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Agriculture · Yield prediction

Your yield number is last year's harvest — and your contracts are priced on it

Banao builds yield prediction models that produce a per-plot estimate before harvest is cut — drawing from satellite vegetation indices, soil moisture, cumulative weather, and your own historical records.

Procurement teams get a number they can quote to buyers. Storage managers know how much capacity to book. The estimate is a model output, not a memory, and it updates as the season moves.

What a Banao yield prediction deployment includes

A forecast that procurement teams will act on needs more than a model — it needs clean input data, a per-plot view, and a number that is honest about its own confidence.

Satellite and weather data pipeline

We ingest NDVI satellite passes, cumulative rainfall and temperature, evapotranspiration signals, and soil-moisture readings into a clean pipeline you own — so the model always runs on current-season data, not a static snapshot.

Per-plot models trained on your records

Models are calibrated against your historical harvest weights by plot, variety, and input treatment — not against a generic agricultural dataset. More seasons logged means a tighter per-block estimate.

Harvest window forecasting

Beyond total volume, the model estimates the harvest date range per block so labour rosters, transport, and cold-chain bookings can be confirmed weeks ahead rather than on the week.

Scenario outputs for planning

Procurement heads see a base estimate alongside a stress case — what the number looks like if rainfall drops ten percent or a heat event hits during grain fill. Decisions rest on a range, not a single figure.

Dashboard updated on each satellite pass

A per-block view that refreshes each time a satellite pass clears and new weather data arrives, with alert flags when an estimate moves more than a set threshold from the prior week.

ERP and procurement system integration

Forecast outputs push into your procurement system, farm-management software, or buying-desk tool — so the number lives in the system the contract team already works in, not a separate app they have to remember to open.

Deployed, with names attached where we can

Metrics shown dotted (··) are being finalised in our case-study metrics pack — figures published only once verified in the field.

A contract-farming aggregator

Per-plot yield model replacing procurement guesswork across hundreds of grower sites

  • ··%yield-forecast error vs actuals
  • ··weeksearlier procurement visibility
  • ··%manual agronomist call-outs for estimates

A buying organisation aggregating produce from hundreds of small plots set procurement terms three months ahead using last season's volumes and phone calls to local agronomists. Banao built a per-plot model over satellite passes, rainfall accumulation, and three seasons of buyer-recorded harvest weights — giving the procurement desk a number that updates each week through the growing season.

We run AI on our own operation before we put it on yours

Banao is a ~300-person engineering company that runs on its own AI products before any of them ship to a client. InterviewGod screens every engineering candidate we hire. Vikaas drives the demand-generation pipeline that fills our sales funnel.

When we build a yield model, we are not describing production AI from the outside. We live the same operational discipline every working day — which is the standard we bring to a forecast a procurement head is going to sign contracts against.

  • InterviewGodScreens Banao's own engineering hires before any client sees it.
  • VikaasRuns Banao's own demand-gen pipeline end to end, every week.

When yield prediction won't earn its cost

Yield prediction works where there is data to train on and a decision that depends on the number. Where those conditions are absent, we say so before you spend:

  • Fewer than two or three seasons of plot-level harvest records: the model can be built, but calibration is weak and the estimate carries wide error bars — we give you the honest confidence bounds, not a polished number.
  • Missing satellite coverage: if plots are too small, too fragmented, or cloud-obscured through the critical growth window, the input signal is degraded. We start with a monitoring phase to establish what data is actually available.
  • Procurement that won't bind to the model output: if the buying desk contracts on gut read regardless, the build cost does not pay back. We include change management as a deliverable, but internal buy-in must exist before the Sprint.

How we start — fixed price, no guesswork

A yield model is only as good as the data going in. We look at your actual records and satellite coverage before quoting a build.

  1. AI Discovery Sprint2 weeks · fixed price

    We audit your available season records, test satellite coverage across your plots, and hand back a baseline accuracy estimate, a data-gap plan, and ROI maths for your procurement team — yours to keep. If you proceed, the Sprint fee is credited against the build.

  2. Build

    We assemble the satellite, weather, soil, and harvest-record pipeline, train the per-plot model, and integrate forecast outputs with your procurement system or farm-management software.

  3. Production through the season

    The model runs through your growing season with a dashboard your buyers and estate managers open daily. Each harvest feeds the next season's calibration — accuracy improves with every year of production data.

Frequently asked questions

Two or three seasons of plot-level yield weights gives the model enough calibration to be trusted. With one season, we start with a monitoring and data-collection phase and flag the confidence bounds honestly. More history almost always means a tighter forecast.

A useful estimate is typically available six to eight weeks before harvest in most annual crops — earlier if satellite coverage is clean and the season is tracking normally. The exact window is established in the Discovery Sprint against your specific crop and geography.

Accuracy depends on data quality, crop type, and geography — numbers we establish against your actual plots in the Sprint, not quote from industry averages. We show you the error bands on your own history before you commit to a build.

Yes. Models are trained per crop and per geography and run in parallel. If you grow several varieties or operate across different regions, each gets its own calibration — aggregated into a single dashboard view for your buyers.

Forecast outputs push into whatever system your buying desk uses — farm-management software, ERP procurement modules, or a purpose-built dashboard. Integration is scoped in the Discovery Sprint and delivered as part of the build, not a separate project.

Bring your harvest records — we'll show you what the model can see

Share a few seasons of plot-level yield data and we'll estimate what a satellite-and-soil model would have predicted, and how far that sits from your last manual estimate.

Book a 45-min scoping call