Crop classification
Machine-learning classification of crop types from full-season satellite time series. Reliable acreage and crop-mix intelligence for procurement, insurance, and policy.
- Season time-series ML
- Region-scale coverage
- Adaptable to local crops
Benefits
What crop classification unlocks
Short-term optimization
- Quantify planted area without field enumeration
- Catch crop-mix shifts as the season develops
- Sharpen procurement and sourcing plans
Long-term impact
- Multi-year crop-rotation intelligence per region
- Better regional production estimates
- Inputs for food-security and land-use planning
Approach & methodology
How the analytics work
We are transparent about data sources, models, and limits — so you can trust what you act on.
- Data sources
- Multi-date multispectral imagery across the crop season; ground-truth samples where available.
- Approach
- Supervised ML classification on spectral-temporal signatures, validated against reference data.
- Update cadence
- Mid-season preliminary map; end-of-season final classification.
- Limitations
- Accuracy depends on ground-truth quality; spectrally similar crops may need extra reference data.
Deliverables
Expected outputs
- GIS-ready data layers (GeoJSON / KML / SHP / GeoTIFF)
- Decision-ready PDF report with interpretation
- Interactive smart map with time-series view
Process
Project stages
3–6 weeks depending on season stage
Define area of interest and indicators with your team
Acquire and process multi-date satellite imagery
Run analytics models and validate outputs
Deliver layers, maps, and report; set up recurring monitoring