🌿 Plant Disease Classification System

A modern, web-based application for detecting and classifying plant diseases using deep learning. Built with Flask and TensorFlow, featuring a beautiful, farmer-friendly interface with real-time camera integration.

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📋 Table of Contents

• Features
• Supported Plants & Diseases
• Installation
• Usage
• Project Structure
• Technologies Used
• Model Architecture
• UI/UX Features
• API Endpoints
• Contributing
• License

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✨ Features

🔍 Disease Detection

• 38 Disease Classes: Classifies diseases across 9 different plant types
• High Accuracy: CNN-based model trained on large dataset
• Real-time Analysis: Fast prediction on uploaded images
• Health Report: Professional results display with disease badges

📷 Image Input Methods

• File Upload: Upload images from your device
• Direct Camera Capture: Take photos directly using device camera
• Camera Switching: Toggle between front and back cameras
• Live Preview: Real-time camera feed with viewfinder overlay

💡 Treatment Recommendations

• Fertilizer Recommendations: Specific fertilizer advice for each disease
• Treatment Tips: Practical tips for disease management
• Tabbed Interface: Easy navigation between fertilizer and tips
• Expert Guidance: Detailed recommendations based on disease type

🎨 Modern UI/UX

• Agricultural Theme: Deep forest green (#2D5A27) and earthy gold (#D4A373) color scheme
• Responsive Design: Fully optimized for mobile and laptop
• Card-Based Layout: Clean, modern card design
• Smooth Animations: Fade-in effects and scanning animations
• High Contrast: Optimized for outdoor/sunlight viewing

📱 Mobile Features

• Hamburger Menu: Collapsible navigation on mobile
• Large Touch Targets: Minimum 50px buttons for easy tapping
• Full-Width Layout: 95% width on mobile devices
• Camera Integration: Native camera access on mobile devices

💻 Desktop Features

• Two-Column Layout: Side-by-side upload and camera options
• Full Navigation: Always-visible navigation bar
• Optimized Spacing: Professional desktop layout

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🌾 Supported Plants & Diseases

The system can detect diseases in the following plants:

🫑 Bell Pepper

• Bacterial Spot
• Healthy

🌿 Cassava

• Bacterial Blight (CBB)
• Brown Streak Disease (CBSD)
• Green Mottle (CGM)
• Healthy
• Mosaic Disease (CMD)

🌽 Corn (Maize)

• Cercospora Leaf Spot (Gray Leaf Spot)
• Common Rust
• Healthy
• Northern Leaf Blight

🍇 Grape

• Black Rot
• Esca (Black Measles)
• Healthy
• Leaf Blight (Isariopsis Leaf Spot)

🥭 Mango

• Anthracnose Fungal Leaf Disease
• Healthy Leaf
• Rust Leaf Disease

🥔 Potato

• Early Blight
• Healthy
• Late Blight

🌾 Rice

• Brown Spot
• Healthy
• Hispa
• Leaf Blast

🌹 Rose

• Healthy Leaf
• Rust
• Sawfly Slug

🍅 Tomato

• Bacterial Spot
• Early Blight
• Healthy
• Late Blight
• Leaf Mold
• Mosaic Virus
• Septoria Leaf Spot
• Spider Mites (Two-Spotted Spider Mite)
• Target Spot
• Yellow Leaf Curl Virus

Total: 38 Disease Classes

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🚀 Installation

Prerequisites

• Python 3.8 or higher
• pip (Python package manager)

Step 1: Clone the Repository

git clone <repository-url>
cd Plant_Disease_Classification

Step 2: Create Virtual Environment

python -m venv venv

Step 3: Activate Virtual Environment

Windows:

.\venv\Scripts\Activate.ps1

Linux/Mac:

source venv/bin/activate

Step 4: Install Dependencies

pip install -r requirements.txt

Step 5: Download and Place Model File

The model file (Team3model.h5) is too large for GitHub. Download it from Google Drive:

📥 Download Team3model.h5 from Google Drive

After downloading:

1. Place the Team3model.h5 file in the project root directory
2. Ensure the file is named exactly Team3model.h5
3. The application will automatically load this model on startup

Note: The model file is required for the application to function. Without it, the disease detection feature will not work.

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💻 Usage

Starting the Application

1. Activate Virtual Environment (if not already activated)

   .\venv\Scripts\Activate.ps1  # Windows
   # or
   source venv/bin/activate     # Linux/Mac

2. Run the Flask Application

   python app.py

3. Access the Application

   • Open your browser and navigate to: http://localhost:5000
   • The application will start on port 5000 by default

Using the Application

1. Login

   • Enter any email and password (authentication is bypassed for demo)
   • Click "Login"

2. Upload or Capture Image

   • Option 1: Click "Upload Image" and select a file from your device
   • Option 2: Click "Open Camera" to capture a photo directly

3. View Results

   • Disease name with color-coded badge (red for infected, green for healthy)
   • Fertilizer recommendations tab
   • Treatment tips tab
   • Uploaded/captured image with scanning animation

4. Analyze Another Image

   • Click "Analyze Another Image" to start over

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📁 Project Structure

Plant_Disease_Classification/
│
├── app.py                          # Main Flask application
├── requirements.txt                 # Python dependencies
├── Team3model.h5                    # Trained CNN model
├── README.md                        # Project documentation
│
├── static/
│   ├── style.css                    # Shared stylesheet
│   └── uploads/                     # Uploaded images directory
│
├── templates/
│   ├── login.html                   # Login page
│   ├── home.html                    # Home page
│   ├── disease-recognition.html     # Disease detection page
│   └── prediction.html              # Results page
│
└── venv/                            # Virtual environment (created after setup)

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🛠 Technologies Used

Backend

• Python 3.11: Programming language
• Flask 3.0.0: Web framework
• TensorFlow 2.15.0: Deep learning framework
• Keras 2.15.0: High-level neural networks API
• NumPy 1.24.3: Numerical computing
• Pillow 10.1.0: Image processing

Frontend

• HTML5: Markup language
• CSS3: Styling with modern features
• JavaScript: Client-side interactivity
• Inter Font: Modern typography (Google Fonts)

Machine Learning

• Convolutional Neural Network (CNN): Model architecture
• Image Preprocessing: Resizing, normalization
• Transfer Learning: Pre-trained model optimization

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🧠 Model Architecture

The CNN model consists of:

• Input Layer: 256x256x3 (RGB images)
• Convolutional Layers: Multiple Conv2D layers with ReLU activation
• Pooling Layers: MaxPooling2D for dimensionality reduction
• Dropout Layers: Regularization to prevent overfitting
• Dense Layers: Fully connected layers for classification
• Output Layer: 38 classes (softmax activation)

Model Details

• Total Parameters: ~59 million
• Training Accuracy: ~65%
• Validation Accuracy: ~81%
• Test Accuracy: ~65%

Model File Download

The trained model file (Team3model.h5) is available for download:

📥 Download Team3model.h5

Important:

• The model file is not included in the repository due to its large size
• You must download it separately and place it in the project root directory
• The file must be named Team3model.h5 for the application to work

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🎨 UI/UX Features

Design System

• Primary Color: Deep Forest Green (#2D5A27)
• Accent Color: Earthy Gold (#D4A373)
• Background: Clean Off-White (#F8F9FA)
• Typography: Inter font family
• Border Radius: 16px for modern look

Key UI Components

1. Sticky Navigation Bar: Always accessible navigation
2. Card-Based Layout: Clean, organized content cards
3. Health Report Style Results: Professional disease report display
4. Scanning Animation: Visual feedback during image processing
5. Tabbed Interface: Easy navigation between fertilizer and tips
6. Responsive Grid: Adapts to screen size

Animations

• Fade-in: Smooth page load animations
• Scanning Line: Green laser scan over uploaded images
• Button Hover: Scale and shadow effects
• Smooth Transitions: All interactive elements

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🔌 API Endpoints

Routes

Method	Endpoint	Description
GET	/	Redirects to login page
GET/POST	/login	Login page and authentication
GET	/home	Home page with features and supported plants
GET/POST	/disease-recognition	Disease detection page (upload/camera)
GET	/logout	Logout and redirect to login

Form Submission

• Endpoint: /disease-recognition
• Method: POST
• Content-Type: multipart/form-data
• Field: file (image file)
• Response: Renders prediction page with results

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📊 Treatment Recommendations

Each disease includes:

🌾 Fertilizer Recommendations

• Specific fertilizer types (e.g., Potassium Sulfate, NPK 10-10-10)
• Application methods and timing
• Nutrient requirements

💡 Treatment Tips

• Cultural practices
• Chemical treatments
• Prevention strategies
• Environmental management

Examples:

• Bacterial Diseases: Copper-based bactericides, pathogen-free seeds
• Fungal Diseases: Fungicides, proper spacing, crop rotation
• Viral Diseases: Vector control, resistant varieties
• Healthy Plants: Maintenance recommendations

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📱 Responsive Design

Mobile (< 992px)

• Hamburger menu navigation
• Single-column stacked layout
• Full-width buttons (95% screen width)
• Large touch targets (50px minimum)
• Camera view optimized for mobile
• Vertical scrolling only

Desktop (≥ 992px)

• Full navigation bar
• Two-column layout for upload/camera
• Standard button sizes
• Side-by-side content arrangement
• Optimized spacing and padding

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🔒 Security Features

• Session Management: Flask sessions for user authentication
• File Upload Security: Secure filename handling
• Input Validation: Form validation on client and server
• Error Handling: Graceful error messages

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🚧 Future Enhancements

Potential improvements:

• User authentication system
• Disease history tracking
• Export reports (PDF)
• Multi-language support (Telugu ready)
• Batch image processing
• Disease severity assessment
• Treatment cost calculator
• Integration with agricultural databases

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🤝 Contributing

Contributions are welcome! Please feel free to:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

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📄 License

This project is open-source and free to use under the MIT License.

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👨‍💻 Development

Running in Development Mode

The application runs with debug=True by default, enabling:

• Auto-reload on code changes
• Detailed error messages
• Development server

Production Deployment

For production, set debug=False in app.py:

if __name__ == '__main__':
    app.run(debug=False, host='0.0.0.0', port=5000)

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📞 Support

For issues, questions, or contributions, please open an issue on the repository.

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🌾 Empowering farmers with AI-powered plant disease detection
© 2025 Plant Disease Classification System – All rights reserved.