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license: gpl-3.0 |
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# Potato & Tomato Disease Classification Web Application |
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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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## 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 |
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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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- **Potato Disease Classification Model** |
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- **Classes:** Potato Early Blight, Potato Late Blight, 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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- **Tomato Disease Classification Model** |
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- **Classes:** Tomato Early Blight, Tomato Late Blight, 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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## Usage |
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- Install the required dependencies using `pip install -r requirements.txt`. |
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- Download the pre-trained model weights and place them in the `models/` directory. |
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- Run the Flask web application using `python app.py`. |
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- Access the application in your web browser at `http://localhost:5000`. |
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## Outcome |
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- **Performance** |
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- **Potato Model:** Achieved an accuracy of 98% 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 97% 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. |
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## App |
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