Semantic Segmentation of Artificial Intelligence Agriculture Rangeland Degradation Under Class-Imbalance Shift: A Computer Dice-Reliability Evaluation

Semantic segmentation models for rangeland degradation are routinely ranked by Dice similarity, yet the Dice coefficient is a prevalence-dependent statistic whose behaviour under class-imbalance shift is rarely characterised before deployment. the present analysis treats Dice reliability as a design property rather than a post hoc observation. We formalise degradation segmentation as a pixel-level decision problem over a four-class label space (non-degraded, moderately degraded, severely degraded, bare ground) and derive the sampling distribution of the per-class Dice coefficient under a declared variance structure. A replication-based evaluation of the before-after-control-impact estimator was conducted with between-cluster SD = 6.0, residual SD = 4.5 and intraclass correlation = 0.640, across 2000 design cells spanning cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points.52 clusters are required to detect a 5 percentage point shift at 80% power, and that the minimum detectable effect falls steeply between 8 and 24 clusters before flattening. Dice reliability is therefore not a fixed attribute of a segmentation architecture but a function of the cluster budget allocated to its validation. We propose a reliability contract that binds reported Dice to the imbalance regime and cluster count under which it was estimated, and we specify the boundary conditions under which the contract is informative.