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Why Crop Health Monitoring Matters
40%
of global crops lost to pests & disease annually
$220B
economic impact from crop losses worldwide
72hrs
typical delay in manual stress/disease detection
Smart Detection in 3 Steps
1. Upload
Capture and upload crop images from phone or drone
2. Analyze
AI processes using vegetation indices and CNN model
3. Insights
Get diagnosis, confidence scores, and action recommendations
Our Competitive Edge
Precision
95%+ accuracy using computer vision
Speed
Results in under 15 seconds per image
Accessibility
Works with smartphone cameras
AI500! Hackathon Winner
Our team won the AI500! Hackathon organized by Agrobank, validating our innovative approach to AI-powered crop stress detection.
Recognized Excellence in AgriTech Innovation
Apollo AI was awarded first place at the AI500 Hackathon hosted by Agrobank, competing against top teams in agricultural technology innovation.
Grand Prize Winner
Recognized for outstanding innovation in AI-powered agriculture
Real-World Impact
Solution addresses critical challenges in crop health monitoring
Industry Validation
Endorsed by agricultural and tech sector experts
How It Works Under the Hood
PlantVillage
PlantVillage is a dataset of 38 types of leaves with diseases
CNN Model
Convolutional Neural Network for stress/disease classification
LLM Reports
AI-generated insights and recommendations by our AI-chatbot
Development Timeline
MVP
Current- Image upload
- Basic stress/disease detection
- Diagnostic reports
Enhancement
- Multi-crop support
- Historical analysis
- Mobile app
Scale
- Drone integration
- Real-time monitoring
- API access
See It In Action
Experience the power of our analysis engine with your own data.
2. Analysis Output
Stress/disease detection with severity levels
Disease Detection Output