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:

   git clone https://github.com/ghaith-jbali/Smart-Greenhouse-IoT-System.git
   cd Smart-Greenhouse-IoT-System

2. Install Python Dependencies:

   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:

     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:

   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 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

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.