Learning Under Label Shift for Artificial Intelligence Agriculture Crop Disease Diagnosis With Delayed Pathologist Confirmation: A Computer Risk-Consistency Evaluation

Crop disease classifiers deployed in smallholder agriculture face a label-shift problem that is rarely formalised: the field prevalence of a disease differs from the prevalence in the curated training corpus, and the reference label itself arrives only after a pathologist's delayed confirmation. The framework defines a shift operator on the joint distribution of image features and confirmed labels, derives a consistency functional that bounds the gap between source-risk and target-risk estimates, and shows that the bound is governed by the total variation distance between source and target label marginals and by the confirmation delay. A replication-based evaluation of the ['Intercept', 'exposure', 'group', 'severity'] estimator 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 study characterises the behaviour of the ordinary least squares estimator under the stated process. Across the 600 replications the estimated bias for the intercept was 0.01569 and for the exposure coefficient -0.00033; the root mean squared error was 0.8802 for the intercept and 0.01628 for exposure; empirical coverage of the nominal 95% confidence intervals was 1.0 for both parameters. The framework proposes that risk-consistency auditing, rather than point accuracy, should govern deployment decisions when confirmation is delayed, and it identifies the conditions under which a classifier's reported risk can be trusted.