Software Components
- **sensor-reader**: Python application running on the "plant" Raspberry Pi that reads sensor data and sends it to the plant-hub
- **plant-doctor**: AI-powered visual analysis tool using OpenAI's GPT-4 Vision to assess plant health from camera images
- **Grafana**: Data visualization and dashboard for monitoring environmental conditions
- **Prometheus**: Time-series database for storing metrics
- **Loki**: Log aggregation service
- **Node-RED**: Flow-based programming tool for automation and alerts
- **Alloy**: Observability data pipeline
## Setup Instructions
### Plant-Hub Setup (Raspberry Pi #1)
1. **Prerequisites**
Install Docker and Docker Compose on the Raspberry Pi: https://docs.docker.com/engine/install/debian/
2. **Clone the Repository**
```bash
git clone https://github.com/username/carnivorous-plant-health.git
cd carnivorous-plant-health
```
3. **Start the Services**
```bash
docker-compose up -d
```
4. **Access the Services**
- Grafana: http://plant-hub:3000
- Node-RED: http://plant-hub:1880
- Prometheus: http://plant-hub:9090
### Plant Sensor Hub Setup (Raspberry Pi #2)
1. **Prerequisites**
```bash
sudo apt update && sudo apt upgrade -y
sudo apt install -y python3-pip python3-venv git i2c-tools
```
2. **Enable I2C Interface**
```bash
sudo raspi-config
# Navigate to Interface Options > I2C > Enable
```
3. **Clone the Repository**
```bash
git clone https://github.com/username/carnivorous-plant-health.git
cd carnivorous-plant-health/sensor-reader
```
4. **Install the Sensor Reader Service**
```bash
sudo ./install.sh
```
5. **Verify Installation**
```bash
sudo systemctl status greenhouse-sensor
```
### Sensor Wiring
#### SHT-30 Temperature & Humidity Sensor
- Connect VIN to 3.3V
- Connect GND to GND
- Connect SCL to SCL (GPIO 3)
- Connect SDA to SDA (GPIO 2)
#### Ambient Light Sensor (Optional will need to enable pins for general use)
- Connect VCC to 3.3V
- Connect GND to GND
- Connect SCL to SCL (GPIO 27)
- Connect SDA to SDA (GPIO 28)
### Web Camera Setup
1. **Connect the USB webcam to Plant Raspberry Pi**
2. **Install the Plant Doctor Service**
```bash
cd carnivorous-plant-health/plant-doctor
pip install -r requirements.txt
```
3. **Set up OpenAI API Key**
```bash
# Option 1: Using AI_TOKEN (preferred)
export AI_TOKEN="your-api-key"
# Option 2: Using OPENAI_API_KEY (alternative)
export OPENAI_API_KEY="your-api-key"
```
4. **Setup the Plant Doctor Application**
```bash
# Option 1: Preserve environment variables with sudo -E
sudo -E ./install.sh
# Option 2: Pass environment variable directly to sudo
sudo OPENAI_API_KEY="your-api-key" ./install.sh
```
Note: When using sudo, environment variables are not automatically passed to the new environment. You must either:
- Use the `-E` flag with sudo to preserve all environment variables
- Or pass the environment variable directly to the sudo command
## System Features
### Environmental Monitoring
- Real-time temperature and humidity tracking
- Light level monitoring (planned implementation)
- Historical data visualization and trends
- Alert thresholds for critical conditions
### Plant Health Analysis
- AI-powered visual assessment of plant health
- Identification of potential issues or diseases
- Carnivorous plant species recognition
- Health status tracking over time
- Tank health analysis combining visual and sensor data
- Historical trend analysis for environmental conditions
### Automation
- Smart humidifier control based on humidity levels
- Alert notifications for adverse conditions
- Scheduled image capture and health assessment
- Automatic image retention (keeps last 5 images)
## API Endpoints
The plant-doctor service provides the following API endpoints:
### Plant Health
- `GET /`: Web interface for monitoring plant health
- `GET /image`: Current plant image
- `GET /api/health`: JSON data of latest plant health analysis
- `POST /api/capture`: Manually trigger image capture and analysis
### Sensor Data
- `GET /api/metrics`: Test endpoint for Prometheus queries
- Returns 12-hour historical data for temperature and humidity
- Includes min, max, average, current values
- Includes rate of change analysis
### Image Data
- `GET /api/image/base64`: Current image in base64 format
- Useful for testing and debugging
- Returns JSON with encoded image data
## Dashboard Guide
The Grafana dashboard at http://plant-hub:3000 provides:
- Current temperature and humidity readings
- Historical trends of environmental conditions
- Alert status and history
- Plant health assessment results
- Tank health analysis and recommendations
## Prometheus Metrics
The system collects and stores the following metrics:
### Temperature
- `temperature_celsius`: Current temperature
- Historical analysis:
- Minimum temperature over 12h
- Maximum temperature over 12h
- Average temperature over 12h
- Rate of temperature change
### Humidity
- `humidity_percent`: Current humidity
- Historical analysis:
- Minimum humidity over 12h
- Maximum humidity over 12h
- Average humidity over 12h
- Rate of humidity change
## Troubleshooting
### Sensor Issues
- Verify I2C connection: `sudo i2cdetect -y 1`
- Check system logs: `journalctl -u greenhouse-sensor -f`
- Ensure proper wiring and power supply
- Test Prometheus queries: `curl http://plant-hub:9090/api/v1/query?query=temperature_celsius`
### Camera Issues
- Verify camera connection: `ls -l /dev/video*`
- Test camera capture: `fswebcam test.jpg`
- Check image quality: `curl http://plant-hub:5000/api/image/base64`
### Connectivity Issues
- Ensure both Raspberry Pis are on the same network
- Verify network configuration: `ping plant-hub` from the sensor Pi
- Check service status: `docker-compose ps` on the hub Pi
- Test API endpoints: `curl http://plant-hub:5000/api/metrics`
### AI Analysis Issues
- Verify OpenAI API key is set correctly
- Check image quality and lighting conditions
- Review OpenTelemetry logs for analysis results
- Test individual components via API endpoints
## Contributing
Contributions to this project are welcome! Please submit pull requests or open issues for bugs, feature requests, or improvements.
## License
This project is licensed under the MIT License - see the LICENSE file for details.