Leakage-Aware Cross-Dataset Multi-Task Learning for Shrimp Disease Recognition and Biomass Estimation

Public shrimp image collections are reused to train disease classifiers and non-invasive biomass estimators, yet they mix originals with published augmentations, byte-identical copies, and repeated views of the same animal, potentially inflating performance through data leakage. This study examines what these collections support once leakage is controlled: how protocol and acquisition conditions, rather than architecture alone, influence performance; whether disease recognition transfers to an independently collected dataset; and whether monocular imagery supports individual-weight estimation. One encoder is trained across three public datasets under masked supervision, using a grouping strategy that accounts for duplicates, augmentations, near-duplicates, and repeated views; a 22-check audit guides training and evaluation, with 240 runs reported. Regrouping by animal rather than file eliminates the 84.8–92.6% animal overlap in ShrimpDiseaseImageBD test folds, reducing it to 0.0%. A random image-level split yields 0.976 balanced accuracy, but 63% of its test records are published augmented copies; using originals alone yields 0.947, compared with 0.934 in the augmentation-enabled group-aware arm, which tests different original records. Transfer is asymmetric: the best-performing encoder reaches 0.615 balanced accuracy and 0.751 area under the curve on TigerShrimpBD; on ShrimpDiseaseImageBD, every encoder ranks below chance, with values of 0.271–0.390. TigerShrimpBD's 0.998 within-dataset score is affected by acquisition confounding, as every recovered capture session contains only one health class. Holding out a physical site increases biomass error from 3.18 g to 9.20 g, indicating limited cross-site generalization. The contribution is a reusable audit and evaluation protocol for interpreting performance on such collections.