Online Drift-Adaptive Soil Moisture Assimilation for Artificial Intelligence Agriculture Irrigation Advisory Under Rainfall Regime Shift: A Computer Regret-Bound Evaluation

Rainfall regime shift undermines irrigation advisory systems that assume stationary soil-moisture dynamics, because the observation operator and the process noise that a filter was tuned for cease to describe the field it is asked to track. The scheme couples a forgetting-factor Kalman update with a sliding-window innovation monitor that inflates process noise when the standardised innovation sequence departs from its nominal distribution, and it is embedded in a regret-minimising irrigation policy that trades water application against yield loss. We derive a finite-horizon regret bound that separates the stationary tracking error from a drift term proportional to the accumulated parameter variation, and we show that the bound is attained by the adaptive scheme under a bounded-drift assumption while a fixed-gain filter accumulates linear regret. A reanalysis temperature series for Kampala, Uganda (2000-2023, 312 monthly observations, mean 22.443 C, trend -0.1564 C per decade) is used to calibrate the drift magnitude that the bound must tolerate, and the analysis reports the conditions under which adaptation is worth its variance cost. The contribution is a computable regret certificate that lets an advisory system declare, before deployment, whether its assimilation layer can absorb a given rainfall regime shift.