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README.md

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-license: gpl-3.0
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+---
+license: gpl-3.0
+---
+---
+#  Potato and 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
+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.