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https://github.com/sudarshanc00/potato-disease-classification

This machine learning project classifies potato leaf images into three categories: Early Blight, Late Blight, and Healthy. Using a CNN model, it accurately detects potato diseases based on leaf images. The project includes a FastAPI-powered web interface for image uploads and real-time classification results.
https://github.com/sudarshanc00/potato-disease-classification

css fastapi html keras matplotlib python tensorflow

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This machine learning project classifies potato leaf images into three categories: Early Blight, Late Blight, and Healthy. Using a CNN model, it accurately detects potato diseases based on leaf images. The project includes a FastAPI-powered web interface for image uploads and real-time classification results.

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README 

        
# Potato Disease Classification



## Tech Stack

![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)

![TensorFlow](https://img.shields.io/badge/TensorFlow-%23FF6F00.svg?style=for-the-badge&logo=TensorFlow&logoColor=white)

![Keras](https://img.shields.io/badge/Keras-%23D00000.svg?style=for-the-badge&logo=Keras&logoColor=white) 

![Matplotlib](https://img.shields.io/badge/Matplotlib-%23ffffff.svg?style=for-the-badge&logo=Matplotlib&logoColor=black)

![FastAPI](https://img.shields.io/badge/FastAPI-005571?style=for-the-badge&logo=fastapi) 

![CSS3](https://img.shields.io/badge/css3-%231572B6.svg?style=for-the-badge&logo=css3&logoColor=white) 

![HTML5](https://img.shields.io/badge/html5-%23E34F26.svg?style=for-the-badge&logo=html5&logoColor=white) 



## Overview

This project is a machine learning application designed to classify potato leaf images into three categories: **Early Blight**, **Late Blight**, and **Healthy**. Using a convolutional neural network (CNN), the system accurately identifies and categorizes potato diseases based on leaf images. The project includes a web interface for uploading images and receiving classification results, powered by FastAPI.



## Features

- **Disease Classification**: Classifies potato leaf images into Early Blight, Late Blight, or Healthy.

- **Web Interface**: Provides an HTML interface for image uploads and displays the predicted class and confidence score.

- **API Endpoint**: Built with FastAPI, allowing image uploads for real-time predictions.



## Directory Structure

- **potato-disease/**

  - **api/**

    - `main.py`: FastAPI application with an endpoint for predicting the disease class of an uploaded image.

    - `requirements.txt`: List of dependencies required to run the project.

  - **saved_models/**

    - `version_1_model`: Contains the first version of the trained model.

    - `version_2_model`: Contains the second version of the trained model (optional for updates).

  - **training/**

    - `Potato_Disease_Detection.ipynb`: Jupyter Notebook containing code to train the CNN model on potato leaf images.

  - **index.html**: HTML file providing a simple frontend interface for uploading images and displaying prediction results.



## Installation

1. Clone this repository:

   ```bash

   git clone https://github.com/SudarshanC00/Potato-Disease-Classification.git

   ```

2. Navigate to the project directory:

   ```bash

   cd Potato-Disease-Classification/potato-disease/api

   ```

3. Install dependencies:

   ```bash

   pip install -r requirements.txt

   ```

4. Ensure your model is saved in the `saved_models/version_1_model` directory or update the path in `main.py` if necessary.



## Usage

1. **Start the FastAPI server**:

   ```bash

   uvicorn main:app --reload --host 127.0.0.1 --port 8000

   ```

2. **Access the Web Interface**:

   - Open `index.html` in a browser to use the web interface for image uploads.

   - Upload an image of a potato leaf, and click **Upload** to get the disease classification and confidence score.



3. **Using the API**:

   - Send a `POST` request to `http://localhost:8000/predict` with an image file to receive the classification.



## Model Details

- **Model Architecture**: A convolutional neural network (CNN) was used to classify potato leaf images.

- **Classes**: The model predicts one of the following classes:

  - Early Blight

  - Late Blight

  - Healthy

- **Training Details**: The model was trained using images of potato leaves with different diseases, achieving high accuracy in distinguishing between classes.



## Dependencies

- **FastAPI**: For serving the API endpoint.

- **Uvicorn**: ASGI server for running the FastAPI application.

- **TensorFlow**: For loading and utilizing the trained CNN model.

- **NumPy** and **Pillow**: For image preprocessing.



## License

This project is licensed under the MIT License.