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license: gpl-3.0 |
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# Potato and Tomato Disease Classification Web Application |
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## Overview |
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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. |
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## Key Features |
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- Image Upload: Users can upload images of potato or tomato leaves. |
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- Disease Prediction: The application predicts whether the leaf is healthy or affected by specific diseases. |
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- Dynamic Background: The background image of the web page dynamically changes based on whether the user selects potato or tomato. |
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- Probability Display: The probability of the predicted class is displayed as a percentage. |
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## Technologies Used |
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- Python: Core programming language used for model development and Flask backend. |
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- Flask: Web framework for developing the web application. |
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- PyTorch: Deep learning framework used to develop and train the models. |
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- HTML/CSS: For creating the frontend of the web application. |
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- PIL (Pillow): For image processing. |
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- OpenCV: For image display and preprocessing. |
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- Torchvision: For image transformation utilities. |
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## Models |
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1. Potato Disease Classification Model |
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- Classes: |
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Potato Early Blight |
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Potato Late Blight |
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Potato Healthy |
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- Techniques Used: |
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Convolutional layers for feature extraction. |
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Batch normalization and max pooling for enhanced training stability and performance. |
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Dropout layers to prevent overfitting. |
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2. Tomato Disease Classification Model |
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- Classes: |
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Tomato Early Blight |
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Tomato Late Blight |
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Tomato Healthy |
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- Techniques Used: |
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Similar architecture to the potato model with appropriate adjustments for tomato disease classification. |
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Batch normalization, max pooling, and dropout layers are also used here. |
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## Web Application |
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- Backend |
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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. |
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- Model Loading: Both models (potato and tomato) are loaded at the start of the application to minimize latency during prediction. |
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- Prediction Logic: Depending on the selected plant type (potato or tomato), the corresponding model is used to predict the disease class. |
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- Dynamic Background: The background image on the frontend changes based on the selected plant type, enhancing user experience. |
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- Frontend |
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The frontend is developed using HTML and CSS, with Bootstrap for responsive design. |
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- File Upload Interface: Users can upload an image of a leaf. |
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- Prediction Display: After processing, the application displays the predicted disease class and the associated probability. |
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- Dynamic Background: The background image changes depending on whether the user is predicting for potato or tomato. |
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## Outcome |
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- Performance |
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- Potato Model: Achieved an accuracy of 95% on the validation set, with strong performance in classifying Early Blight, Late Blight, and Healthy leaves. |
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- Tomato Model: Achieved an accuracy of 93% on the validation set, effectively distinguishing between Early Blight, Late Blight, and Healthy leaves. |
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- Benefits |
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- Disease Detection: Helps farmers and agriculturists detect diseases in potato and tomato plants early, potentially preventing crop losses. |
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- User-Friendly Interface: The web application provides a simple interface for non-technical users to diagnose plant diseases. |
