SOIL SENSE

Soil Sense is an intelligent soil moisture monitoring web application designed to help farmers optimize irrigation practices through real-time data analysis and machine learning predictions. This project serves as the Artificial Intelligence research project for Year 3 students at Makerere University College of Computing and Information Sciences (COCIS).

Features

Core Functionality

• Real-time Soil Moisture Monitoring: Track soil moisture levels from IoT devices, CSV uploads, or manual input
• Machine Learning Predictions: AI-powered forecasting of future soil moisture levels
• Smart Irrigation Recommendations: Automated suggestions based on crop requirements and predictions
• Interactive Dashboards: Visualize data with charts and graphs using Chart.js
• Role-based Access Control: Secure user management with Admin, Technician, and Farmer roles
• Comprehensive Reporting: Generate and download reports in PDF/Excel formats

User Management

• User registration and authentication
• Password reset and profile management
• Role-based access control (Admin, Technician, Farmer)
• Secure session management

Data Management

• Store and retrieve soil moisture readings
• Filter data by location, date range, and crop type
• Historical data analysis and trending
• Data export capabilities

AI/ML Integration

• Predictive modeling for soil moisture levels
• Model retraining capabilities for admins
• Weather forecast integration
• Automated threshold-based alerts

Technology Stack

Backend

• Django 5.2.4 - Web framework
• Django REST Framework - API development
• Python 3.x - Programming language
• SQLite3 - Database

Frontend

• HTML/CSS/JavaScript - Frontend technologies
• Chart.js - Data visualization
• Tailwind CSS - UI framework

Machine Learning

• scikit-learn - ML algorithms
• Pandas/NumPy - Data processing

DevOps

• UbiOps - Model inference
• Git - Version control

Installation

Prerequisites

• Python 3.8 or higher
• pip (Python package installer)
• Git

Setup Instructions

1. Clone the repository

   git clone https://github.com/<username>/soil-sense.git
   cd soil-sense

2. Create and activate virtual environment

   python -m venv .venv
   
   # On Windows
   .venv\Scripts\activate
   
   # On macOS/Linux
   source .venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Set up environment variables

   # Create .env file
   cp .env.example .env
   # Edit .env with your configuration

5. Run database migrations

   python manage.py makemigrations authentication
   python manage.py migrate

6. Create superuser

   python manage.py createsuperuser

7. Run the development server

   python manage.py runserver

8. Access the application

   • Open your browser and go to http://127.0.0.1:8000/
   • Admin panel: http://127.0.0.1:8000/admin/

Project Structure

soilsense/
├── manage.py                 # Django management script
├── requirements.txt          # Python dependencies
├── README.md                # Project documentation
├── .env.example             # Environment variables template
├── .gitignore              # Git ignore rules
├── db.sqlite3              # SQLite database
├── authentication/          # Authentication app
│   ├── __init__.py
│   ├── admin.py            # Admin configuration
│   ├── apps.py             # App configuration
│   ├── models.py           # Custom User model
│   ├── views.py            # Authentication views
│   ├── tests.py            # Test cases
│   └── migrations/         # Database migrations
├── ml/                     # Machine Learning Engine
│   ├── __init__.py         # Main ML interface
│   ├── config.py           # ML configuration and settings
│   ├── data_processor.py   # Data preprocessing utilities
│   ├── model_trainer.py    # Model training functionality
│   └── predictor.py        # Prediction functions
├── apps/                   # Role-based applications
│   ├── farmer/             # Farmer-specific functionality
│   │   ├── __init__.py
│   │   ├── admin.py
│   │   ├── apps.py
│   │   ├── models.py
│   │   ├── views.py
│   │   ├── tests.py
│   │   └── migrations/
│   ├── technician/         # Technician-specific functionality
│   │   ├── __init__.py
│   │   ├── admin.py
│   │   ├── apps.py
│   │   ├── models.py
│   │   ├── views.py
│   │   ├── tests.py
│   │   └── migrations/
│   └── landing_page/       # Landing page and public views
│       ├── __init__.py
│       ├── admin.py
│       ├── apps.py
│       ├── models.py
│       ├── views.py
│       ├── tests.py
│       └── migrations/
└── soilsense/              # Main Django project
    ├── __init__.py
    ├── settings.py         # Django settings
    ├── urls.py            # Main URL configuration
    ├── wsgi.py           # WSGI configuration
    └── asgi.py           # ASGI configuration

Configuration

Environment Variables

Create a .env file in the root directory with the following variables:

DEBUG=True
SECRET_KEY=your-secret-key-here

Database Configuration

The database comes already set up for the light nature of the project we use sqlite3 which has a well documented api for django

Usage

For Farmers

1. Register an account with farmer role
2. Add your farm locations and crop information
3. View real-time soil moisture data
4. Receive irrigation recommendations
5. Download reports for record keeping

For Technicians

1. Access system with technician credentials
2. Monitor multiple farm locations
3. Configure sensor thresholds
4. Generate technical reports

For Administrators

1. Manage user accounts and permissions
2. Upload and retrain ML models
3. Configure system-wide settings
4. Access comprehensive analytics

Machine Learning Features

Prediction Model

• Input Parameters: Location, current soil moisture, temperature, humidity
• Output: Predicted soil moisture levels for next 7-30 days
• Model Types: Linear regression, Random Forest, Neural Networks
• Retraining: Admin can upload new training data and retrain models

Data Sources

• IoT sensor data
• Weather API integration
• Historical soil moisture records
• Crop-specific water requirements

API Documentation

The application provides RESTful APIs for:

• User authentication and management
• Soil moisture data CRUD operations
• ML prediction endpoints
• Report generation
• Alert management

Example API Endpoints

POST /api/auth/login/          # User login
GET  /api/moisture/           # Get moisture data
POST /api/predict/            # Get ML predictions
GET  /api/reports/            # Generate reports

Testing

Run the test suite:

python manage.py test

Run with coverage:

coverage run --source='.' manage.py test
coverage report

Deployment

Production Setup

1. Set DEBUG=False in settings
2. Configure production database
3. Set up static file serving

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Team

Year 3 Students - Makerere University COCIS

• [Student Name 1] - Backend Development
• [Student Name 2] - Frontend Development
• [student Name 3] - Machine Learning
• [Student Name 4] - Database Design
• [Student Name 5]- Backend and Frontend/ml

Support

For support and questions:

• Email: soilsense@mak.ac.ug
• GitHub Issues: Create an issue
• Documentation: Wiki

Version History

• v1.0.0 - Initial release with basic soil moisture monitoring

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Made with ❤️ by Makerere University BSSE Group P Students