Update README.md

README.md

@@ -1,8 +1,7 @@
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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
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 ## Overview
@@ -36,32 +35,32 @@ Dropout layers to prevent overfitting.
 
 2. Tomato Disease Classification Model
 - Classes:
-Tomato Early Blight
-Tomato Late Blight
-Tomato Healthy
+    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.
+    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.
+- 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.