Sequential Decision Under Partial Observability for Artificial Intelligence Agriculture Irrigation Scheduling Under Unmodeled Soil Dynamics: A Computer Regret-Bound Comparison

Irrigation scheduling under partial observability is formalised as a finite-horizon partially observable Markov decision process in which the soil-moisture transition kernel is not known to the controller. Four candidate policies are compared: certainty-equivalent model predictive control, a point-based value iteration approximation, a Thompson-sampling bandit, and a sliding-window online learner. The comparison is analytical rather than empirical: for each policy a regret bound is derived as a function of the observation horizon, the size of the unmodeled dynamics class, and the number of irrigation decisions per season. The central result is that the regret of certainty-equivalent control grows linearly in the horizon whenever the unmodeled transition error is nonzero, whereas the online learner attains sublinear regret of order the square root of the horizon under a bounded-elasticity assumption on the soil-moisture response. The bound gap is quantified for a reference parameterisation with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64, and the cluster count required for eighty per cent power at a five percentage-point effect is reported as 54. The contribution is a decision-theoretic account of when partial observability is tolerable in irrigation scheduling and when it forces exploration, together with a reproducible regret-bound template that can be instantiated for any bounded soil-moisture model class.