AgriGenie is a ground-breaking mobile application created to empower farmers by offering precise forecasts for seasonal crops and individualized crop and fertilizer suggestions. This software uses cutting-edge data analytics, machine learning algorithms, and agricultural knowledge to help farmers make educated decisions about crop selection and fertilization practices, with the goal of maximizing agricultural output and sustainability.
This repository contains the source code for the Agrigenie App. The app is designed to help farmers predict the most suitable crop based on certain input values and provide recommendations for fertilizers. It aims to assist farmers in making informed decisions about crop selection and optimizing agricultural practices.
- ML Powered Crop prediction based on Soil testing values
- Recommended crop suggestions
- Fertilizer recommendations for selected crops
- User-friendly interface for easy input and navigation
- Flutter: A cross-platform framework for building mobile applications.
- Dart: The programming language used with Flutter.
- Machine Learning and its Algorithms such as Decision tree and libraries like Tensorflow
To get started with the Farmer's Crop Prediction App, follow these steps:
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Clone the repository:
git clone https://github.com/pawanbhayde/agrigenie.git -
Install Flutter by following the official Flutter installation guide: Flutter Installation
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Open the project in your preferred code editor.
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Install dependencies by running the following command in the project directory:
flutter pub get -
Connect a device or start an emulator.
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Run the app using the following command:
flutter run -
The Agrigenie App should now be running on your device/emulator.
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All the ML Files , dumps are Stored in Respective Folders
Contributions to the Farmer's Crop Prediction App are welcome! If you find any bugs, have feature requests, or would like to contribute in any way, please follow these steps:
- Fork the repository.
- Create a new branch for your feature/bug fix.
- Make the necessary changes in your branch.
- Test your changes to ensure they work as expected.
- Commit and push your changes to your forked repository.
- Open a pull request against the main repository.
- Provide a clear description of your changes and why they should be merged.
Please note that all contributions will be reviewed before being merged.
This project is licensed under the MIT License.
Special thanks to all the contributors, guides who have helped in developing the Open Source Farmer's Crop Prediction App.
The Entire Projects is our Mini Project in association with Department of Artificial Intelligence , GH Raisoni College of Engineering Nagpur by Pawan Bhayde and Prasanna Muppidwar and Guide Prof. Priyanka Ujjainkar Ma'am
If you have any questions, suggestions, or feedback, feel free to reach out:
- Your Name: [Prasanna Muppidwar]
- Project Link: GitHub Repository