🌱 Plant Disease Detection Project

📋 Project Overview

This project is an innovative approach to leveraging cutting-edge image processing, machine learning, and deep learning techniques for the detection of plant diseases from leaf images. By creating a comprehensive and efficient pipeline, we aim to revolutionize agricultural diagnostics.

Key Features:

• End-to-End Solution: From preprocessing to deployment.
• Accurate Predictions: Achieving state-of-the-art results with machine learning and deep learning models.
• User-Friendly Deployment: A fully functional web application for real-time disease detection.

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🛠️ Pipeline Breakdown

Preprocessing

• 🖼️ Image Augmentation: Enhances dataset diversity to improve model generalization.
• ❌ Noise Removal: Filters out irrelevant artifacts for cleaner inputs.
• 🌟 Contrast Enhancement: Boosts the visibility of critical features.

Texture Analysis

Sophisticated feature extraction techniques:

• 📊 GLCM (Gray Level Co-occurrence Matrix): Captures textural patterns.
• 🖼️ LBP (Local Binary Patterns): Extracts local texture descriptors.
• 🌈 RGB Histogram: Analyzes color distribution.
• 🎨 HSV Histogram: Captures hue, saturation, and value properties.

Machine Learning Models

Hyperparameter tuning conducted with Optuna on:

• 🌟 XGBoost: Delivered the best results.
• 🌳 Random Forest
• 📈 KNN
• 📉 SVM
• 🌲 Decision Tree

Best Results:

• XGBoost with HSV features:
  • ✅ Validation Accuracy: 96%
  • ✅ Test Accuracy: 84%

Deep Learning with MobileNet

• Achieved exceptional results:
  • ✅ Test Accuracy: 95%

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🚀 Deployment Details

The project integrates a powerful backend and an intuitive frontend:

• 🔧 Backend: Flask, providing a robust API for model inference.
• 🌐 Frontend: React, ensuring a seamless user experience.

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📂 Dataset

The dataset used for this project is publicly available and can be downloaded from: 🔗 Plant Disease Dataset

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🚀 How to Operate the Project

🛠️ Step 1: Start the Backend (Flask)

1. Install the required libraries:

   pip install -r requirements.txt

2. Run the Flask app:

   python app.py

🌐 Step 2: Start the Frontend (React)

1. Navigate to the frontend directory:

   cd frontend

2. Install the required packages:

   npm install

3. Run the React app:

   npm start

🖼️ Step 3: Test with Images

1. Open your browser and navigate to http://localhost:3000.
2. Upload an image of a plant leaf.
3. View predictions and analysis in real-time with detailed results.

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📊 Key Results

Machine Learning (XGBoost + HSV Features)

• ✅ Validation Accuracy: 96%
• ✅ Test Accuracy: 84%

MobileNet

• ✅ Test Accuracy: 95%

Deployment

• 🌟 Fully functional web application combining Flask backend and React frontend.
• 🌟 Real-time predictions with a user-friendly interface.

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💡 Future Enhancements

• 🌍 Multi-Language Support: Expand the application to support multiple languages for global accessibility.
• 📱 Mobile App: Develop a mobile version for on-the-go disease detection.
• 📈 Continuous Learning: Implement a feedback loop to improve model accuracy with user-uploaded images.
• 🌟 Cloud Integration: Deploy on cloud platforms for scalable and fast inference.

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🤝 Contributing

We welcome contributions to enhance the project. Please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature.
3. Submit a pull request with detailed explanations.

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👥 Contributors

Salma Muhammed

Mostafa Rabie

Abdulrahman Mostafa

Mohamed Khaled

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📝 License

This project is licensed under the MIT License. Feel free to use, modify, and distribute this project as per the license terms.

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Thank you for exploring the Plant Disease Detection Project. Together, let's make agriculture smarter and more sustainable!