Technical Documentation
Everything you need to know about using and integrating with GrainHero
User Guide
Complete guide to using GrainHero platform features and real-time monitoring.
- Dashboard & Analytics
- Silo & Warehouse Management
- Alert Configuration
- Batch Tracking & Reports
API Documentation
Server functions and API endpoints for grain storage operations.
- Supabase Authentication
- Sensor Data Endpoints
- AI Prediction APIs
- Analytics Functions
ML & AI Integration
Machine learning models for spoilage prediction and anomaly detection.
- Python ML Inference
- Gemini AI Insights
- Risk Classification Models
- Real-time Predictions
Platform Features
Core features available in the GrainHero web platform.
- Real-time Monitoring
- Predictive Analytics
- Insurance & Claims
- Team Management
IoT & Sensor Integration
IoT sensor specifications and data collection protocols.
- Temperature & Humidity Sensors
- Moisture & CO2 Monitoring
- VOC Detection
- Firebase Real-time Sync
Data & Reports
Export capabilities and reporting tools for grain storage data.
- Batch Analytics
- Risk Reports
- Traceability Logs
- Activity History
ML Model: Spoilage Risk Classifier
GrainHero uses advanced machine learning models including Gradient Boosted Trees for spoilage classification, Isolation Forest for anomaly detection, and LSTM networks for yield forecasting. Our AI leverages temperature, humidity, moisture, CO₂, VOC, and storage duration data to predict grain spoilage 24-48 hours in advance with high accuracy.