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    Automatic recognition methods of fish feeding behavior in aquaculture: a review

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    Authors
    Li, Daoliang
    Wang, Zhenhu
    Wu, Suyuan
    Miao, Zheng
    Du, Ling
    Duan, Yanqing
    Affiliation
    China Agricultural University
    Renmin University of China
    University of Bedfordshire
    Issue Date
    2020-05-23
    Subjects
    aquaculture
    Subject Categories::D435 Aquaculture
    
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    Abstract
    Feeding is a major factor that determines the production costs and water quality of aquaculture. Analysis of fish feeding behavior forms an important part of the feeding optimization. Fish feeding has generally been performed with automatic feeding machines which can lead to excessive or insufficient feeding. Recognition of fish feeding behavior can provide valuable input for optimizing feeding quantity. Due to the complexity of the environment and the uncertainty of fish behavior, the correlation and accuracy of behavior recognition are generally low. The accurate identification of fish feeding behavior till faces substantial challenges. This paper reviews the technical methods that have been used to identify fish feeding behavior in aquaculture over the past 30 years. The advantages and disadvantages of each method under different experimental conditions and applications are analyzed. Many methods are effective at evaluating and quantifying fish feeding intensity, but the recognition accuracy still needs further improvement. It is proposed by this paper that technologies such as data fusion and deep learning has great potential for improving the recognition of fish feeding behavior.
    Citation
    Li D, Wang Z, Wu S, Miao Z, Du L, Duan Y (2020) 'Automatic recognition methods of fish feeding behavior in aquaculture: a review', Aquaculture, 528, pp.735508-.
    Publisher
    Elsevier
    Journal
    Aquaculture
    URI
    http://hdl.handle.net/10547/624015
    DOI
    10.1016/j.aquaculture.2020.735508
    Additional Links
    https://www.sciencedirect.com/science/article/pii/S0044848620315258
    Type
    Article
    Language
    en
    ISSN
    0044-8486
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.aquaculture.2020.735508
    Scopus Count
    Collections
    Business and management

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