Update README.md

README.md

@@ -8,59 +8,59 @@ license: gpl-3.0
 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.
+- **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.
+- **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:
+1. **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:
+2. **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
-- Backend
+- **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.
+- **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
+- **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.
+- **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.
+- **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.