Otolith Image-Based Age Classification of Japanese Jack Mackerel Trachurus japonicus Using Convolutional Neural Networks

Reliable age information is needed for fisheries assessment, but conventional otolith reading requires trained readers and considerable time. This study evaluated whether convolutional neural networks could classify reader-assigned age classes of Japanese jack mackerel Trachurus japonicus directly from sagittal otolith images. Otolith images from fish aged 0 to 4 years were used to compare three image-only backbones: Inception v3, Xception, and EfficientNet B4. The models were trained under the same data split, preprocessing, augmentation, and evaluation framework. In Stage 1, Inception v3 showed the highest validation macro F1 score (0.933) and was selected as the image-only baseline. After additional optimization, the selected model reached a validation macro F1 score of 0.944, validation exact accuracy of 0.935, and validation agreement within one age class of 1.000. On the independent test set, the optimized image-only model achieved exact accuracy of 0.866, macro F1 score of 0.873, and agreement within one year of 1.000. These results indicate that otolith images contain useful age-related visual information. Convolutional neural networks may support age class screening in T. japonicus, although they should complement rather than replace expert otolith reading. These findings apply to the initial screening of T. japonicus within the 0 to 4 age range represented in commercial purse seine catches, and performance for ages older than 4 was not evaluated.