Conformal-Risk Evaluation for Computer Agriculture Irrigation Advisories Under Unmodeled Actuator Latency: An Artificial Intelligence Coverage-Guarantee Audit

Conformal prediction offers distribution-free coverage guarantees for irrigation advisories, but those guarantees assume that the actuation channel is instantaneous and that the calibration and deployment distributions coincide. In computer agriculture, valve and pump commands are subject to unmodeled actuator latency, so the advisory issued at time t is executed at t + τ with τ unknown and covariate-dependent. the present analysis formalises the resulting coverage gap as a functional of the latency distribution and derives a bound on the realised miscoverage of split-conformal irrigation advisories under bounded latency. The bound shows that the nominal 1 − α guarantee degrades by an amount controlled by the total variation distance between the calibration and latency-shifted score distributions, and it yields a latency-aware calibration rule that restores the guarantee when τ is observable. The theoretical results are illustrated with a replication-based evaluation of an ordinary least squares estimator on a declared data-generating process (intercept 12.0, exposure 0.42, group −3.1, severity 1.35, error SD 4.2) across 600 replications of N = 500 observations with seed 740192663. Estimated bias was −0.03967 for the intercept and 0.0007 for the exposure coefficient, with RMSE 0.87118 and 0.01527 respectively, and empirical coverage of the nominal 95% intervals was 1.0 for both parameters. The audit framework gives irrigation-system designers a computable criterion for deciding when latency must be modelled explicitly rather than absorbed into a wider conformal quantile.