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Fish age is an important biological variable required as part of routine stock assessment and analysis of fish population dynamics. Age estimates are traditionally obtained by human experts from the count of ring-like patterns along calcified structures such as otoliths. To automate the process and minimize human bias, modern methods have been designed utilizing the advances in the field of artificial intelligence (AI). While many AI-based methods have been shown to attain satisfactory accuracy, there are concerns regarding the lack of explainability of some early implementations. Consequently, new explainable AI-based approaches based on U-Net and Mask R-CNN have been recently published having direct compatibility with traditional ring counting procedures. Here we further extend this endeavor by creating an interactive website housing these explainable AI methods allowing age readers to be directly involved in the AI training and development. An important aspect of the platform presented in this article is that it allows the additional use of different advanced concepts of Machine Learning (ML) such as transfer learning, ensemble learning and continual learning, which are all shown to be effective in this study. [ABSTRACT FROM AUTHOR]

AbstractFisheries science aims to understand and manage marine natural resources. It relies on resource-intensive sampling and data analysis. Within this context, the emergence of machine learning (ML) systems holds significant promise for understanding disparate components of these marine ecosystems and gaining a greater understanding of their dynamics. The goal of this paper is to present a review of ML applications in fisheries science. It highlights both their advantages over conventional approaches and their drawbacks, particularly in terms of operationality and possible robustness issues. This review is organized from small to large scales. It begins with genomics and subsequently expands to individuals (catch items), aggregations of different species in situ, on-board processing, stock/populations assessment and dynamics, spatial mapping, fishing-related organizational units, and finally ecosystem dynamics. Each field has its own set of challenges, such as pre-processing steps, the quantity and quality of training data, the necessity of appropriate model validation, and knowing where ML algorithms are more limited, and we discuss some of these discipline-specific challenges. The scope of discussion of applied methods ranges from conventional statistical methods to data-specific approaches that use a higher level of semantics. The paper concludes with the potential implications of ML applications on management decisions and a summary of the benefits and challenges of using these techniques in fisheries. [ABSTRACT FROM AUTHOR]