Welcome to the Crop Recommendation System! This application provides intelligent and data-driven recommendations for the most suitable crops to cultivate based on environmental and soil parameters. By leveraging machine learning models and advanced data preprocessing techniques, it predicts the best crops for your farming environment, helping optimize crop selection and improve agricultural practices. Whether you’re a farmer, researcher, or agricultural enthusiast, this tool can offer valuable insights into farming best practices.
The application predicts the most suitable crop based on the following environmental and soil parameters:
- Nitrogen (N)
- Phosphorus (P)
- Potassium (K)
- Temperature
- Humidity
- pH level
- Rainfall
By analyzing these inputs, the system suggests the best crop tailored to your specific conditions using a trained machine learning model.
In addition to crop prediction, the app integrates AI models to provide comprehensive guidance on:
- Cultivation processes
- Fertilizer recommendations
- Pest prevention strategies
- Optimal growing seasons
Engage in real-time conversations with the AI to get personalized advice on crop cultivation. Ask specific questions about the recommended crop and receive dynamic, data-driven responses to guide your farming practices.
This application integrates various cutting-edge technologies, including:
- Python – The core language for development
- Flask – Lightweight web framework for building the backend
- Streamlit – Interactive web app framework for the frontend
- Scikit-learn – Machine learning library for model training and prediction
- Hugging Face API – For advanced AI-powered agricultural insights
These tools enable a robust and scalable solution for smart farming.
Before you get started, make sure you have the following installed:
- Python 3.x
- Dependencies listed in
Requirements.txt
To get the Crop Recommendation System up and running on your local machine, follow these simple steps:
-
Clone the repository:
git clone https://github.com/AKHIL0238/Crop-Recommendation-System.git
-
Install all dependencies:
Navigate into the project directory and install the necessary Python packages using the following command:
pip install -r Requirements.txt
-
Run the application:
Start the Streamlit app to interact with the system:
streamlit run st_app.py
Once the application is running, you can input various environmental and soil parameters, and the system will predict the most suitable crops and provide valuable insights into agricultural practices.
The machine learning model is trained on data from arid regions, where rainfall ranges between 150-300mm. This training data helps the model generate accurate predictions for environments with similar weather conditions.
U AKHIL