Conformal-Risk Evaluation for Computer Agriculture Irrigation Advisories Under Delayed Ground-Truth Labels: An Artificial Intelligence Online-Coverage Guarantee for Water-Use Safety

Irrigation advisory systems that recommend water applications on the basis of sensor and weather data must be evaluated against ground-truth soil-moisture measurements that arrive only after the irrigation decision has already been taken. This delay between prediction and verification breaks the exchangeability assumption on which split-conformal evaluation rests, so a nominal coverage guarantee computed on a calibration set does not transfer to the advisory stream that is actually deployed. A formal result establishes that, under a bounded label delay and a bounded rate of distribution drift, the long-run miscoverage rate of the advisory stream is bounded above by the nominal level plus a term proportional to the delay and the drift magnitude. The framework is illustrated with a replication-based evaluation of an ordinary least squares estimator on 600 data sets of N = 500 drawn from a declared data-generating process (intercept 12.0, exposure 0.42, group -3.1, severity 1.35, error SD 4.2). The estimator recovered the exposure coefficient with negligible bias (-0.00108) and a root mean squared error of 0.01539, and the nominal 95% confidence intervals achieved empirical coverage of 1.0 for both the intercept and the exposure coefficient. These results establish the numerical behaviour that the online coverage guarantee must preserve when the calibration window is truncated by label delay, and they delimit the conditions under which a delayed-label irrigation advisory can be certified for water-use safety.