CropForesight (Frontend)

This Repository includes the frontend code of the CropForesight website. The frontend of the project is written in HTML, CSS, Javascript, and ReactJS. Before moving ahead, a short intro about the project.

CropForesight🌾

CropForesight is an advanced website designed to assist farmers and agriculture enthusiasts in making smart choices about which crops to grow on their land. It achieves this by using special computer programs that can learn from data and environmental information. These programs take into account factors like soil nutrients, rainfall, pH levels, and weather conditions. With all this data, CropForesight can accurately predict the best crop to cultivate, helping farmers maximize their productivity and yield. In addition, CropForesight employs an AlexNet model for the classification of tomato leaf diseases. This model analyzes images of tomato leaves to identify and diagnose diseases, helping farmers take timely action to protect their crops. It's like having a knowledgeable farming expert to guide you towards success!

🌐 Live Demo

Table of Contents

• Features
• Technologies
• Usage
• Local Development
• Deployment
• Contributing
• License

Features

• Intelligent crop recommendation based on soil composition, rainfall, pH, potassium, humidity, and temperature.
• User-friendly interface to input land and environmental parameters.
• Efficient machine learning model leveraging Logistic Regression algorithm.
• Efficient Deep learning model leveraging Alexnet Architecture.
• Integrated with Cloudinary, enabling users to upload and analyze images of tomato leaves easily.
• Responsive frontend developed using ReactJS for seamless user experience.
• Scalable backend powered by FastAPI for quick data processing.
• The platform can analyze historical agricultural data to identify trends and patterns, aiding in better decision-making for future crops.
• CropForesight can utilize its data analysis capabilities to predict and warn users about potential pest and disease outbreaks, allowing for timely preventive measures.
• By integrating real-time weather data, CropForesight can provide up-to-date recommendations, adapting to sudden changes in weather patterns for better accuracy.

Technologies

HTML, CSS, Javascript, ReactJS

Usage

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.

Local Development

If you want to contribute to CropForesight or run it locally for development purposes, follow these steps:

1. Clone the frontend repository:

   git clone https://github.com/abhijeet141/CropForesight-FrontEnd.git

2. Change to the project directory:

   cd CropForesight-FrontEnd

3. Install the required dependencies:

   npm install

4. Run the frontend:

   npm start

5. Clone the backend repository:

   git clone https://github.com/abhijeet141/CropForesight_BackEnd.git

6. Change to the CropForesight_BackEnd directory:

   cd CropForesight_BackEnd

7. Install the required dependencies:

   pip install -r requirements.txt

8. Run the backend:

```sh
 uvicorn main:app --reload

9. Open the website in your browser at http://localhost:3000 to access the local instance of CropForesight.

Deployment

✅ 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!

Contributing

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:

1. Fork the Project
2. Clone your forked repository

 git clone https://github.com/<your_github_username>/CropForesight-FrontEnd.git

3. Now go ahead and create a new branch and move to the branch

   git checkout -b fix-issue-<ISSUE-NUMBER>

4. After you have added your changes, follow the following command chain

   • Check the changed files

    git status -s

   • Add all the files to the staging area

     git add .

     or

     git add <file_name1> <file_name2>

   • Commit your changes

    git commit -m "<EXPLAIN-YOUR_CHANGES>"

5. Push your changes

   git push origin fix-issue-<ISSUE-NUMBER>

6. Open a Pull Request

Congratulations! 🎉 you've made your contribution.

Contributors ✨

We would like to express our heartfelt gratitude to the following contributors for their valuable contributions to Friday: We would like to express our heartfelt gratitude to the following contributors for their valuable contributions to CropForesight-FrontEnd:

Thanks to these wonderful people.

License

This project is licensed under the MIT License.

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