Evaluation of Concept-Probe Calibration Audits for Artificial Intelligence Agriculture Disease Classifiers Under Coarse Sensor Quantization: An Interpretability Confidence Bound for Smallholder Fungicide Dosage

Smallholder fungicide dosing decisions increasingly rely on artificial intelligence (AI) disease classifiers whose inputs arrive through coarsely quantized leaf-wetness and humidity sensors, yet the interpretability signals used to audit those classifiers are themselves calibrated on unquantized data. The analysis treats quantization as a bounded perturbation of the probe's input distribution and characterises the resulting bias in concept-activation estimates through a Lipschitz continuity argument. A before-after-control-impact design is specified to evaluate the audit procedure, with a variance structure of between-cluster SD = 6.0, residual SD = 4.5 and intraclass correlation = 0.64; power was estimated across 2000 replications per design cell for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The design requires 54 clusters to detect a 5-percentage-point effect at 80% power. The derived bound shows that probe calibration error propagates superlinearly into dosage error once quantization exceeds a threshold determined by the probe's Lipschitz constant, and that audit designs with fewer than 24 clusters cannot distinguish a safe from an unsafe classifier at the 5-percentage-point margin. The contribution is a decision-theoretic criterion for when a concept-probe audit is informative enough to license smallholder dosage recommendations.