Markets / Yield modeling, soil carbon, crop monitoring
Agriculture & Agribusiness
We built plant-stress detection that reads 92.3% accurate from a handheld spectral sensor, and soil moisture monitoring that works through cloud cover. Our models separate water stress from nutrient deficiency in under 30 seconds, at 85% lower cost than lab analysis.
What we address
The problems on the table
Crop stress detection
Spectral machine learning classifies healthy, water-stressed, and nutrient-deficient plants at 92.3% accuracy, validated in greenhouse and multi-state field trials with Croptix Inc.
Soil moisture and irrigation
Sentinel-1 SAR fused with optical data resolves volumetric soil moisture from 0.05 to 0.45 m³/m³, cloud or no cloud. Irrigation events show up in the time series.
Yield and carbon
Crop yield prediction, soil carbon estimation, and canopy growth models built on satellite time series and process models such as DSSAT.
Services that apply
How we work this market
- Satellite Analytics & Change DetectionMultispectral, SAR, time-series monitoring
- Machine Learning & Predictive ModelingYield, risk, and anomaly models on geodata
- Hyperspectral Data AnalysisCrop classification, disease, and nutrient prediction
- Advisory & TrainingMethod design, review, and team enablement
Delivered work
Projects in this market
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