Broccoli head segmentation is the upstream step of a sizing pipeline that feeds harvest decisions, growth models, and yield forecasts, but the broccoli vision literature reports each on a different metric stack without confidence intervals, stratification, or robustness testing. This work identifies which evaluation variables actually differentiate broccoli head segmentation models under two deployment use cases: end-of-life-cycle harvest decisions and whole-cycle growth monitoring.

Five recent broccoli segmentation models are evaluated with cluster-bootstrap confidence intervals on two publicly available broccoli image datasets, under stratification by leaf occlusion, head size, and time of day; under two controlled photometric and motion-blur perturbations calibrated against published in-deployment image-sharpness anchors; and under a noise-injection sweep on a published downstream growth-prediction model.

Stratification by occlusion and by head size each flip the ranking between two of the broccoli segmentation models tested; no single architecture is operationally optimal across the growth curve. The two controlled perturbations agree on the qualitative robustness tiers but not on the strict rank order; a cross-camera evaluation produces no usable ordering at all. Upstream sizing-label noise propagates linearly into downstream growth-prediction MAE with a small coefficient, so cleaning sizing labels beyond the most accurate currently published sizing pipeline buys at most one to four hours of biological head growth, well under one harvest-decision cycle. The implication is that broccoli vision evaluation needs deployment-context-aware reporting rather than a single headline number, and sizing-pipeline accuracy is not the operational lever that moves downstream growth-prediction performance. ... Read more