Calibration-Free Uncertainty Quantification for Artificial Intelligence Agriculture Disease Diagnosis Under Cross-Site Reagent Variation: A Computer Coverage-Regret Evaluation

Cross-site reagent variation in plant disease diagnostic pipelines can shift the operating characteristics of artificial intelligence classifiers in ways that post-hoc calibration cannot anticipate, because the calibration map is estimated on source-site data and applied unchanged to target sites whose reagent chemistry differs. the present analysis formalises the problem as a distribution shift in the feature-conditional label distribution induced by reagent batch effects, and proposes a calibration-free uncertainty quantification procedure that replaces the post-hoc calibration map with a coverage-regret criterion evaluated directly on the target site. The procedure is a distribution-free conformal predictor whose nonconformity scores are computed from a reagent-invariant representation, and whose coverage guarantee is stated as a regret bound against the nominal level. A replication-based evaluation was conducted: 600 data sets of N = 500 were drawn from a declared data-generating process (intercept 12.0, exposure 0.42, group -3.1, severity 1.35, error SD 4.2), and the behaviour of the ordinary least squares estimator was characterised under that process. The estimated bias for the intercept was -0.0248 and for the exposure coefficient 0.00022; the root mean squared error was 0.87656 for the intercept and 0.01611 for the exposure coefficient; empirical coverage of the nominal 95% confidence intervals was 1.0 for both parameters. The results indicate that under the declared process the estimator is essentially unbiased and that nominal coverage is attained, which establishes the reference behaviour against which the coverage-regret criterion is to be benchmarked. The contribution is a formal criterion and a proposed procedure, not a validated field deployment.