- Introduction
- Features
- Technologies
- Usage
- Local Development
- Deployment
- License
- Intelligent crop recommendation based on soil composition, rainfall, pH, potassium, humidity, and temperature.
- User-friendly interface to input land and environmental parameters.
- Integrated with Cloudinary, enabling users to upload and analyze images of tomato leaves easily.
- Efficient machine learning model leveraging Logistic Regression Algorithm.
- Efficient Deep learning model leveraging Alexnet Architecture.
- Responsive frontend developed using ReactJS for seamless user experience.
- Scalable backend powered by FastAPI for quick data processing.
CropForesight leverages the following technologies:
- ReactJS (Frontend): A popular JavaScript library for building interactive user interfaces.
- FastAPI (Backend): A modern, fast (high-performance) web framework for building APIs with Python 3.7+.
- Logistic Regression (Model): A machine learning algorithm used for classification tasks.
- AlexNet (Model): A deep convolutional neural network architecture known for its ability to classify images with high accuracy. Back to top
To experience the power of CropForesight, follow these simple steps:
✅ Visit the CropForesight website: https://abhijeet141.github.io/CropForesight-FrontEnd/.
✅ Enter the required details such as soil nitrogen value, phosphorus value, rainfall, pH, potassium, humidity, and temperature.
✅ Click on the "Recommend Crop" button to generate the optimal crop recommendation.
✅ Explore the recommended crop and gain insights into its suitability for your land.
We welcome contributions from anyone who is interested in improving this project. If you'd like to contribute, here are some ways you can get started:
- Submit a bug report if you find any issues with the application.
- Suggest new features or improvements.
- Submit a pull request to fix a bug or add a feature after an issue is assigned to you.
To submit a pull request, please follow these steps:
-
Fork the repository and create your branch:
git checkout -b your-branch-name -
Make your changes and commit them:
git commit -m 'Add some feature' -
Push to your forked repository:
git push origin your-branch-name -
Open a pull request to the main repository's branch
Congratulations! 🎉 you've made your contribution.
Please follow the cotribution guide in all your interactions with the project. We will review your pull request and provide feedback. Once your changes are approved, we will merge them into the main branch.
If you want to contribute to CropForesight or run it locally for development purposes, follow these steps:
-
Clone the frontend repository:
git clone https://github.com/your_username/CropForesight-FrontEnd.git -
Change to the project directory:
cd CropForesight-FrontEnd -
Install the required dependencies:
npm install -
Run the frontend:
npm start -
Change to the CropForesight_BackEnd directory:
cd BackEnd -
Change to the CropForesight_BackEnd_ML directory:
cd backend_ML -
Install the required dependencies:
pip install -r requirements.txt -
Run the backend:
uvicorn main:app --reload -
Change to the CropForesight_BackEnd_DL directory:
cd backend_DL -
Install the required dependencies:
pip install -r requirements.txt
- Run the backend:
uvicorn main:app --reload
- Open the website in your browser at http://localhost:3000 to access the local instance of CropForesight.
✅ CropForesight's frontend is deployed and can be accessed online at https://crop-foresight-front-end.vercel.app/.
✅ Feel free to explore the website and witness the power of smart crop recommendation firsthand!
This project is licensed under the MIT License.
Please feel free to modify the sections and add any additional information or badges relevant to your project. Let me know if you need further help.
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