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The Crop Yield Prediction System uses machine learning to forecast agricultural yields and provides essential crop information. Integrating weather, soil, and historical data, it offers accurate predictions and supports models like Linear Regression, Random Forest, and Neural Networks.

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Crop Yield Prediction System (CYPS)

Overview

The Crop Yield Prediction System (CYPS) is a web application designed to provide detailed information about various crops, including their scientific names, descriptions, uses, nutritional values, and optimal growing conditions. This system allows users to view a list of crops and access detailed information about each crop.

Features

  • List of crops with links to detailed information.
  • Detailed crop pages with sections on scientific name, description, uses, nutritional values, and growing conditions.
  • User-friendly interface for navigating crop information.

Technologies Used

  • Python 3: The core programming language used for backend development.
  • Flask: A lightweight WSGI web application framework used for building the web application.
  • HTML/CSS: For structuring and styling the web pages.
  • Jinja2: For templating in Flask.

Installation

Prerequisites

  • Python 3.x installed on your machine.
  • pip, the Python package installer.

Steps

  1. Clone the repository:

    git clone https://github.com/lawalTheWest/_CYPS_.git
    cd crop-yield-prediction
  2. Create and activate a virtual environment (optional but recommended):

    python3 -m venv venv
    source venv/bin/activate
  3. Install the required packages:

    pip install -r requirements.txt
  4. Run the application:

    export FLASK_APP=run.py
    export FLASK_ENV=development
    flask run

    The application should now be running at http://127.0.0.1:5000/.

Project Structure


CYPS/
├── app
│   ├── api.py
│   ├── crops.py
│   ├── __init__.py
│   ├── __pycache__
│   │   ├── crops.cpython-310.pyc
│   │   ├── crops.cpython-38.pyc
│   │   ├── __init__.cpython-310.pyc
│   │   ├── __init__.cpython-38.pyc
│   │   ├── routes.cpython-310.pyc
│   │   └── routes.cpython-38.pyc
│   ├── README.md
│   └── routes.py
├── config.py
├── old.py
├── __pycache__
│   ├── app.cpython-38.pyc
│   ├── config.cpython-310.pyc
│   └── config.cpython-38.pyc
├── README.md
├── run.py
├── static
│   ├── css
│   │   ├── about.css
│   │   ├── common.css
│   │   ├── crops.css
│   │   ├── footer.css
│   │   ├── header.css
│   │   ├── index.css
│   │   └── README.md
│   ├── images
│   │   └── lawal.jpg
│   ├── js
│   │   └── script.js
│   └── README.md
└── templates
    ├── 404.html
    ├── about.html
    ├── base.html
    ├── blog.html
    ├── crop_detail.html
    ├── crops.html
    ├── index.html
    ├── layout.html
    ├── README.md
    └── weather.html

  • app/: Contains the application modules and packages.
    • api.py: API endpoints for the application.
    • crops.py: Contains the crop data.
    • routes.py: Contains the Flask routes for handling web requests.
    • init.py: Initializes the Flask application.
  • config.py: Configuration settings for the Flask application.
  • old.py: (Deprecated or old code if not in use).
  • run.py: The main entry point to run the Flask application.
  • static/: Contains static files like CSS, JavaScript, and images.
  • templates/: Contains HTML templates for the web pages.
  • README.md: Project documentation (this file).

Usage

  • Navigate to /crops to see the list of all crops.
  • Click on any crop name to view detailed information about that crop.
  • advanced features Coming soon...

Contributing

Contributions are welcome! Please follow these steps to contribute:

  1. Fork the repository.
  2. Create a new branch for your feature or bug fix.
  3. Commit your changes.
  4. Push the branch to your fork.
  5. Create a pull request with a detailed description of your changes.

License

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

Contact

For any questions or suggestions, feel free to open an issue or contact me at [email protected].

About

The Crop Yield Prediction System uses machine learning to forecast agricultural yields and provides essential crop information. Integrating weather, soil, and historical data, it offers accurate predictions and supports models like Linear Regression, Random Forest, and Neural Networks.

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