Soil Moisture Prediction Using Machine Learning

Efficient water management is a cornerstone of precision farming. This project utilizes machine learning techniques to predict soil moisture levels, enabling smarter irrigation strategies and reducing dependency on sensors.

Key Features

• Soil Moisture Prediction: Predict moisture levels at unsensed points using data from a small number of sensors.
• Simulation: Model moisture behavior over time and distance.
• Irrigation Planning: Enable smarter and real-time irrigation strategies.

How It Works

Model-1

• Purpose: Predicts moisture levels at fixed distances (2.5 and 3.0 units).
• Algorithm: Voting Regressor combining Decision Tree, Random Forest, and XGBoost.
• Performance: Achieves high accuracy with R-squared scores > 0.99 in most scenarios.

Model-2

• Purpose: Dynamically predicts moisture levels at any user-specified distance.
• Algorithm: Random Forest Regressor trained on features like time, initial center moisture, and distance.
• Capabilities: Simulates moisture absorption and evaporation over time.

Simulation

• Visualization: Displays moisture trends at the center, two sensor points, or user-defined distances.
• Inputs: Time and initial center moisture levels.

Technologies Used

• Programming: Python
• Libraries: Scikit-learn, XGBoost, Pandas, NumPy
• Visualization: Matplotlib

Repository Structure

.
├── soil_moisture_summary.md  # Detailed project documentation
├── README.md                 # Project overview and instructions
├── scripts/                  # Python scripts for data preprocessing and modeling
├── plots/                    # Visualizations of predictions and simulations
└── data/                     # Dataset used for training and testing

Getting Started

Prerequisites

• Python 3.7 or later
• Required Python libraries:

  pip install -r requirements.txt

Installation

1. Clone the repository:

   git clone https://github.com/your-username/soil-moisture-prediction.git

2. Navigate to the project directory:

   cd soil-moisture-prediction

3. Install the required libraries:

   pip install -r requirements.txt

Usage

1. Run the preprocessing and modeling scripts:

   python scripts/preprocess_data.py
   python scripts/train_model.py

2. Generate predictions or simulate moisture behavior:

   python scripts/predict_moisture.py

3. Visualize results: Check the plots/ directory for generated visualizations.

Contributing

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature/your-feature-name

3. Commit your changes:

   git commit -m "Add your message here"

4. Push to the branch:

   git push origin feature/your-feature-name

5. Open a pull request.

License

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

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For more details, refer to the soil_moisture_summary.md file.