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Recent advances of deep learning algorithms for aquacultural machine vision systems with emphasis on fish

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Abstract

Monitoring the growth conditions and behavior of fish will enable scientific management, reduce the threat of losses caused by disease and stress. Traditional monitoring methods are time-consuming, laborious, and untimely monitoring readily leads to aquaculture accidents. As a non-invasive, objective, and repeatable tool, machine vision systems have been widely used in various aspects of aquaculture monitoring. Nevertheless, the complex underwater environment makes it difficult to obtain ideal data processing results only using traditional image processing methods. Due to their powerful feature extraction capabilities, deep learning (DL) algorithms have been widely used in underwater image processing. Hence, the combination of DL algorithms and machine vision for the automated monitoring of aquaculture is of great importance. As evidence for the multidisciplinary aspects of DL applications, attention is focused on the latest DL methods applied to five fields of research: classification, detection, counting, behavior recognition, and biomass estimation. Meanwhile, due to the low training efficiency of DL models caused by insufficient dataset, transfer learning and GAN have also put into spotlight of this filed to pursue high performance of DL models. We also present the challenges and benchmarks in terms of the advantages and disadvantages of the selected method in each field. In addition, we review the sources of image acquisition and pre-processing methods in aquaculture. Finally, the challenges and prospects of DL in aquaculture machine vision systems are discussed. The literature review shows that the deep neural networks such as AlexNet, LSTM, VGG, and GoogLeNet, have been used for aquaculture machine vision systems.

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Acknowledgements

This work is supported by National Key R&D Program of China “Next generation precision aquaculture: R&D on intelligent measurement, control and equipment technologies” [2017YFE0122100]; Research on the rapid detection mechanism and method of trace-level toxic nitrogen in aquaculture water based on SERS optopole [2018QC188]. The authors also would like to thank Charlesworth Author Services for providing an English-language edit of this article.

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Authors and Affiliations

  1. National Innovation Center for Digital Fishery, China Agricultural University, Beijing, People’s Republic of China

    Daoliang Li & Ling Du

  2. Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agriculture University, Beijing, 100083, People’s Republic of China

    Daoliang Li & Ling Du

  3. China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, 100083, People’s Republic of China

    Daoliang Li & Ling Du

  4. Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agriculture University, Beijing, 100083, People’s Republic of China

    Daoliang Li & Ling Du

  5. College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, People’s Republic of China

    Daoliang Li & Ling Du

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  1. Daoliang Li
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  2. Ling Du
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Correspondence to Daoliang Li.

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Li, D., Du, L. Recent advances of deep learning algorithms for aquacultural machine vision systems with emphasis on fish. Artif Intell Rev 55, 4077–4116 (2022). https://doi.org/10.1007/s10462-021-10102-3

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