Evaluation of Concept-Embedding Drift Audits for Artificial Intelligence Agriculture Disease Classifiers Under Seasonal Pathogen Evolution: An Interpretability Stability Bound for Smallholder Fungicide Timing

Concept-embedding drift audits are increasingly proposed as a safeguard for deployed crop-disease classifiers, yet the statistical conditions under which such audits can distinguish genuine pathogen evolution from sampling noise remain unformalised. The audit is modelled as a sequential test on the drift of class-conditional concept centroids, and a bound is derived that relates the audit's false-alarm rate to the intraclass correlation of embedding coordinates, the between-cluster standard deviation of drift, and the number of monitoring clusters. A before-after-control-impact design is used to characterise the estimator's power across a declared variance structure with between-cluster standard deviation of 6.0, residual standard deviation of 4.5, and intraclass correlation of 0.64. Power was estimated across 2000 design cells spanning 8, 16, 24 and 32 clusters and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 52 clusters are required to detect a 5 percentage point drift at 80% power, a requirement that exceeds the monitoring capacity of most smallholder advisory systems. The bound implies that concept-embedding drift audits, as currently specified, cannot reliably certify classifier stability for fungicide timing decisions at the cluster counts typically available, and that audit design must be tied explicitly to the decision cost of a missed drift rather than to conventional significance thresholds.