Plant Disease Detection System for Sustainable Agriculture

A Deep Learning-based web application to detect and classify plant leaf diseases, promoting sustainable agriculture.

Overview

This project uses a Convolutional Neural Network (CNN) to identify plant diseases from leaf images. The model predicts the type of disease, helping farmers and agricultural experts take timely action.

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🛠️ Technologies Used

• TensorFlow/Keras: To build and train the CNN model for plant disease classification.
• OpenCV: For image preprocessing (resizing, color conversion, and normalization).
• Streamlit: To create an interactive web application for disease detection.
• NumPy: For numerical operations and array manipulations.

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📋 Features

✅ Identify diseases in plants from uploaded images.
✅ Supports multiple plants such as Apple, Corn, Potato, and Tomato.
✅ Provides real-time, accurate disease classification.
✅ User-friendly interface built with Streamlit.

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📷 Model Architecture

• Input layer expects images of size 128x128x3.
• The model predicts the disease class using a Softmax activation.
• Trained on a diverse dataset of plant leaf images for improved accuracy.

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Clone the repository:

git clone https://github.com/Or4cle404/Plant-Disease-Detection.git
cd plant-disease-detection

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🖼️ Usage

1. Go to the "Disease Recognition" section of the app.
2. Upload an image of a plant leaf.
3. Click the "Predict" button.
4. View the disease classification and suggested actions.

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📊 Class Labels

The model supports a wide range of plant diseases, including:

• Apple___Apple_scab
• Corn_(maize)___Common_rust
• Potato___Early_blight
• Tomato___Late_blight
• And many more!

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⭐ Acknowledgments

Special thanks to open-source datasets and libraries that made this project possible!

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✨ Let's build a healthier, more sustainable future for agriculture! ✨