Crop Disease Detection with Streamlit
Crop Disease Detection is a web application developed to identify and classify diseases in crop images using a pre-trained deep learning model. The application is built with Streamlit and utilizes a convolutional neural network (CNN) model created with TensorFlow and Keras.
#Features Image Upload: Users can upload images of crops to the web application for disease detection. Disease Classification: The application uses a pre-trained CNN model to classify the uploaded image and identify whether the crop is affected by a disease. User-Friendly Interface: The Streamlit interface is designed to be simple and intuitive, allowing users to easily interact with the application. Getting Started
Follow these instructions to set up and run the Crop Disease Detection web application on your local machine.
#Prerequisites Python 3.x Streamlit TensorFlow OpenCV NumPy
#Install the required Python packages using the following command:
bash Copy code pip install -r requirements.txt Running the Application Clone the repository to your local machine: bash Copy code git clone https://github.com/your-username/crop-disease-detection.git cd crop-disease-detection Run the Streamlit application: bash Copy code myenv\Scripts\activate streamlit run app.py
Open your web browser and navigate to
to access the Crop Disease Detection application. Usage Access the application through your web browser. Upload an image of a crop using the provided interface. View the prediction result, indicating whether the crop is affected by a disease. Contributing Contributions to the Crop Disease Detection project are welcome. If you find a bug or have an enhancement in mind, please open an issue or submit a pull request.
Acknowledgments
The Crop Disease Detection model is based on research and development in the field of computer vision and deep learning. Special thanks to the open-source community for providing valuable resources and tools.