Calibration-Drift Evaluation for Computer Agriculture Frost-Warning Classifiers Under Shifting Microclimate Sensor Mixes: An Artificial Intelligence Temporal-Recalibration Bound

Frost-warning classifiers in computer agriculture are typically trained on a fixed mix of microclimate sensors and then deployed while that mix changes, so the calibration map that converts scores into frost probabilities drifts even when discrimination is preserved. The classifier's score is treated as a sufficient statistic, the sensor mix is modelled as a latent regime that shifts the score distribution, and calibration drift is defined as the divergence between the source and target conditional probability of frost given the score. Under a bounded rate of regime change and a Lipschitz calibration map, the bound states that the excess expected calibration error accumulated over a deployment window grows at most linearly in the window length and in the drift rate, with a constant that depends on the score's Lipschitz constant and the frost base rate. The bound is derived for a recalibration schedule that re-estimates the calibration map at intervals, and it yields a closed-form optimal interval that trades recalibration cost against accumulated miscalibration. A worked illustration uses NASA POWER monthly reanalysis air temperature at 2 m for Lagos, Nigeria (6.52, 3.38), 2000-2023, 312 monthly observations, with mean temperature 26.414 C and a warming trend of 0.1553 C per decade, to show how a slowly warming base rate compresses the usable calibration window. The contribution is a deployable evaluation criterion: a frost-warning classifier should be judged not only by its discrimination but by whether its recalibration interval is shorter than the bound's recommended interval for the observed sensor-mix drift rate.