Abstract
The age structure of a fish population has important implications for recruitment processes and population fluctuations, and is a key input to fisheries-assessment models. The current method of determining age structure relies on manually reading age from otoliths, and the process is labor intensive and dependent on specialist expertise. Recent advances in machine learning have provided methods that have been remarkably successful in a variety of settings, with potential to automate analysis that previously required manual curation. Machine learning models have previously been successfully applied to object recognition and similar image analysis tasks. Here we investigate whether deep learning models can also be used for estimating the age of otoliths from images. We adapt a pre-trained convolutional neural network designed for object recognition, to estimate the age of fish from otolith images. The model is trained and validated on a large collection of images of Greenland halibut otoliths. We show that the model works well, and that its precision is comparable to documented precision obtained by human experts. Automating this analysis may help to improve consistency, lower cost, and increase the extent of age estimation. Given that adequate data are available, this method could also be used to estimate age of other species using images of otoliths or fish scales.
Highlights
Age of fish is a key parameter in age-structured fisheries-assessment models
Age is usually considered as a discrete parameter that identifies the individual year class i.e. those originating from the spawning activity in a given year [1]
Predictions were made on the test set for the different configurations and the mean squared error (MSE) of single otolith predictions of age were recorded
Summary
Age of fish is a key parameter in age-structured fisheries-assessment models. Age is usually considered as a discrete parameter (age group) that identifies the individual year class i.e. those originating from the spawning activity in a given year [1]. An individual is categorized as age group 0 from the first early larval stage, and all age groups increase their age at 1 January. Assessment models typically express the dynamics of the individual year class from the age when they recruit, through sexual maturation, reproduction, and throughout their life cycle [2]. The models are fitted to data originating from commercial catches and fisheries-independent surveys. A sampling program for a specific fish stock typically involves sampling throughout the year using several different types of fishing gears
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