Industry Focus
From satellite to smallholder — precision at every scale.
Agriculture operates at scales that defy manual monitoring — millions of hectares, millions of farmers, seasons that wait for no one. Kaistrum brings GIS and remote sensing to bear on food security.
2.1M+
hectares monitored
5-day
Sentinel-2 analysis cadence
350k+
farmers reached
The challenge
What makes agriculture hard.
Crop monitoring at scale
Field-level crop health across millions of hectares is impossible to survey manually with any frequency — problems are found too late to intervene.
Irrigation inefficiency
Uniform water application across variable soils and crop conditions wastes 20–40% of irrigation water while under-serving stressed zones.
Yield prediction uncertainty
Planning without reliable spatial yield forecasts creates market uncertainty, logistics failures, and input waste.
Rural connectivity barriers
Farmers and extension workers in remote areas can't access complex GIS tools — the last-mile data problem undermines every other investment.
Crop health monitoring — NDVI satellite imagery
Our approach
What Kaistrum delivers.
Satellite crop health monitoring
Multi-temporal NDVI and EVI analysis from Sentinel-2 and Landsat — delivered as field-level alerts every five days.
Variable rate irrigation prescription
GIS-generated irrigation prescription maps combining soil type, crop health index, and weather data to drive variable-rate application.
Spatial yield prediction models
Statistical models integrating historical yield, soil, weather, and remote sensing data to produce field-level yield forecasts.
Low-bandwidth farmer interfaces
SMS-based early warning systems and simplified mobile apps that work on feature phones and 2G connections.
Field-level irrigation prescription map
Applications
In practice.
Crop health monitoring
NDVI and EVI analysis from Sentinel-2 imagery — identifying stress zones, water deficits, and pest pressure at field level every five days.
Irrigation planning
Variable rate irrigation prescription maps generated from soil type, topography, crop health, and weather forecast integration.
Yield prediction
Spatial regression models combining historical yield data, soil profiles, crop health indices, and seasonal weather to forecast yield at field level.
Land use change analysis
Multi-temporal mapping to detect cropping pattern shifts, deforestation for agriculture, and informal settlement encroachment on arable land.
Smallholder early warning
SMS-based drought and flood risk alerts derived from GIS analysis — delivered to smallholder farmers with no smartphone or internet access.
Get started
Ready to deploy GIS for agriculture?
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