🌿 Smart Greenhouse IoT System

An intelligent IoT greenhouse monitoring and control system with AI-powered automation

Table of Contents

• 🌿 Smart Greenhouse IoT System
• ✨ Features
  • 🔍 Real-time Monitoring
  • 🤖 Smart Automation
  • 🎛️ Device Management
  • 🔔 Smart Alerts
• 🛠️ Tech Stack
  • Backend
  • Frontend
  • Machine Learning
  • DevOps & Deployment
• Project Setup
  • 1. Clone the Repository
  • 2. Database Setup
  • 3. Backend Setup
  • 4. Frontend Setup
• 📁 Project Structure

✨ Features

🔍 Real-time Monitoring

• Environmental Sensors: Temperature, Humidity, Soil Moisture, Light intensity
• Live Dashboard: Real-time data visualization with charts and graphs
• Historical Analytics: Track trends and patterns over time
• WebSocket Integration: Instant updates without page refresh

🤖 Smart Automation

• AI-Powered Control: Machine learning models for optimal device control
• Automatic Mode: Intelligent responses based on sensor readings
• Scheduled Control: Time-based automation with custom schedules
• Manual Override: Complete manual control when needed

🎛️ Device Management

• LED Control: Smart lighting with intensity adjustment (0-100%)
• Fan Control: Ventilation management with variable speed (0-100%)
• Water Pump: Automated irrigation system with flow control (0-100%)

🔔 Smart Alerts

• Threshold Monitoring: Custom alerts for sensor values
• Reminder System: Scheduled notifications and maintenance reminders
• Alert History: Track all notifications and system events

🛠️ Tech Stack

Backend

• Runtime: Node.js with Express.js
• Database: PostgreSQL with connection pooling
• Real-time: WebSocket (ws library)
• IoT Platform: Adafruit IO with MQTT
• Authentication: JWT tokens
• Environment: dotenv configuration

Frontend

• Framework: React Native with Expo
• Language: TypeScript
• UI Library: React Native Paper
• Navigation: Expo Router
• State Management: React Query
• Charts: React Native Chart Kit & Gifted Charts

Machine Learning

• Language: Python 3.x
• Framework: Scikit-learn
• Algorithm: Random Forest Classifier
• Features: Environmental sensor data processing
• Devices: LED, Fan, Water Pump control prediction

DevOps & Deployment

• Deployment: Render.com (Backend), Expo (Frontend)
• Version Control: Git
• Package Management: npm (Node.js), pip (Python)

Project Setup

1. Clone the Repository

git clone https://github.com/bawfng04/DADN-SmartGreenhouseSystem.git
cd DADN-SmartGreenhouseSystem

2. Database Setup

1. Install PostgreSQL and create a database.

2. Import database schema:

   • Navigate to backend/src/database/create.sql
   • Copy the SQL content and execute it in your PostgreSQL database
   • Or run: psql -U postgres -d <database name> -f backend/src/database/create.sql

3. Backend Setup

1. Navigate to the backend directory:

   cd backend

2. Install the dependencies:

   pip install -r requirements.txt && npm install

3. Configure the environment variables:

   • Create a .env file in the backend directory with the following content:

   # Port to run the server on
   PORT = <yours>
   
   # Postgres connection - For local database
   POSTGRES_HOST=localhost
   POSTGRES_PORT=5432
   POSTGRES_DB=<yours>
   POSTGRES_USER=<yours>
   POSTGRES_PASSWORD=<yours>
   
   # Postgres connection - For hosted database (comment out when running locally)
   # POSTGRES_HOST=<yours>
   # POSTGRES_PORT=<yours>
   # POSTGRES_DB=<yours>
   # POSTGRES_USER=<yours>
   # POSTGRES_PASSWORD=<yours>
   # DB_SSLMODE=require
   # POSTGRES_EXTERNAL_URL=<yours>?ssl=tr
   
   # Secret key for encrypting password
   SECRET_KEY=<yours>
   
   # Secret key for token
   JWT_SECRET_KEY=<yours>
   
   # Adafruit IO configuration
   ADAFRUIT_IO_USERNAME=<yours>
   ADAFRUIT_IO_KEY=<yours>
   
   # Frontend URL
   FRONTEND_URL=<yours>
   
   

4. Start the backend server:

   npm run dev

For complete API documentation, see backend/README.md.

4. Frontend Setup

1. Navigate to the frontend directory:

   cd frontend

2. Install the dependencies:

   npm install

3. Configure the environment variables:
   • Create a .env file in the frontend directory with the following content:

     API_URL = <yours>
     WEBSOCKET_URL = <yours>
     

4. Start the frontend application:

   npx expo start

📁 Project Structure

DADN-SmartGreenhouseSystem/
├── backend/                    # Node.js Backend
│   ├── src/
│   │   ├── controllers/        # API route handlers
│   │   ├── models/            # Database models
│   │   ├── services/          # Business logic
│   │   ├── repository/        # Data access layer
│   │   ├── routes/            # Route definitions
│   │   ├── utils/             # Utilities (MQTT, etc.)
│   │   ├── database/          # Database configuration
│   │   └── GreenhouseModel/   # AI prediction models
│   ├── .env                   # Environment variables
│   ├── server.js              # Entry point
│   └── package.json
│
├── frontend/                   # React Native Frontend
│   ├── app/                   # App screens (Expo Router)
│   ├── components/            # Reusable components
│   ├── constants/             # App constants
│   ├── contexts/              # React contexts
│   ├── hooks/                 # Custom React hooks
│   ├── utils/                 # Utility functions
│   ├── assets/                # Static assets
│   ├── .env                   # Environment variables
│   └── package.json
│
├── Machine learning model/     # Python ML Models
│   ├── fan_control/           # Fan control model
│   ├── led_control/           # LED control model
│   ├── pump_control/          # Pump control model
│   └── README.md
│
└── README.md                  # This file