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https://github.com/ghaith-jbali/smart-greenhouse-iot-system
An IoT system for smart greenhouse management with real-time monitoring and automated control of temperature, humidity, soil moisture, and light. Integrates Python, Node-RED, MQTT, and the Google Gemini API for AI-powered plant care assistance. Ideal for optimizing greenhouse conditions and plant health.
https://github.com/ghaith-jbali/smart-greenhouse-iot-system
ai aiassistant arduino c chatbot esp32 gemini gemini-api generative-ai iot nodejs nodered python smartgreenhouse smartplanner
Last synced: about 2 months ago
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An IoT system for smart greenhouse management with real-time monitoring and automated control of temperature, humidity, soil moisture, and light. Integrates Python, Node-RED, MQTT, and the Google Gemini API for AI-powered plant care assistance. Ideal for optimizing greenhouse conditions and plant health.
- Host: GitHub
- URL: https://github.com/ghaith-jbali/smart-greenhouse-iot-system
- Owner: ghaith-jbali
- Created: 2024-11-06T20:26:06.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-06T21:28:27.000Z (about 2 months ago)
- Last Synced: 2024-11-06T21:34:08.384Z (about 2 months ago)
- Topics: ai, aiassistant, arduino, c, chatbot, esp32, gemini, gemini-api, generative-ai, iot, nodejs, nodered, python, smartgreenhouse, smartplanner
- Language: C++
- Homepage:
- Size: 388 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Smart Greenhouse IoT System
This repository contains the Smart Greenhouse IoT System, which integrates IoT technologies, AI-driven insights, and real-time data visualization to manage greenhouse conditions. The system can monitor environmental factors, automate responses, and offer interactive plant care advice using an AI assistant.
## Project Overview
The Smart Greenhouse project is designed to:
- Monitor temperature, humidity, soil moisture, and light intensity using sensors.
- Automatically control pumps, fans, lights, and a humidifier to maintain optimal conditions.
- Provide an interactive dashboard via Node-RED for real-time control and data visualization.
- Respond to user questions with plant care insights using an AI assistant integrated with the Google Gemini API.
## Repository Contents
- **Node-RED Flows**:
- `weather_dashboard_flow.json`: Manages weather data collection, visualization, and environmental monitoring.
- `greenhouse_automation_flow.json`: Automates equipment based on sensor data and user settings.
- **Python Server**:
- `smart_planter_server.py`: The server code for processing questions, generating AI responses, and managing sensor data.
- **ESP32 Code**:
- `smart_greenhouse_esp32.ino`: Code for the ESP32 to read sensors and control actuators, designed to interact with the MQTT broker.
- **Data Files**:
- `temp.csv`, `hum.csv`, `shum.csv`, `lum.csv`: Store sensor readings for temperature, humidity, soil moisture, and light intensity.
- `plant.txt`: Contains the type of plant being monitored.
- `conversation_history.json`: Logs questions and responses for the AI assistant.
## Setup Guide
### Prerequisites
- **Python 3.7+**
- **Node-RED**
- **MQTT Broker** (e.g., Mosquitto)
- **OpenWeatherMap API Key**
### Installation
1. **Clone the Repository**:
```bash
git clone https://github.com/ghaith-jbali/Smart-Greenhouse-IoT-System.git
cd Smart-Greenhouse-IoT-System
```
2. **Install Python Dependencies**:
```bash
pip install -r requirements.txt
```
3. **Set Up Environment Variables**:
- Create a `.env` file in the root directory and add your Google Gemini API key:
```bash
API_KEY=your_google_gemini_api_key
```
4. **Create `plant.txt`**:
- Add the type of plant being monitored (e.g., "Tomato").
5. **Start the Flask Server**:
```bash
python smart_planter_server.py
```
6. **Upload ESP32 Code**:
- Flash `smart_greenhouse_esp32.ino` to your ESP32 board, ensuring the Wi-Fi and MQTT broker settings are configured.
7. **Import Node-RED Flows**:
- Open Node-RED and import `weather_dashboard_flow.json` and `greenhouse_automation_flow.json`.
### Configuring Node-RED
1. **HTTP Node Setup**:
- **Install HTTP Node**: Go to `Manage Palette` > `Install` in Node-RED, and install the HTTP nodes if not already available.
- **Configure Endpoint**: Add an HTTP input node with the method `POST`. Define the endpoint (e.g., `/api/data`) and connect it to the necessary nodes for data processing.
2. **OpenWeatherMap API Integration**:
- **Register for API Key**: Create an account at [OpenWeatherMap](https://openweathermap.org/) and obtain an API key.
- **Set Up Node-RED OpenWeatherMap Node**: Add your API key and location details to the `openweathermap` node in Node-RED, linking it to UI components for real-time weather data display.
### Usage
- **Access the Dashboard**: Open the Node-RED UI to view and control the system.
- **Interact with the AI**: Send POST requests to the `/ask` endpoint on the Flask server to receive plant care tips or inquire about greenhouse conditions.
### Example API Request
```json
POST /ask
{
"question": "How is my plant doing?"
}
```
## Security Notes
- **API Key Security**: Store API keys and sensitive information in environment variables, and exclude them from the repository using `.gitignore`.
- **Data Files**: Keep `conversation_history.json` and other log files private or ensure they are properly managed if shared.