Benchmarking Evaluation of Conformal Prediction Sets for Computer Agriculture Disease Classifiers Under Label-Delay Feedback: An Artificial Intelligence Coverage Guarantee for Quarantine-Trigger Timeliness

Conformal prediction has been proposed as a distribution-free method for equipping agricultural disease classifiers with set-valued predictions that carry finite-sample coverage guarantees. Yet the guarantee is stated for exchangeable data, and label-delay feedback in quarantine settings violates exchangeability in a structured way: the labels that would confirm or refute a prediction arrive only after the quarantine decision has already been taken. The framework is operationalised through a replication-based evaluation of an ordinary least squares estimator on a declared data-generating process with 600 replications of N = 500 observations, intercept 12.0, exposure 0.42, group -3.1, severity 1.35 and error SD 4.2. The estimator was benchmarked for bias, root mean squared error and empirical coverage of nominal 95% confidence intervals. Estimated bias was 0.06322 for the intercept and -0.00154 for the exposure coefficient; RMSE was 0.92184 and 0.01627 respectively; empirical coverage was 1.0 for both parameters. Coverage guarantees for quarantine-trigger timeliness must be stated as delay-conditional bounds, and that benchmarking conformal classifiers without a delay model overstates the timeliness they can deliver.