Fisheries and Aquatic Sciences

The Korean Society of Fisheries and Aquatic Science (한국수산과학회)
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Abnormal behaviour in rock bream (Oplegnathus fasciatus) detected using deep learning-based image analysis

  • Received : 2021.11.15
  • Accepted : 2022.01.17
  • Published : 2022.03.31
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Abstract

Various approaches have been applied to transform aquaculture from a manual, labour-intensive industry to one dependent on automation technologies in the era of the fourth industrial revolution. Technologies associated with the monitoring of physical condition have successfully been applied in most aquafarm facilities; however, real-time biological monitoring systems that can observe fish condition and behaviour are still required. In this study, we used a video recorder placed on top of a fish tank to observe the swimming patterns of rock bream (Oplegnathus fasciatus), first one fish alone and then a group of five fish. Rock bream in the video samples were successfully identified using the you-only-look-once v3 algorithm, which is based on the Darknet-53 convolutional neural network. In addition to recordings of swimming behaviour under normal conditions, the swimming patterns of fish under abnormal conditions were recorded on adding an anaesthetic or lowering the salinity. The abnormal conditions led to changes in the velocity of movement (3.8 ± 0.6 cm/s) involving an initial rapid increase in speed (up to 16.5 ± 3.0 cm/s, upon 2-phenoxyethanol treatment) before the fish stopped moving, as well as changing from swimming upright to dying lying on their sides. Machine learning was applied to datasets consisting of normal or abnormal behaviour patterns, to evaluate the fish behaviour. The proposed algorithm showed a high accuracy (98.1%) in discriminating normal and abnormal rock bream behaviour. We conclude that artificial intelligence-based detection of abnormal behaviour can be applied to develop an automatic bio-management system for use in the aquaculture industry.

Keywords

Acknowledgement

This research was funded by the Ministry of Oceans and Fisheries, Republic of Korea, under Contract No. 20180373 as a part of the project "Establishing a Foundation for the Year-Round Production of Flatfish Eggs and Improving Productivity. YR Kim was also supported by 'LED-Marine Technology Convergence R&D Center" funded by the Ministry of Ocean and Fisheries, Korea.

References

  1. Allken V, Handegard NO, Rosen S, Schreyeck T, Mahiout T, Malde K. Fish species identification using a convolutional neural network trained on synthetic data. ICES J Mar Sci. 2019;76:342-9. https://doi.org/10.1093/icesjms/fsy147
  2. Ayob AF, Khairuddin K, Mustafah YM, Salisa AR, Kadir K. Analysis of pruned neural networks (MobileNetV2-YOLO v2) for underwater object detection. In: Proceedings of the 11th National Technical Seminar on Unmanned System Technology; 2019; Gambang, Malaysia.
  3. Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell. 2013;35:1798-828. https://doi.org/10.1109/TPAMI.2013.50
  4. Choi Y, Kim JH, Park JY. Marine fishes of Korea. Seoul, Korea: KyoHak; 2002.
  5. Cutter G, Stierhoff K, Zeng J. Automated detection of rockfish in unconstrained underwater videos using Haar cascades and a new image dataset: labeled fishes in the wild. Paper presented at: IEEE Winter Conference on Applications of Computer Vision Workshops; 2015; Waikoloa, HI.
  6. Fukusho K. Studies on fry production of Japanese striped knife jaw Oplegnathus fasciatus, with special reference to feeding ecology and mass culture of food organisms. Spec Rep Nagasaki Pre Ins Fish. 1979; 430:173.
  7. Girshick R. Fast R-CNN. Paper presented at: 2015 IEEE International Conference on Computer Vision (ICCV); 2015; Santiago, Chile. pp. 1440-8.
  8. Han F, Yao J, Zhu H, Wang C. Marine organism detection and classification from underwater vision based on the deep CNN method. Math Probl Eng. 2020;3937580.
  9. Jang JC, Choi MJ, Yang YS, Lee HB, Yu YM, Kim JM. Dim-light photoreceptor of chub mackerel Scomber japonicus and the photoresponse upon illumination with LEDs of different wavelengths. Fish Physiol Biochem. 2016;42:1015-25. https://doi.org/10.1007/s10695-015-0193-z
  10. Jang JC, Noh GE, Kim YR, Yu YM, Kim JM. Spectral sensitivity and photoresponse in the rock bream Oplegnathus fasciatus and their relationships with the absorption maximum of the photoreceptor. Fish Physiol Biochem. 2019;45:1759-69. https://doi.org/10.1007/s10695-019-00672-z
  11. Kumai H. Biological studies on culture of the Japanese parrot fish Oplegnathus fasciatus (Temmincket Schlegel). Bull Fish Lab Kinki Univ. 1984;2:127.
  12. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436-44. https://doi.org/10.1038/nature14539
  13. Levy D, Belfer Y, Osherov E, Bigal E, Scheinin AP, Nativ H, et al. Automated analysis of marine video with limited data. Papaer presented at: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 2018; Salt Lake City, UT.
  14. Ma K, Huang B, Yin H. Underwater sea cucumbers detection based on improved SSD. Paper presented at: 2019 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS); 2019; Shenyang, China.
  15. Maggio E, Cavallaro A. Video tracking: theory and practice. Hoboken, NJ: John Wiley & Sons; 2011.
  16. Noldus LPJJ, Spink AJ, Tegelenbosch RAJ. EthoVision: a versatile video tracking system for automation of behavioral experiments. Behav Res Methods Instrum Comput. 2001;33:398-414. https://doi.org/10.3758/BF03195394
  17. Redmon J, Farhadi A. Yolov3: an incremental improvement. 2018. https://arxiv.org/abs/1804.02767
  18. Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell. 2017;39:1137-1149. https://doi.org/10.1109/TPAMI.2016.2577031
  19. Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015;61:85-117. https://doi.org/10.1016/j.neunet.2014.09.003
  20. Sun M, Yang X, Xie Y. Deep learning in aquaculture: a review. J Comput. 2020;31:294-319.
  21. Yang X, Zhang S, Liu J, Gao Q, Dong S, Zhou C. Deep learning for smart fish farming: applications, opportunities and challenges. Rev Aquacult. 2020;13:66-90. https://doi.org/10.1111/raq.12464