README.md

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license: gpl-3.0
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# Potato & Tomato Disease Classification Web Application


### Overview
This project is a web application developed using Flask that allows users to upload images of potato or tomato leaves and receive predictions regarding potential diseases. The application utilizes two deep learning models: one trained to classify potato leaf diseases and another for tomato leaf diseases. Both models were trained using convolutional neural networks (CNNs) and implemented using PyTorch.


### Key Features
- **Image Upload:** Users can upload images of potato or tomato leaves.
- **Disease Prediction:** The application predicts whether the leaf is healthy or affected by specific diseases.
- **Dynamic Background:** The background image of the web page dynamically changes based on whether the user selects potato or tomato.
- **Probability Display:** The probability of the predicted class is displayed as a percentage.

  
### Technologies Used
- **Python:** Core programming language used for model development and Flask backend.
- **Flask:** Web framework for developing the web application.
- **PyTorch:** Deep learning framework used to develop and train the models.
- **HTML/CSS:** For creating the frontend of the web application.
- **PIL (Pillow):** For image processing.
- **OpenCV:** For image display and preprocessing.
- **Torchvision:** For image transformation utilities.

  
### Models
- **Potato Disease Classification Model**
   - **Classes:**
Potato Early Blight,
Potato Late Blight,
Potato Healthy
   - **Techniques Used:**
      -  Convolutional layers for feature extraction.
      -  Batch normalization and max pooling for enhanced training stability and performance.
      -  Dropout layers to prevent overfitting.

- **Tomato Disease Classification Model**
   - **Classes:**
    Tomato Early Blight,
    Tomato Late Blight,
    Tomato Healthy
   - **Techniques Used:**
      -  Similar architecture to the potato model with appropriate adjustments for tomato disease classification.
      -  Batch normalization, max pooling, and dropout layers are also used here.


### Web Application
- **Backend**
The backend of the application is powered by Flask. It loads the trained models, handles image uploads, processes images, and returns predictions to the frontend.

  - **Model Loading:** Both models (potato and tomato) are loaded at the start of the application to minimize latency during prediction.
  - **Prediction Logic:** Depending on the selected plant type (potato or tomato), the corresponding model is used to predict the disease class.
  - **Dynamic Background:** The background image on the frontend changes based on the selected plant type, enhancing user experience.

- **Frontend**
The frontend is developed using HTML and CSS, with Bootstrap for responsive design.

  - **File Upload Interface:** Users can upload an image of a leaf.
  - **Prediction Display:** After processing, the application displays the predicted disease class and the associated probability.
  - **Dynamic Background:** The background image changes depending on whether the user is predicting for potato or tomato.


### Usage
1. Install the required dependencies using ```pip install -r requirements.txt```.
2. Download the pre-trained model weights and place them in the `models/` directory.
3. Run the Flask web application using ```python app.py```.
4. Access the application in your web browser at `http://localhost:5000`.

### Outcome
- **Performance**
  - **Potato Model:** Achieved an accuracy of 98% on the validation set, with strong performance in classifying Early Blight, Late Blight, and Healthy leaves.
  - **Tomato Model:** Achieved an accuracy of 97% on the validation set, effectively distinguishing between Early Blight, Late Blight, and Healthy leaves.
- **Benefits**
  - **Disease Detection:** Helps farmers and agriculturists detect diseases in potato and tomato plants early, potentially preventing crop losses.
  - **User-Friendly Interface:** The web application provides a simple interface for non-technical users to diagnose plant diseases.