An efficient protocol and data set for automated otolith image analysis

Abstract

Abstract Information on fish age constitutes one of the most important biological variables for a fish stock, and an accurate estimation of the age structure of the fish populations is essential for the reliable management of these natural resources. The age of individual cod (Gadus morhua) is determined by manually examining the layered structure of otoliths, a calcium carbonate structure of the inner ear. Image‐based methods to age otoliths have been investigated for over 4 decades with varying results, but recent developments in automatic image analysis techniques are promising. The objective of this paper is to describe a method to efficiently image a manually broken otolith (avoiding the time‐consuming embedding and cross‐sectioning process) and to describe the organization and acquisition of imaged broken otolith images with associated metadata for a collection of north‐east Arctic cod otoliths. A single‐lens reflex camera was used for capturing photographs of the broken otoliths. A total of six images were acquired for each subject, consisting of three images in the first position with three different light exposures and three images in the second position with three different light exposures. This results in a simple and efficient procedure for capturing clear, satisfactory, and reproducible images of broken fish otoliths, and a more straightforward and less labour‐intensive alternative to the commonly used methods that involve embedding and cross‐sectioning of the otolith.

Keywords

• big data
• deep learning
• fish ageing
• Gadus morhua
• north‐east arctic cod
• Otolith