Application of machine learning in intelligent fish aquaculture: A review

Zhao, Shili; Zhang, Song; Liu, Jincun; Wang, He; Jia, Zhu; Li, Bingbing; Zhao, Ran

doi:10.1016/j.aquaculture.2021.736724

Cited by 135 publications

(87 citation statements)

References 126 publications

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“…Each machine learning model has its own advantages and disadvantages. According to Zhao et al [83], although DT and SVM are considered good models to use, they seem very sensitive to missing values. Otherwise, NB, ANN, and KNN are better than DT and SVM with many advantages such as being more efficient, less sensitive for missing values and having a high accuracy rate.…”

Section: Machine Learningmentioning

confidence: 99%

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Overview of Smart Aquaculture System: Focusing on Applications of Machine Learning and Computer Vision

Kim

2021

Electronics

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Smart aquaculture is nowadays one of the sustainable development trends for the aquaculture industry in intelligence and automation. Modern intelligent technologies have brought huge benefits to many fields including aquaculture to reduce labor, enhance aquaculture production, and be friendly to the environment. Machine learning is a subdivision of artificial intelligence (AI) by using trained algorithm models to recognize and learn traits from the data it watches. To date, there are several studies about applications of machine learning for smart aquaculture including measuring size, weight, grading, disease detection, and species classification. This review provides and overview of the development of smart aquaculture and intelligent technology. We summarized and collected 100 articles about machine learning in smart aquaculture from nearly 10 years about the methodology, results as well as the recent technology that should be used for development of smart aquaculture. We hope that this review will give readers interested in this field useful information.

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Section: Machine Learningmentioning

confidence: 99%

“…In general, machine vision/computer vision is the main tool to measure the size of fish. Images are taken through a camera that are attached in a fish measurement system and then all collected images are sent to software and are analyzed and then the results compiled [81][82][83][84].…”

Section: Applications Of Machine Learning and Computer Vision In Aquaculturementioning

confidence: 99%

Overview of Smart Aquaculture System: Focusing on Applications of Machine Learning and Computer Vision

Kim

2021

Electronics

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“…The production estimation and optimization and livestock production problems in Figure 10 are classified using the knowledge discovery and function approximation and uncertain knowledge and reasoning attributes. Such categorization is determined by the central characteristics of these problems, which are concerned with the prediction and optimization of production values using historical data records (structured data) [ 104 , 105 ]. Besides, production forecasting contains uncertainties regarding external factors of the production systems, which encourage the use of probabilistic methods, such as the studies carried out by [ 106 , 107 ].…”

Section: A Taxonomy Of Ci-based Problems In the Food Supply Chainmentioning

confidence: 99%

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

Angarita-Zapata

Alonso-Vicario

Masegosa

et al. 2021

Sensors

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In the last few years, the Internet of Things, and other enabling technologies, have been progressively used for digitizing Food Supply Chains (FSC). These and other digitalization-enabling technologies are generating a massive amount of data with enormous potential to manage supply chains more efficiently and sustainably. Nevertheless, the intricate patterns and complexity embedded in large volumes of data present a challenge for systematic human expert analysis. In such a data-driven context, Computational Intelligence (CI) has achieved significant momentum to analyze, mine, and extract the underlying data information, or solve complex optimization problems, striking a balance between productive efficiency and sustainability of food supply systems. Although some recent studies have sorted the CI literature in this field, they are mainly oriented towards a single family of CI methods (a group of methods that share common characteristics) and review their application in specific FSC stages. As such, there is a gap in identifying and classifying FSC problems from a broader perspective, encompassing the various families of CI methods that can be applied in different stages (from production to retailing) and identifying the problems that arise in these stages from a CI perspective. This paper presents a new and comprehensive taxonomy of FSC problems (associated with agriculture, fish farming, and livestock) from a CI approach; that is, it defines FSC problems (from production to retail) and categorizes them based on how they can be modeled from a CI point of view. Furthermore, we review the CI approaches that are more commonly used in each stage of the FSC and in their corresponding categories of problems. We also introduce a set of guidelines to help FSC researchers and practitioners to decide on suitable families of methods when addressing any particular problems they might encounter. Finally, based on the proposed taxonomy, we identify and discuss challenges and research opportunities that the community should explore to enhance the contributions that CI can bring to the digitization of the FSC.

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“…can collect behavioral data such as the location of fish in three dimensions, swimming trajectory, acceleration, pressure or muscular activity, swimming depth, body temperature, and acceleration (Pittman et al, 2014;Føre et al, 2017;Macaulay et al, 2021b). Moreover, progress is being made in the development of automatic methodologies that collect and analyze data from a wide range of camera systems, e.g., single or stereo cameras that can exploit different spectra of light such as the visible or infrared (Joseph et al, 2017;Saberioon et al, 2017;An et al, 2021;Zhao et al, 2021). Intelligent monitoring and control methods using mathematical models and computer vision have been developed as a result (Killen et al, 2017;Føre et al, 2018;Zhou et al, 2018;Awalludin et al, 2020;An et al, 2021;Zhao et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Tracking and Analysis of the Movement Behavior of European Seabass (Dicentrarchus labrax) in Aquaculture Systems

et al. 2021

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Monitoring and understanding fish behavior is crucial for achieving precision in everyday husbandry practices (i.e. for optimizing farm performance), and for improving fish welfare in aquaculture. Various intelligent monitoring and control methods, using mathematical models, acoustic methods and computer vision, have been recently developed for this reason. Here, a tracking algorithm based on computer vision that extracts short trajectories of individual European seabass in both recirculating aquaculture systems and sea cages was developed using videos from network cameras. Using this methodology, parameters such as instantaneous normalized speed, travel direction and preference for the tank surface by European seabass could be quantified. When testing the sensitivity of this algorithm for detecting fish swimming variations under different husbandry scenarios, we found that the algorithm could detect variations in all of the abovementioned parameters and could potentially be a useful tool for monitoring the behavioral state of European seabass.

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Application of machine learning in intelligent fish aquaculture: A review

Cited by 135 publications

References 126 publications

Overview of Smart Aquaculture System: Focusing on Applications of Machine Learning and Computer Vision

Overview of Smart Aquaculture System: Focusing on Applications of Machine Learning and Computer Vision

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

Tracking and Analysis of the Movement Behavior of European Seabass (Dicentrarchus labrax) in Aquaculture Systems

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