Features
Global methane plume detection using multi-satellite data
Automated emission quantification (kg/hr)
Source attribution to facilities or regions
Daily satellite imagery processing updates
Interactive map with heatmaps and event markers
Customizable alerts for large releases
Time-series analysis and trend charts
Exportable reports (PDF, CSV) for compliance
API for integrating methane data into own systems
Historical archive for baseline comparisons
Multi-satellite fusion (Sentinel-2, Sentinel-5P, and others)
AI-based plume identification with reduced false positives
Web dashboard accessible without satellite expertise
Solar-powered rover with controlled illumination
Per-plant high-resolution imaging across growing season
AI-powered pest, weed, and disease detection per plant
Real-time plant growth monitoring
Automated yield forecasting from imagery and environmental data
Crop phenotyping via computer vision (leaf size, biomass)
Quality inspection for fruits and vegetables
Food waste reduction analytics
Integration of satellite, weather, and soil data layers
Multi-angle image capture with controlled rolling speeds
Machine learning to identify plant-environment interaction patterns
Data collection in muddy fields and bright sunlight
Terabyte-scale image datasets per field
Controlled rolling speeds and sophisticated sensors
