AI-Powered Mobile Diagnostics for Farmers
A mobile Android application that uses advanced AI to instantly detect crop diseases through smartphone cameras, empowering farmers with immediate, accurate diagnostics in the field.
- $220 billion in global crop losses annually due to diseases
- Limited access to agricultural experts in rural areas
- Delayed diagnosis leads to widespread crop damage
- Traditional methods require expensive lab testing
Our Solution: Real-time, AI-powered crop disease detection that works offline on any Android smartphone.
- 3-Class Neural Network: Healthy, Diseased, Not-Crop classification
- MobileNetV2 Base: Optimized for mobile deployment
- 1.7MB Model: Fast inference with INT8 quantization
- PlantVillage Dataset: Trained on 50,000+ crop images
- Sub-second Analysis: Real-time processing with confidence scoring
- Offline Operation: No internet required
- Multi-crop Support: Apple, tomato, potato, corn, and more
- Confidence Scoring: Transparent accuracy metrics
- Non-crop Rejection: Distinguishes crops from other objects
- 85-95% accuracy on trained crop species
- 90%+ accuracy for disease detection
- <1 second processing time per analysis
- 1.7MB model size for low-end devices
Apple β’ Cherry β’ Peach β’ Grape β’ Tomato β’ Potato β’ Bell Pepper β’ Squash β’ Corn β’ Strawberry
- Smallholder Farmers (500M+ globally)
- Agricultural Extension Workers
- Agribusiness supply chain
- Agricultural Students
- Early Detection prevents disease spread
- Cost Savings eliminates expensive lab testing
- Increased Yields through faster treatment
- Rural Empowerment via accessible technology
- Android SDK - Native mobile development
- TensorFlow Lite - On-device AI inference
- CameraX - Advanced camera functionality
- Kotlin - Modern Android development
- Material Design - Professional UI/UX
- Point camera at crop leaf
- Tap "Analyze Crop" button
- Get Results with confidence score
- ADTC Branding: Professional agricultural theme
- Visual Feedback: Color-coded results (Green=Healthy, Red=Diseased)
- Confidence Display: Transparent accuracy metrics
- Guidance: Clear instructions and error messages
- Offline Operation: Works without internet
- Low-End Device Support: Optimized for budget phones
- Multiple Languages: Extensible for localization
- Visual Indicators: Color and text feedback
- Dataset Curation: PlantVillage + synthetic non-crop data
- Model Architecture: MobileNetV2 with custom classification head
- Training Strategy: Transfer learning + fine-tuning
- Optimization: INT8 quantization for mobile deployment
- Validation: Comprehensive testing across crop types
- Mobile Development: Native Android with modern architecture
- Image Processing: Real-time camera integration
- AI Integration: TensorFlow Lite deployment
- User Experience: Iterative design and testing
- Performance Optimization: Memory and battery efficiency
- Additional Crops: Expand to rice, wheat, cotton, soybeans
- Disease Specificity: Identify specific disease types
- Treatment Recommendations: Suggest appropriate interventions
- Multi-language Support: Localization for global markets
- Cloud Sync: Optional data backup and sharing
- Expert Network: Connect farmers with agricultural specialists
- Historical Tracking: Monitor crop health over time
- IoT Integration: Connect with farm sensors and equipment
- iOS Version: Expand to Apple ecosystem
- Web Application: Browser-based diagnostics
- API Services: Integration with agricultural platforms
- Enterprise Solutions: Large-scale farm management
- Freemium Model: Basic detection free, advanced features premium
- Enterprise Licensing: B2B solutions for agribusiness
- Data Services: Anonymized crop health analytics
- Training & Support: Educational services for organizations
- Agricultural Extension Services: Government partnerships
- NGOs: Development organization collaborations
- Agribusiness: Supply chain integration
- Educational Institutions: Research and training partnerships
- 3-Class Architecture: Unique non-crop rejection capability
- Mobile Optimization: Smallest model size in category
- Offline Operation: No connectivity requirements
- High Accuracy: Superior performance on agricultural crops
- Simplicity: One-tap operation
- Speed: Sub-second results
- Transparency: Detailed confidence metrics
- Accessibility: Works on budget devices
- Open Source Foundation: Community-driven development
- Extensible Architecture: Easy to add new crops/diseases
- Cost Effective: Minimal infrastructure requirements
- Global Applicability: Works in any agricultural context
- Android Application: Complete source code
- AI Training Pipeline: Jupyter notebooks and scripts
- Documentation: Comprehensive setup and usage guides
- Testing Suite: Validation and performance tests
- Video Demo: 3-minute application walkthrough
- Live Presentation: Interactive demonstration
- Performance Metrics: Detailed accuracy and speed benchmarks
- User Testimonials: Feedback from testing with farmers
- Architecture Overview: System design and components
- API Documentation: Integration guidelines
- Deployment Guide: Installation and setup instructions
- Research Paper: Technical methodology and results
ADTC Smart Crop Disease Detection represents the future of agricultural technology - putting the power of AI directly into farmers' hands. Our solution addresses critical global challenges while demonstrating technical excellence and real-world impact.