The Crop Recommendation System is a machine learning-based application designed to recommend the most suitable crop types for given environmental conditions. Utilizing data on soil type, climate, and precipitation, our system leverages a RandomForestClassifier to provide data-driven crop recommendations. This tool aims to assist farmers, agricultural researchers, and hobbyists in making informed decisions to maximize yield and sustainability.
- Data-Driven Recommendations: Utilizes historical farming data to predict optimal crop types.
- Environmental Consideration: Takes into account various environmental factors such as soil type, climate, and precipitation.
- Machine Learning Integration: Employs RandomForestClassifier for accurate prediction models.
- Easy Integration: Designed to be easily integrated with other systems through a straightforward API.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
- Python 3.8 or newer
- pip
- Clone the repository to your local machine:
git clone https://your-repository-url.git
cd crop-recommendation-system- Install the required Python packages:
pip install -r requirements.txtTo predict the recommended crop type based on environmental conditions, run:
python recommend_crop.py --soil_type loamy --climate temperate --precipitation 500Replace the values for --soil_type, --climate, and --precipitation with your specific conditions.
We welcome contributions to the Crop Recommendation System! Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests to us.
This project is proprietary and protected under a custom license agreement. For licensing information, please contact the repository owner.
For further inquiries, including potential use cases and collaboration, please reach out to androidextractions@gmail.com.
- Thanks to all contributors who have helped to build this project.