Smart Garden System

Class2_Group5

Smart Garden System is an intelligent IoT-based platform for real-time monitoring, prediction, and control of garden environmental parameters. It features sensor simulation, historical data management, AI-powered trend analysis, and a modern web interface for both monitoring and manual control.

Features

Real-time sensor data simulation and collection
Humidity, light, pH, CO₂, and rain detection
Historical data storage and retrieval
AI-based trend prediction (LLM integration)
Manual control for irrigation, lighting, pH, and ventilation
User authentication (login/register)
Responsive frontend with modular page design

Project Structure

smart_garden/
├── sensor_simulator/       # Sensor simulator for simulating sensor data collection
│   ├── __init__.py         # Package initialization file
│   ├── simulator.py        # Main logic for sensor data simulation
│   └── sensors/            # Implementations of various sensors
│       ├── __init__.py     # Sub - package initialization file
│       ├── humidity_sensor.py  # Humidity sensor simulation code
│       ├── light_sensor.py     # Light sensor simulation code
│       ├── ph_sensor.py        # pH sensor simulation code
│       ├── co2_sensor.py       # CO2 sensor simulation code
│       └── rain_sensor.py      # Rain sensor simulation code
├── web_app/               # Flask web app for user interaction and data display
│   ├── __init__.py         # Package initialization file
│   ├── app.py              # Core logic of the web application
│   ├── templates/          # HTML templates for page views
│   │   ├── base.html       # Basic HTML template
│   │   ├── index.html      # Home page template
│   │   ├── login.html      # Login page template
│   │   ├── register.html   # Registration page template
│   │   ├── database.html   # Database display page template
│   │   └── selection.html  # Selection page template
│   └── static/             # Static files (CSS, JS, images)
│       └── style.css       # CSS style file for page beautification
├── config.py             # Configuration file storing system params and connection info
└── requirements.txt      # Dependency file listing required Python packages

Team Contributions

[Lanyu Yue]

Code writing for humidity sensor
Code writing for light sensor
Code writing for generating values and alert logic
API Route for Historical Data
Homepage Data Rendering
Frontend-Backend Data Flow
Historical Data Retrieval
Latest Data Retrieval from Database
Ring buffer setup

[Xinyao Zhong]

Code writing for simulator
Code writing for controller
Code writing for database
Code writing for VAE generator
Code splitting of simulator and controller

[Yingzhuo Yu]

Code writing for humidity sensor
Code writing for light sensor
Code writing for generating values and alert logic
API Route for Historical Data
Homepage Data Rendering
Frontend-Backend Data Flow
Historical Data Retrieval
Latest Data Retrieval from Database
Ring buffer setup

[Yitong Li]

LLM prediction model
API endpoint development
Forecast visualization
Frontend auth system (login/register pages)
Frontend modularization (page splitting)
Navigation index
Cross-page state management
Manual control of environmental adjustment
Lighting and humidity controller

Quick Start

Install dependencies

pip install -r login_register_Page/requirements.txt

Start the backend

cd login_register_Page
python Web_page.py

Start the simulator (optional, for data generation)
```
cd ../smart_garden
python main_simulator.py
```
Access the system
- Open your browser and go to http://localhost:5005

Main Functionalities

Sensor Simulation & Data Flow: Simulated sensors generate data, which is sent via MQTT and stored in the database. [Lanyu Yue][Xinyao Zhong][Yingzhuo Yu]
Historical Data API: /api/history provides historical sensor data for frontend visualization. [Lanyu Yue][Yingzhuo Yu]
Latest Data API: /api/latest fetches the most recent sensor reading from the database. [Lanyu Yue][Yingzhuo Yu]
AI Prediction: /api/predict uses an LLM to analyze trends and forecast future conditions. [Yitong Li]
Frontend: Modular pages for dashboard, history, prediction, and manual control, with authentication and navigation. [Yitong Li]
Manual Control: Users can manually trigger irrigation, lighting, pH regulation, and ventilation. [Yitong Li]

Acknowledgements

All team members contributed to the design, implementation, and testing of the Smart Garden System.
Special thanks to [Lanyu Yue], [Xinyao Zhong], [Yingzhuo Yu], and [Yitong Li] for their dedicated work in their respective areas.

CUH405CMD2425FEBJUL/Class2-Team5

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