Large yellow croaker (Larimichthys crocea) is an economically important marine fish, yet rapid and non-destructive discrimination between sea-caught and farmed collection-source groups remains challenging. This study developed a fish-level analytical workflow integrating YOLO11n-seg instance segmentation with visible–near-infrared hyperspectral imaging to extract relative reflectance ratios from six anatomical regions. The analytical cohort comprised 258 unique fish, including 171 sea-caught and 87 farmed individuals. Source categories were assigned according to the original capture or cage-culture collection channels. Fish identity was used as the grouping unit in five-fold internal cross-validation, and classification performance was evaluated based on out-of-fold fish-level predictions. Logistic-regression models achieved AUC values ranging from 0.985 to 1.000 across the six individual anatomical regions. The combined six-region model achieved an accuracy of 0.996 (95% CI, 0.988–1.000) and an AUC of 1.000 (95% CI, 1.000–1.000) within the present cohort. For anatomical-region segmentation, the 20-image validation set contained 140 annotated instances. Bounding-box precision, recall, mAP@0.5, and mAP@0.5–0.95 were 0.976, 0.983, 0.981, and 0.700, respectively, while the corresponding mask metrics were 0.969, 0.976, 0.972, and 0.669. These results indicate that region-specific hyperspectral information can support highly accurate internal discrimination between the two collection-source groups and provide an interpretable basis for characterizing source-associated spectral differences. However, the source labels were not independently verified, potentially influential covariates were incompletely recorded, and no independent external cohort was available. Therefore, the present findings should be interpreted as internally validated collection-source discrimination rather than verified provenance authentication or evidence of external generalizability.
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