Yield, risk, and insurance signals you can actually explain.
KurimaSense turns satellite and ground-truth data into yield forecasts, field-level risk scores, and parametric-insurance triggers — calibrated for Zimbabwe's Natural Regions and delivered with an explicit confidence score on every number. Built for buyers, lenders, and insurers, validated by the farmers in the field.
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Field 3 · Mazowe
Maize · Natural Region II
range 4.4–7.0 t/ha · 80% band
- NDVI trendHigh
+12% over 14 days — canopy closing on schedule
- Rainfall vs 10-yr normalMedium
−18% this month — mild moisture deficit
- Growth stageHigh
V8–V10, calibrated to Natural Region II maize
Confidence-scored
Predictions with explicit uncertainty
Zimbabwe-trained
Calibrated for local crops & Natural Regions
<5 min
Farmer setup time
Local crops
Tobacco, maize, cotton, soya & more
Local calibration and explainable confidence — the part competitors can't copy.
Calibrated for Zimbabwe
Models tuned to the Natural Regions and to local crops — tobacco, maize, cotton, soya — not retrofitted from American maize or Brazilian soya datasets that have never seen Compound D or Region II.
A confidence score on every number
Each forecast ships with an explicit uncertainty band. You see how sure the model is and why — so you can price, lend, and underwrite against a number you can defend.
Explainable, not a black box
Every signal traces back to its inputs: NDVI trend, rainfall against the 10-year normal, growth stage. Determinism over probabilistic hand-waving.
Portfolio-grade agricultural intelligence
Three deliverables, one calibrated engine and the same explainable confidence scoring underneath each — across Southern Africa.
Explainability, made visible
We'd rather show our work than ask for your trust. Here is how a number becomes a forecast you can underwrite — and how its confidence score is earned.
Ingest
Multi-source satellite imagery (optical + radar), weather reanalysis, and historical yield records are pulled per field and per growing zone.
Calibrate
Signals are tuned against Zimbabwe-specific ground truth — Natural Region, crop variety, and management practice — not a generic global baseline.
Score
Each output carries an explicit confidence band derived from input agreement and data coverage. Thin or conflicting data widens the band, visibly.
Data provenance
Satellite sources
Open Sentinel-2 / Landsat optical and Sentinel-1 radar for cloud-resilient NDVI and moisture proxies.
Ground truth
Field boundaries, planting dates, crop and variety logged by farmers and agronomists on the platform.
Calibration approach
Region- and crop-specific adjustment against local agronomic constants and prior-season outcomes.
Reliability & data handling
· Field and account data is access-controlled and never sold to third parties.
· Forecasts are versioned, so a number can always be traced to the inputs and model behind it.
· Outputs are labelled estimates with confidence bands — not guarantees.
· We're early-stage and say so: where ground-truth coverage is thin, the confidence score reflects it.
For farmers & agronomists
The same intelligence institutions rely on, in the hands of the people closest to the crop — free to start, and the foundation our calibration is built on.
Monitor every hectare from your pocket
Satellite crop health, weather and spray windows, pest alerts, and an AI agronomist — free to start. The fields you manage become the validated ground truth the whole platform is built on.
- Satellite NDVI monitoring
- AI agronomist & alerts
- Field & input tracking
Advise with data, not guesswork
Manage multiple growers, validate advice against real-time satellite and weather data, and turn field visits into evidence-backed recommendations.
- Multi-client dashboard
- Data-backed advisory
- Shareable field reports
Now onboarding pilot farms and partners in Zimbabwe
We're building KurimaSense in the open with a small group of farmers, agronomists, and institutional partners. We don't publish reviews we can't stand behind — what we can show you is the methodology, the confidence scores, and exactly how the intelligence is calibrated for Zimbabwe's Natural Regions.
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Practical agronomy notes, field observations, and how our intelligence works — written by our team to help you grow better.