Agriculture · Irrigation optimization
Over-irrigating drowns roots and leaches nutrients. Under-irrigating stresses the canopy before anyone notices. Either way, the yield number at harvest is lower than it should be.
Banao builds soil-moisture and evapotranspiration models that output a per-zone watering schedule each day, wired to your pump controllers and valves where the hardware allows. The farm manager keeps a manual override and sees the reasoning behind every schedule change.
Irrigation AI is not a single model. It is a sensor layer, a scheduling model, a controller integration, and a dashboard — we scope and deliver all four.
We instrument each zone with capacitance probes at root depth and pair sensor readings with local weather data and evapotranspiration estimates — so the schedule responds to what the soil actually holds, not the calendar.
A daily model per field zone outputs recommended water volume and timing based on crop stage, soil type, current moisture, and the next 48 hours of forecast. Different zones on the same farm run different schedules.
Where drip or sprinkler controllers allow it, the schedule pushes to them directly. Where hardware is older or manual, the model outputs a farm-manager checklist with the same irrigation plan.
A daily view per zone: current soil moisture, scheduled water volume, next trigger time, and a map of any zones that have fallen behind or been overridden. Designed to be opened in three minutes each morning.
Total water applied per crop, per plot, per season — so you can see at audit time how actual application compared to plan, and which zones were consistently over or under.
An agronomist or farm manager can override any schedule and flag the reason. Those corrections feed back into the model — so it improves with each season's decisions rather than repeating the same errors.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified in the field.
A mid-size contract-farming aggregator
The aggregator ran all farms on a fixed weekly schedule set by the previous season's averages. Banao instrumented a pilot of twelve zones with soil probes, built a per-zone model over local weather and crop-stage data, and integrated the schedule output with their existing drip controllers.
Banao is a ~300-person engineering company and it runs on the same AI it builds for clients. InterviewGod runs the first-round screen on every engineering hire. Vikaas drives the demand-generation pipeline that fills our project capacity.
A model we cannot keep running inside our own operation does not ship to yours. That is the standard we hold an irrigation scheduling model to — it has to stay useful through every season, not just the first.
Precision irrigation needs reliable sensing and workable infrastructure. We will tell you when those conditions are not yet in place:
We do not quote an irrigation AI project off a description of the farm. We audit your current scheduling, assess the sensing baseline, and price the opportunity first.
We review your current irrigation schedules, assess sensor availability, and run a feasibility pass on your highest-cost crops. You leave with a per-crop ROI estimate and an honest assessment of what instrumentation and build would take — yours to keep. Proceed and the Sprint fee is credited to the build.
Sensor installation, data pipeline, per-zone scheduling model, and controller integration. The scope is fixed at the end of the Sprint — no changes once the build starts.
Live scheduling with a farm-manager dashboard, manual override, and an end-of-season retraining pass that incorporates the season's actual yield and soil readings into next season's model.
What sensors do we need before you can start?
We assess your existing sensing in the Discovery Sprint. Many farms start with no soil probes at all — we identify which zones to instrument first based on crop value and current waste, and include sensor installation in the build scope. The model can also start on weather-only data and be upgraded as probes are added.
Does the model work with drip, sprinkler, and flood irrigation?
Yes, though the integration differs. Drip and sprinkler systems usually allow controller commands and get the most from automation. Flood irrigation on an automated system gets scheduled volume calls. Fully manual flood systems get a daily checklist — still useful for planning, but without the automation saving.
How does it handle crops with different water needs on the same farm?
Each zone is modelled separately. If you have three crops with different canopy stages and soil types, you get three separate scheduling models aggregated into a single farm view. One crop being in a drought-stress window does not affect another crop's schedule.
What happens when the model is wrong?
The farm manager or agronomist overrides the call and flags the reason. That correction feeds back into the model before the next season. Over time the model learns the farm's specific conditions rather than relying on generic evapotranspiration tables.
How long before we see a measurable change in water usage?
Typically within a few weeks of go-live on instrumented zones. The scheduling model can start making different calls from day one; the measurement takes a few weeks of comparison against your prior usage baseline. The Discovery Sprint will give you a forecast of the likely range before you commit.
Bring your current irrigation schedule and last season's water bills. In 45 minutes we'll show you where the model would water differently and what that would be worth.
Book a 45-min scoping call