A Dynamic Data-driven Decision Support for Aquaculture Farm Closure

Shahriar, Md. Sumon; McCulluch, John

doi:10.1016/j.procs.2014.05.111

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…More attention is being paid to data and intelligence in current fish farming than ever before. As shown in Figure 1, data-driven intelligence methods, including artificial intelligence and big data, have begun to transform these data into operable information for smart fish farming (Shahriar & McCulluch 2014;Olyaie et al 2017). Artificial intelligence, especially machine learning and computer vision applications, is the next frontier technology of fishery data systems (Bradley et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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Deep learning for smart fish farming: applications, opportunities and challenges

Yang

Zhang

Liu

et al. 2020

Reviews in Aquaculture

213

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The rapid emergence of deep learning (DL) technology has resulted in its successful use in various fields, including aquaculture. DL creates both new opportunities and a series of challenges for information and data processing in smart fish farming. This paper focuses on applications of DL in aquaculture, including live fish identification, species classification, behavioural analysis, feeding decisions, size or biomass estimation, and water quality prediction. The technical details of DL methods applied to smart fish farming are also analysed, including data, algorithms and performance. The review results show that the most significant contribution of DL is its ability to automatically extract features. However, challenges still exist; DL is still in a weak artificial intelligence stage and requires large amounts of labelled data for training, which has become a bottleneck that restricts further DL applications in aquaculture. Nevertheless, DL still offers breakthroughs for addressing complex data in aquaculture. In brief, our purpose is to provide researchers and practitioners with a better understanding of the current state of the art of DL in aquaculture, which can provide strong support for implementing smart fish farming applications.

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Section: Introductionmentioning

confidence: 99%

“…More attention is being paid to data and intelligence in current fish farming than ever before. As shown in Figure 1, data‐driven intelligence methods, including artificial intelligence and big data, have begun to transform these data into operable information for smart fish farming (Shahriar & McCulluch 2014; Olyaie et al . 2017).…”

Section: Introductionmentioning

confidence: 99%