Harvest Harmony

Harvest Harmony is a crop recommendation system designed to assist farmers in selecting the most suitable crops based on their soil data. This application utilizes machine learning techniques to analyze various soil parameters and provide recommendations for crops that are likely to thrive in the given conditions.

Features

• Soil Data Input: Farmers can easily input their soil data, which includes nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall, through a user-friendly form.
• Crop Recommendation: Based on the provided soil data, Harvest Harmony utilizes a machine learning model to recommend a list of crops that are most likely to thrive in the given conditions.

Getting Started

To get started with Harvest Harmony, follow these steps:

1. Clone the repository:

   git clone https://github.com/pkshetty15/harvest-harmony.git

2. Change working directory:

   cd harvest-harmony

3. Setup Virtual Environment:*

   python -m venv .venv

4. Activate the virtual environment:*

   • On Windows:

     .venv\Scripts\activate

   • On macOS and Linux:

     source .venv/bin/activate

5. Install the required dependencies:

   pip install -r requirements.txt

6. Configure the application settings in config.py

7. Run the application:

   python run.py

   or

   flask run

8. Access the application in your web browser at http://localhost:5000

* Note: Steps 3 and 4 are optional but recommended to isolate the project dependencies from other Python projects on your system. To know more about virtual environments, refer to this guide.

Prerequisites

• Python 3.6 or higher
• Flask
• scikit-learn (or any other machine learning library used)
• Additional dependencies listed in requirements.txt

Contributing

We welcome contributions from the community to further enhance Harvest Harmony. If you encounter any issues or have suggestions for improvements, please submit them to our issue tracker.

To contribute to the project, follow these steps:

1. Fork the repository
2. Create a new branch: git switch -c branch-name or git checkout -b branch-name
3. Make your changes and commit them: git commit -m 'Add some feature'
4. Push to the branch: git push origin branch-name
5. Submit a pull request

License

This project is licensed under the MIT License.

Acknowledgments

• Flask - The web framework used
• scikit-learn - Machine learning library (or any other library used)
• matplotlib - Data visualization library
• pandas - Data manipulation library