Distribution-Shift-Robust Inference for Computer Agriculture Weed-Detection Models Under Seasonal Species Turnover: An Artificial Intelligence Temporal-Calibration Generalization Bound

Weed-detection models deployed in computer agriculture face a distinctive inferential problem: the species composition of a field changes across the growing season, so a detector calibrated on one phenological window is evaluated, in practice, on a population it was not trained to represent. the present analysis treats that seasonal species turnover as a temporal distribution shift and asks what can be guaranteed about a detector's error rate on a future window when only source-window data and an unlabelled target sample are available. The contribution is a temporal-calibration generalization bound that decomposes target risk into three estimable terms: source risk, a label-shift correction computed from predicted class proportions, and a covariate-shift term controlled by a discrepancy between source and target feature distributions. The bound is derived under an assumption of label-shift dominance, which is argued to hold approximately when turnover changes which species are present rather than how they appear. A calibration procedure is proposed that estimates the bound's terms from unlabelled target data and yields a conservative risk certificate. The analysis is theoretical: no field data are used or claimed, and the bound's behaviour is characterised analytically and illustrated with a worked numerical example. The result clarifies when seasonal recalibration is necessary, when it is redundant, and why aggregate accuracy is an unreliable guide to cross-season deployment.