Age interpretation of cod otoliths using deep learning

Abstract

Fish age estimation plays a crucial role in stock management and provides valuable information for biological studies. Fish age is typically estimated by experts that manually count annual increments in otoliths. This process is prone to age reader bias, which makes comparisons between readers and labs challenging, and requires considerable time and resources. In this study, we developed a machine learning framework for fish age prediction using 5150 images of otoliths from Barents Sea Atlantic cod (Gadus morhua) collected between 2012 and 2018. In contrast to previous studies that utilise models trained on otolith sections, we used images of broken otoliths that require no processing prior to imaging, and hence, could potentially facilitate at-sea age estimation. We trained convolutional neural networks (CNNs) based on two modern architectures (EfficientNetV1 and EfficientNetV2), which vary in model size (number of model parameters), and compared performance. Model average accuracy was 72.7% and mean-squared-error was 0.284 when compared with the human-read ages. The models' accuracy for one- and two-year-old individuals was over 90% and no systematic bias in the age predictions across age groups was detected. The best models were EfficientNet B4 and EfficientNet B6 using images taken with low exposure times. A maximum accuracy of 78.6% was achieved using an ensemble consisting of six models. Model predictions were also strongly correlated, limiting the utility of building large ensembles Model performance was compared to the results of an internal workshop where 100 independent images of broken otoliths were aged by a group of experts. Variations in percentage agreement between age classes showed a similar pattern (decreasing with age) in both CNN-based predictions and age estimates made by the expert group. While CNN-based percentage agreement was often lower than the expert estimates, it remained within or close to the range of percentage agreement observed across all readers. Our results demonstrate the potential of deep learning techniques for extracting age estimates from otolith images. When developing frameworks for age estimation using machine learning, we recommend EfficientNet B4 models are used as they are quicker to train than larger models and perform well. Ensemble approaches are also recommended if sufficient computational resources are available, as they can provide increased accuracy and lower variance of the predictions.