- Technologies Used: TensorFlow, Keras
- IDE Used: Jupyter Notebook
- Utilizes TensorFlow and Keras for CNN-based image classification.
- Dataset: PlantVillage.
- Data preprocessing, augmentation, and model training.
- Evaluation and visualization of training/validation metrics.
- Model saving and conversion to TensorFlow Lite format.
- PlantVillage dataset used for training the CNN model.
Note: Detailed code and data storage specifics are available in the Jupyter Notebook (training.ipynb).
- Main webpage for Potato Disease Classification.
- User-friendly interface for image upload and classification results.
- Responsive design with style customization.
- Technologies Used: HTML, CSS
- CSS styling for the front end, enhancing visual appeal and responsiveness.
- Defines layout, colors, and animations for a seamless user experience.
- Technologies Used: JavaScript
- JavaScript file handling user interactions.
- Enables image upload, displays a preview, and triggers API calls for disease classification.
- Technologies Used: FastAPI, TensorFlow
- IDE Used: PyCharm
- Utilizes FastAPI to create an API for potato disease classification.
- Allows CORS for specified frontend URLs.
- Loads a pre-trained TensorFlow model for disease classification.
- Exposes an endpoint "/ping" for a basic health check.
- Exposes an endpoint "/predict" to receive images for classification.
- Returns the predicted class and confidence.
- Technologies Used: Android Studio, TensorFlow Lite
- Developed using Android Studio.
- Utilizes a TensorFlow Lite (tflite) model produced in the training.ipynb notebook.
- Incorporates functionality to capture or select an image.
- Sends the image to the FastAPI backend "/predict" endpoint for classification.
- Displays the predicted class obtained from the backend.
- Provides a user-friendly interface for interacting with the potato disease classification system.