Sample-Efficient Inference for Computer Agriculture Crop-Disease Forecasting Under Scarce Labeled Field Imagery: An Artificial Intelligence Active-Sampling Generalization Bound

Crop-disease forecasting from field imagery is constrained less by model capacity than by the cost of acquiring labelled examples, and active learning is the standard remedy. The bound is derived under a Tsybakov margin condition and a disagreement-coefficient assumption, and it yields an explicit label-complexity rate that is then embedded in a design analysis. The design analysis used a declared variance structure with between-cluster SD = 6.0, residual SD = 4.5 and intraclass correlation = 0.64, and estimated power across 2000 replications per design cell for cluster counts of 8, 16, 24 and 32 and effects of 3, 5 and 8 percentage points. Power rose with cluster count and effect size, and 56 clusters were required for 80% power at a 5-percentage-point effect. The bound and the design analysis together specify the conditions under which an active-sampling claim is identifiable from a field trial, and they identify the cluster count at which the claim becomes testable.