The effect of LED lights on trap catches in Finnish inland fisheries

Ruokonen, Timo; Keskinen, Tapio; Luoma, Mikael; Leskelä, Ari; Suuronen, Petri

doi:10.1111/fme.12482

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…In the meantime, AI may already facilitate the assessment of fishing gear modification. When a fishing gear is designed with a new stimulus (e.g., Southworth et al, 2020;Ruokonen et al, 2021) or when its parts are modified (e.g., Feekings et al, 2019), the certainty that they dominantly cause a change in behavior of fishes leading to escapes or retention is impossible to single out due the large variability in external and internal factors affecting the fishes' responses. It is also unlikely that the exact movements by the same community of fishes can be observed upon two successive occasions (Ryer and Barnett, 2006;Ryer et al, 2010;Lomeli and Wakefield, 2019).…”

Section: Towards Smart Fishingmentioning

confidence: 99%

Artificial intelligence for fish behavior recognition may unlock fishing gear selectivity

2023

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Through the advancement of observation systems, our vision has far extended its reach into the world of fishes, and how they interact with fishing gears—breaking through physical boundaries and visually adapting to challenging conditions in marine environments. As marine sciences step into the era of artificial intelligence (AI), deep learning models now provide tools for researchers to process a large amount of imagery data (i.e., image sequence, video) on fish behavior in a more time-efficient and cost-effective manner. The latest AI models to detect fish and categorize species are now reaching human-like accuracy. Nevertheless, robust tools to track fish movements in situ are under development and primarily focused on tropical species. Data to accurately interpret fish interactions with fishing gears is still lacking, especially for temperate fishes. At the same time, this is an essential step for selectivity studies to advance and integrate AI methods in assessing the effectiveness of modified gears. We here conduct a bibliometric analysis to review the recent advances and applications of AI in automated tools for fish tracking, classification, and behavior recognition, highlighting how they may ultimately help improve gear selectivity. We further show how transforming external stimuli that influence fish behavior, such as sensory cues and gears as background, into interpretable features that models learn to distinguish remains challenging. By presenting the recent advances in AI on fish behavior applied to fishing gear improvements (e.g., Long Short-Term Memory (LSTM), Generative Adversarial Network (GAN), coupled networks), we discuss the advances, potential and limits of AI to help meet the demands of fishing policies and sustainable goals, as scientists and developers continue to collaborate in building the database needed to train deep learning models.

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