Abstract
Age-reading of fish otoliths (ear stones) is important for the sustainable management of fish resources. However, the procedure is challenging and requires experienced readers to carefully examine annual growth zones. In a recent study, convolutional neural networks (CNNs) have been demonstrated to perform reasonably well on automatically predicting fish age from otolith images. In the present study, we carefully investigate the prediction rule learned by such neural networks to provide insight into the features that identify certain fish age ranges. For this purpose, a recent technique for visualizing and analyzing the predictions of the neural networks was applied to different versions of the otolith images. The results indicate that supplementary knowledge about the internal structure improves the results for the youngest age groups, compared to using only the contour shape attribute of the otolith. However, the contour shape and size attributes are, in general, sufficient for older age groups. In addition, within specific age ranges we find that the network tends to focus on particular areas of the otoliths and that the most discriminating factors seem to be related to the central part and the outer edge of the otolith. Explaining age predictions from otolith images as done in this study will hopefully help build confidence in the potential of deep learning algorithms for automatic age prediction, as well as improve the quality of the age estimation.
Highlights
The reliable estimation of the age distribution of fish stocks is an important aspect in marine research and resource management in order to maintain sustainable fisheries
While manual age-readings from otoliths generally focus on annual growth zones, our results showed that neural network models provided reasonably good coefficient of variation (C V) results on the baseline and standardized data (Figs 6 and 7), without enhanced activation of annual zone pixels (Figs 8 and 9)
We showed that using a convolutional neural networks (CNNs) classification network gave similar results to an earlier study (Moen et al [4]) that used regression
Summary
The reliable estimation of the age distribution of fish stocks is an important aspect in marine research and resource management in order to maintain sustainable fisheries. A bottleneck in this area is the complicated task of age-reading of individuals. One of the procedures used for age-reading requires human experts to examine images of otoliths or ear stones (i.e. calcified structures located in the inner ear of bony fish). Specialists are trained to carefully examine incremental daily and annual growth in the otoliths [1] and may sometimes use additional auxiliary data [2] (e.g. fish size, date of capture, sex, etc.).
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