Update README.md

README.md

@@ -1,19 +1,21 @@
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 license: gpl-3.0
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-#                                  Potato and Tomato Disease Classification Web Application
----
+# Potato and Tomato Disease Classification Web Application
+
 
-## Overview
+### 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
+
+### 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
+### 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.
@@ -21,28 +23,30 @@ This project is a web application developed using Flask that allows users to upl
 - **PIL (Pillow):** For image processing.
 - **OpenCV:** For image display and preprocessing.
 - **Torchvision:** For image transformation utilities.
+
   
-## Models
-1. **Potato Disease Classification Model**
-- **Classes:**
+### Models
+- **Potato Disease Classification Model**
+   - **Classes:**
 Potato Early Blight
 Potato Late Blight
 Potato Healthy
-- **Techniques Used:**
+   - **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 Disease Classification Model**
+   - **Classes:**
     Tomato Early Blight
     Tomato Late Blight
     Tomato Healthy
-- **Techniques Used:**
+   - **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
+
+### 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.
 
@@ -57,10 +61,11 @@ The frontend is developed using HTML and CSS, with Bootstrap for responsive desi
   - **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
+
+### 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.
+  - **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.