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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Potato Disease Classification Model
- Classes: Potato Early Blight, Potato Late Blight, Potato Healthy
- Techniques Used:
- Convolutional layers for feature extraction.
- Batch normalization and max pooling for enhanced training stability and performance.
- Dropout layers to prevent overfitting.
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Tomato Disease Classification Model
- Classes: 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.
