Welcome to CropDetection, an innovative project by Team KhetiMitr for detecting plant diseases using imagery and advanced machine learning techniques.

CropDetection aims to assist in identifying plant diseases efficiently by analyzing images of crop leaves. Simply upload an image, and our system will process it to detect any signs of diseases.

• Accuracy: Utilizes state-of-the-art machine learning techniques for accurate disease detection.
• User-Friendly: Simple and intuitive interface for seamless user experience.
• Fast and Efficient: Receive results in seconds, allowing for quick decision-making.

LINK : Click Here.

1. Upload Image: Go to the Disease Recognition page and upload an image of a plant with suspected diseases.
2. Analysis: The system processes the image using advanced algorithms to identify potential diseases.
3. Results: View the results and recommendations for further action.

• Python 3.6 or higher
• TensorFlow
• Streamlit
• NumPy

1. Clone the repository:

   git clone https://github.com/ivarungupta7/KhetiMitr-new.git
   cd KhetiMitr-new

2. Install the required packages:

   pip install -r requirements.txt

3. Ensure you have the trained model file trained_model.h5 in the project directory.

1. Navigate to the project directory.

2. Run the Streamlit application:

   streamlit run main.py

3. Open your browser and go to http://localhost:8501 to access the application.

CropDetection/ ├── main.py ├── trained_model.h5 ├── logo_wheat_with.png ├── background_check.jpg ├── requirements.txt └── README.md

Team KhetiMitr is dedicated to leveraging technology to improve agriculture. Learn more about the project, our team, and our goals on the About page in the application.

We welcome contributions to enhance the project. Please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit your changes (git commit -m 'Add new feature').
5. Push to the branch (git push origin feature-branch).
6. Open a pull request.

• Special thanks to the creators of the dataset used for training the model.
• Thanks to the open-source community for their invaluable tools and resources.

For any inquiries, please reach out to us at ivarungupta7@gmail.com.

Thank you for using CropDetection By KhetiMitr! Let's work together to protect our crops and ensure a healthier harvest! 🌿🔍 .