A commercial farm wanted yield prediction. What it needed first was to know what was actually happening in its own fields.
Measurement before modelling
Yield prediction from satellite indices is a well-studied problem and the methods transfer reasonably. What does not transfer is the calibration: the relationship between an index value and a yield in tonnes depends on variety, soil, planting date and management, all of which are local.
So the first season produces a calibration, not a forecast. Saying so at the start is the difference between a client who understands what they bought and one who concludes the technology does not work.
Where the sensors go
Soil moisture probes placed for convenience tell you about the places that were convenient. Placed against a sampling design that accounts for the soil variation across the block, the same number of probes supports a conclusion about the block.
What actually paid
The yield model was interesting. The irrigation scheduling was what paid. Knowing soil moisture at two depths, in near real time, changed the timing of irrigation events and reduced water use measurably within one season, with no model required beyond a threshold and an alert.
That is a common shape. The ambitious piece justifies the project and the simple piece delivers most of the first-year value. Both are worth building; it helps to be honest about which is which.