Potato & Tomato Disease Classification Web Application

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.

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

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.

Usage

Install the required dependencies using pip install -r requirements.txt.
Download the pre-trained model weights and place them in the models/ directory.
Run the Flask web application using python app.py.
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.