Few-Shot Transfer of Artificial Intelligence Agriculture Disease Classifiers Across Agro-Ecological Zones Under Label Scarcity: A Computer Meta-Learning Generalization Bound

Deploying crop-disease classifiers across agro-ecological zones is constrained less by model capacity than by the joint scarcity of labels in the target zone and the distributional shift between zones. The bound decomposes target-zone risk into three terms: the empirical meta-training risk, a Rademacher complexity term that scales with the number of few-shot tasks and the hypothesis-class capacity, and a task-divergence term that measures the discrepancy between source and target zone distributions. A before-after-control-impact design is used to evaluate the operating characteristics of the bound under a declared variance structure with intraclass correlation 0.64, between-cluster standard deviation 6.0 and residual standard deviation 4.5; 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. Fifty-two clusters were required to detect a 5-percentage-point effect at 80% power. The analysis is theoretical and design-based: no observational field data are used or claimed. The contribution is a bound that makes the label cost of cross-zone transfer explicit and a design analysis that tells practitioners how many zones, tasks and labels a credible transfer claim requires.