Update README.md

README.md

@@ -46,21 +46,21 @@ Dropout layers to prevent overfitting.
 - **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**
 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.
+  - **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.
+  - **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.