Fish Tracking, Counting, and Behaviour Analysis in Digital Aquaculture: A Comprehensive Survey

Reviews in Aquaculture, Journal Year: 2025, Volume and Issue: 17(1)

Published: Jan. 1, 2025

ABSTRACT Digital aquaculture leverages advanced technologies and data‐driven methods, providing substantial benefits over traditional practices. This article presents a comprehensive review of three interconnected digital tasks, namely, fish tracking, counting, behaviour analysis, using novel unified approach. Unlike previous reviews which focused on single modalities or individual we analyse vision‐based (i.e., image‐ video‐based), acoustic‐based, biosensor‐based methods across all tasks. We examine their advantages, limitations, applications, highlighting recent advancements identifying critical cross‐cutting research gaps. The also includes emerging ideas such as applying multitask learning large language models to address various aspects monitoring, an approach not previously explored in literature. identify the major obstacles hindering progress this field, including scarcity datasets lack evaluation standards. To overcome current explore potential multimodal data fusion deep improve accuracy, robustness, efficiency integrated monitoring systems. In addition, provide summary existing available for analysis. holistic perspective offers roadmap future research, emphasizing need standards facilitate meaningful comparisons between promote practical implementations real‐world settings.

Language: Английский

TMVF: Trusted Multi-View Fish Behavior Recognition with correlative feature and adaptive evidence fusion

Information Fusion, Journal Year: 2025, Volume and Issue: 118, P. 102899 - 102899

Published: Jan. 8, 2025

Language: Английский

A review of the application prospects of cloud-edge-end collaborative technology in freshwater aquaculture

Artificial Intelligence in Agriculture, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский