Turkoglu, MuammerDurrani, OmerhanPolat, OnurBal, HabibAtessahin, TuncayIsguzar, SedaSeyhan, Kadir2026-06-192026-06-1920260165-78361872-6763https://doi.org/10.1016/j.fishres.2026.107658https://hdl.handle.net/20.500.12899/5871Accurate age determination of commercially important fish species is essential for sustainable fisheries management and stock assessment. However, traditional methods relying on the manual counting of otolith annuli are labour-intensive, time-consuming, and subject to significant inter-reader variability. This study introduces SwinGPC-AgeRecognitioNet, a hybrid deep learning framework designed for efficient automated age estimation in the Mediterranean horse mackerel (Carangidae: Trachurus mediterraneus), to address these challenges. The proposed architecture synergises Swin Transformer-based feature extraction with a Gaussian Process Classifier (GPC) to capture global structural patterns while providing robust probabilistic predictions. The methodological workflow integrates three critical stages: (1) high-level feature extraction via Swin Transformer; (2) discriminative feature selection using Recursive Feature Elimination; and (3) hyperparameter-optimised classification via GPC. Experimental evaluations on a dataset of 1231 otolith images reveal that the proposed model consistently outperforms Convolutional Neural Networks architectures (e.g., VGG, ResNet), achieving accuracies of 88.66 % in multi-class classification and up to 94.33 % in binary tasks. These findings highlight the potential of SwinGPC-AgeRecognitioNet as a scalable, high-precision tool for fisheries science, offering a reliable alternative for data-driven resource management.eninfo:eu-repo/semantics/closedAccessBlack Sea, Fish Age DetectionOtolith ImagesTransformerTrachurus MediterraneusOptimal PerformanceArticle10.1016/j.fishres.2026.1076582942-s2.0-105028333888Q2WOS:001676998500001Q20000-0001-8875-48670000-0001-9313-4910