Certified-Uncertainty Evaluation for Computer Agriculture Disease-Triage Advisories Under Advisor Liability Regimes: An Artificial Intelligence Certified-Risk Guarantee

Computer agriculture disease-triage advisories are increasingly deployed as decision aids for crop protection, yet their outputs are probabilistic and their advisors operate under liability regimes that attach legal consequences to negligent recommendations. The analysis is theoretical and methodological: it formalises the advisory as a selective predictor over a disease-label space, defines a certified-risk guarantee as a distribution-free upper bound on the conditional error rate of non-deferred recommendations, and derives the bound from a split-conformal calibration of the triage score. The design is characterised through a replication-based evaluation of the before-after-control-impact estimator under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The certified-risk bound is shown to be finite-sample valid under exchangeability, to degrade gracefully under prior shift, and to translate into an explicit advisor-liability threshold that separates defensible from indefensible deferral policies. The contribution is a certified-risk guarantee that makes the uncertainty reported by a disease-triage advisory auditable against the standard of care a liability regime imposes.