AgriSmart is a web application designed to help farmers forecast crop yields and make smart fertilizer and pesticide recommendations based on various factors such as crop type, season, state, area, production, and annual rainfall.
Crop Yield Prediction: Predicts the yield of a selected crop based on user-provided inputs.
Smart Fertilizer and Pesticide Calculator: Recommends the optimal amount of fertilizer and pesticide to maximize crop yield.
User-Friendly Interface: Intuitive and interactive user interface for ease of use.
Data Visualization: Includes charts and graphs to visualize crop distribution and other relevant data.
Python: Backend programming language.
Streamlit: Frontend web framework for building interactive web applications.
Pandas: Data manipulation and analysis library.
Scikit-learn: Machine learning library for training predictive models.
Matplotlib and Seaborn: Data visualization libraries for creating charts and graphs.
Joblib: Library for saving and loading machine learning models.
Clone the repository:
git clone https://github.com/samvitgersappa/Agricultural-Yield.git
Install the required dependencies:
pip install -r requirements.txt
Run the application:
streamlit run app.py
Access the application in your web browser at http://localhost:8501.
Usage
Select the desired functionality from the navigation tabs.
Provide the required inputs such as crop, season, state, area, production, and annual rainfall.
Click the corresponding button (Predict or Recommend) to see the results.
View the yield prediction or fertilizer and pesticide recommendations.
Contributions are welcome! If you'd like to contribute to this project, please follow these steps:
Fork the repository.
Create a new branch (git checkout -b feature/new-feature).
Make your changes.
Commit your changes (git commit -am 'Add new feature').
Push to the branch (git push origin feature/new-feature).
Create a new Pull Request