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license: gpl-3.0
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#  Potato and Tomato Disease Classification Web Application
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## 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.
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## 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.
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## Models
1. 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.

2. 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.

## Outcome
- Performance
--  Potato Model: Achieved an accuracy of 95% on the validation set, with strong performance in classifying Early Blight, Late Blight, and Healthy leaves.
-- Tomato Model: Achieved an accuracy of 93% 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.