Plant Disease

Overview This project is a machine learning web application utilizing a Convolutional Neural Network (CNN) to make predictions based on user input data. The project includes a Jupyter notebook for training and evaluating the CNN model and a Streamlit-based web application for interaction with the model.

Project Structure

Train_Dataset.ipynb: A Jupyter notebook focused on:

• Data preprocessing and exploration
• Model training using a CNN for effective feature extraction and classification
• Evaluation of the model's performance
• Saving the trained CNN model for use in the web app

App.py: A Streamlit web application that enables:

• User input of data for predictions
• Integration with the CNN model for generating and displaying results

Model Training

The CNN model is trained on a prepared dataset in Train_Dataset.ipynb, which includes:

• Training and validation phases to assess accuracy and loss
• Saving the trained CNN model for predictions in the web application

Home Page

Prediction Result

Project Video

Check out the video for a demonstration of the project.

Technical Details

• Framework: Streamlit, Tensorflow
• Model Used: CNN, XGBoost
• Programming Language: Python
• Development Tools: Jupyter Notebook for initial model development and testing

Check out the Plant Disease Prediction App on Streamlit

License

This project is licensed under the GNU General Public License v3.0. See the LICENSE file for details.