Cyomorokenny1/CropDoc

Advance ai technology to detect plant Disease in early stage and offer treatment advice in both English and Kinyarwanda # AI Crop Doctor - Web Application A powerful **web-only** plant disease detection system using AI and computer vision. Upload crop images to get instant disease diagnosis and treatment recommendations. **🌐 100% Browser-Based** - No React Native dependencies, fully optimized for web browsers with TensorFlow.js client-side AI. ## 🌟 Features - **📸 Image Upload & Camera Capture** - Upload crop images or use device camera - **🤖 AI Disease Detection** - Powered by TensorFlow.js for client-side inference - **💊 Treatment Recommendations** - Detailed advice for detected diseases - **📊 Confidence Scoring** - AI prediction confidence levels - **📱 Mobile-Friendly** - Responsive design for all devices - **📈 Analysis History** - Local storage of previous predictions - **🎨 Modern UI/UX** - Clean, nature-inspired design with smooth animations ## 🚀 Quick Start ### Prerequisites - **Node.js 16+** - Download here - **npm** - Comes automatically with Node.js ### Installation Steps 1. **Get the project files:** ```bash # Option A: Download and extract the project files # Option B: Clone from Git repository git clone [your-repository-url] cd ai-crop-doctor ``` 2. **Install all dependencies:** ```bash npm install ``` If you encounter any errors: ```bash # Clear cache and reinstall rm -rf node_modules package-lock.json npm install ``` 3. **Start the development server:** ```bash npm run dev ``` 4. **Open in your browser:** - Navigate to `localhost` - The app should load immediately! ### ✅ Verify Everything Works - You should see the AI Crop Doctor interface - Try uploading a test image - The AI analysis should work (demo mode) ## 🔧 Configuration & Customization ### Adding New Plant Disease Classes To add new plant disease classes, edit the disease labels and advice: 1. **Update Disease Labels** (`src/utils/diseaseModel.ts`): ```typescript // To add new plant classes, edit this array... export const DISEASE_LABELS = [ 'Healthy', 'Early Blight', 'Late Blight', …