Closed-Loop Irrigation Control for Artificial Intelligence Agriculture Under Delayed Soil-Moisture Feedback: A Computer Safe-Policy Regret Evaluation

Delayed soil-moisture feedback is a structural feature of closed-loop irrigation control in which the controller must act on a moisture state that has already changed by the time the measurement arrives. the present analysis formalises the problem as a constrained Markov decision process with a fixed measurement delay and develops a safe-policy regret framework that separates the regret attributable to delay from the regret attributable to the safety constraint. The framework defines a delay-augmented belief state, a safe-policy class that enforces a probabilistic moisture-floor constraint, and a regret decomposition into an optimistic-delay term and a constraint-tightening term. A finite-sample bound is derived showing that the delay-induced regret grows linearly in the delay horizon and inversely in the square root of the number of safe interactions, while the constraint-tightening term is controlled by the Kullback-Leibler divergence between the delayed and undelayed transition kernels. The bound is illustrated on a real World Bank panel of 24 African countries observed from 2000 to 2023 (N = 560), where an OLS model of GDP per capita on access to electricity and urban population yields R-squared = 0.591 and an F statistic of 184.764, and the coefficients are used only to calibrate the scale of the regret terms rather than to estimate irrigation outcomes. The contribution is a computer-science formalisation that makes delay and safety separately auditable in irrigation controllers, together with a regret bound that a designer can evaluate before deployment.