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🌿 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.

📋 Table of Contents

✨ 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

🌾 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

🚀 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.

💻 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

📁 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)

🛠 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

🧠 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

🎨 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

🔌 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

📊 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

📱 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

🔒 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

🚧 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

🤝 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

📄 License

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

👨‍💻 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)

📞 Support

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

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