https://github.com/sabermahjoub/smartgarden
IoT project for a smart garden featuring a web application (Angular + Flask) and leveraging ML/DL algorithms.
https://github.com/sabermahjoub/smartgarden
angular esp32 flask iot ml plant-monitoring smartgarden web
Last synced: 4 months ago
JSON representation
IoT project for a smart garden featuring a web application (Angular + Flask) and leveraging ML/DL algorithms.
- Host: GitHub
- URL: https://github.com/sabermahjoub/smartgarden
- Owner: Sabermahjoub
- License: mit
- Created: 2024-10-15T12:50:52.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-14T16:32:58.000Z (over 1 year ago)
- Last Synced: 2025-10-11T13:11:29.564Z (8 months ago)
- Topics: angular, esp32, flask, iot, ml, plant-monitoring, smartgarden, web
- Language: Jupyter Notebook
- Homepage:
- Size: 67.4 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# ✨ IoT Project with Angular 13, Flask, MQTT, and ML/DL Algorithms
See the app demo ? Consult this link : https://drive.google.com/file/d/1Utyq1ixwL_11p-BXn7cv6J7kePKY8KZW/view?usp=drive_link
Note : To be able to to extract : wind speed, lowest and highest temperature of the day, UV index, you should create an account at : weather.visualcrossing.com
And use your own key :
```bash
private weatherUrl = "https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/YOURCITY?key=YOURKEY";
```
Hardware setup :
## Table of Contents
1. [Project Overview](#project-overview)
2. [Technology Stack](#technology-stack)
3. [Features](#features)
4. [Architecture](#architecture)
5. [Installation and Setup](#installation-and-setup)
6. [Project Structure](#project-structure)
7. [Usage](#usage)
8. [MQTT Configuration](#mqtt-configuration)
9. [ML/DL Algorithms](#ml-dl-algorithms)
10. [API Endpoints](#api-endpoints)
11. [Testing](#testing)
12. [Contributing](#contributing)
13. [License](#license)
---
## Project Overview
This project is an IoT-based system that integrates real-time sensor data, machine learning, and deep learning algorithms. The front end is developed using **Angular 13**, while the backend uses **Flask**. Communication between IoT devices and the server happens via the **MQTT protocol**. Data processing and analysis leverage **ML/DL algorithms** to deliver intelligent insights.
### Key Goals
- Real-time sensor data collection and visualization.
- Efficient communication using MQTT.
- Backend support for data processing and machine learning.
- Web-based interface for data interaction and insights visualization.
---
### Key App functionalities
- Get sensors and external api data and inform farmer of the potential risks for the plant.
- Managing list of plants.
- Managing daily tasks.
- Inform farmer of possible storm for the next hour to come.
- Managing logs.
- Predicting health of plant.
---
## Technology Stack
### Frontend
- **Framework**: Angular 13
- **UI Library**: Angular Material / TailWind
### Backend
- **Web Framework**: Flask
- **Protocol**: MQTT (via `paho-mqtt` library)
- **Data Processing**: Pandas, NumPy
- **ML/DL**: Scikit-learn, TensorFlow/Keras
- **Database**: MYSQL
### IoT Communication
- **Protocol**: MQTT
- **MQTT Configuration**: HiveMQ broker
---
## Features
- Real-time sensor data acquisition via MQTT.
- Dynamic dashboards to visualize sensor data.
- Machine learning models to process and analyze incoming data.
- RESTful APIs for backend communication and plant/tasks management
- Notifications/alerts based on intelligent thresholds.
- Deep learning algorithms for anomaly detection or prediction.
---
When a moderate risk of storm is predicted :
When a moderate risk of storm is predicted :
## Architecture
```
+-------------------------------+
| IoT Devices |
| (Sensors/Actuators) |
+---------------+---------------+
|
| MQTT
v
+-------------------------------+
| Flask Backend |
| - MQTT Broker Integration |
| - Data Preprocessing |
| - ML/DL Processing |
+---------------+---------------+
|
| REST APIs
v
+-------------------------------+
| Angular Frontend |
| - Real-time Dashboards |
| - Visualizations/Controls |
+-------------------------------+
```
---
## Installation and Setup
### Prerequisites
Ensure the following tools are installed on your system:
- Node.js (v14+)
- Angular CLI (v13)
- Python (v3.8+)
- MQTT Broker (e.g., HiveMQ)
- Virtualenv (recommended for Python)
### Frontend Setup (Angular)
```bash
# Clone the repository
git clone https://github.com/Sabermahjoub/SmartGarden.git
cd SmartGarden/Smart-Garden-front
# Install dependencies
npm install
# Start the development server
ng serve
# Open http://localhost:4200 in your browser
```
### Backend Setup (Flask)
```bash
# Navigate to the backend folder
cd ../backend
# Start the Flask server
python app.py
# API will be available at http://127.0.0.1:5000
```
### MQTT Broker Setup
1. Install **HiveMQ** MQTT broker.
2. Start the HiveMQ server with the default configuration.
3. Use the broker's URL (e.g., `mqtt://localhost`) for testing.
---
## Project Structure
```plaintext
SmartGarden/
├── Smart-Garden-front/ # Angular 13 Frontend
│ ├── src/
│ ├── angular.json
│ └── package.json
│
├── backend/ # Flask Backend
│ ├── DL__ClassificationImage.ipynb # DL model
│ ├── PlantVillage/ # DL classification dataset
│ ├── ML_prediction.ipynb # ML model
│ ├── plant_growth_data.csv # ML dataset
│ ├── background_job.py # MQTT configuration
│ ├── smartgarden.sql # DB configuration
│ ├── esp32.py # hardware configuration
│ ├── app.py # Main Flask application
│
└── README.md # Project documentation
```
---
## Usage
1. Start the **MQTT broker**.
2. Run the Flask server to process incoming data and serve APIs.
3. Launch the Angular frontend for real-time data visualization.
---
## MQTT Configuration
- **Broker URL**: `mqtt://localhost` (or your MQTT server address)
## Dependencies
Flask and MQTT library for connecting with the broker and subscribing to messages
```bash
pip install Flask paho-mqtt
```
Example MQTT Message Format:
```json
{
"temperature": 25.3,
"humidity": 60,
"moisture": 40, //soil moisture
"light_intensity": 50,
"timestamp": "2024-12-14:00:00Z"
}
```
---
## ML/DL Algorithms
This project incorporates the following machine learning and deep learning algorithms:
1. **Regression**: For predictive analysis based on sensor data and external api data.
3. **Classification Model**: For predicting health of a plant based on its leaf.
Pre-trained models are stored in the `backend/` directory and loaded during runtime.
---
## Testing
### Frontend Testing
```bash
# Run unit tests
ng test
# Run end-to-end tests
ng e2e
```
### Backend Testing
```bash
# Run Flask unit tests
pytest
```
---
## Contributing
Contributions are welcome! Follow these steps:
1. Fork the repository.
2. Create a new branch: `git checkout -b feature/your-feature`.
3. Commit your changes: `git commit -m 'Add feature XYZ'`.
4. Push to the branch: `git push origin feature/your-feature`.
5. Submit a pull request.
---
## License
This project is licensed under the MIT License. See the LICENSE file for more details.