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A Review on Fish Species Classification and Determination Using Machine Learning Algorithms

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Intelligent Sustainable Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 458))

Abstract

About 50% of world population depend on seafood for the protein content. Because of the nature resource, illegal fishery in addition with uncultured task is providing threat to marine life. Few standard analytical methods are being applied to determine the fish freshness, quality and species discrimination with respect to physicochemical properties. Colour, meat elasticity, odour, taste, texture and outer appearance are the attributes acquired for determination. These methods require highly skilled operators, expensive, destructive and time consuming. In the last decade, advancement in the recent techniques made the fish species discrimination freshness and quality evaluation to be non-invasive and non-destructive rapid analysis. Spectroscopic, biosensors, image processing and E-sensors are the reliable techniques that provide better instrumental evaluation and making them suitable for the online/real-time analysis. This review work discusses the novel techniques and the results obtained for the fish quality and species examination.

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References

  1. L. Franceschelli, A. Berardinelli, S. Dabbou, L. Ragni, M. Tartagni, Sensing technology for fish freshness and safety: a review. Sensors 21(4), 1373 (2021)

    Article  Google Scholar 

  2. D. Rathi, S. Jain, S. Indu, Underwater fish species classification using convolutional neural network and deep learning, in 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) (IEEE, 2017), pp. 1–6

    Google Scholar 

  3. A. Bashar, Survey on evolving deep learning neural network architectures. J. Artif. Intell. 1(02), 73–82 (2019)

    Google Scholar 

  4. A. Sungheetha, R. Sharma, Design an early detection and classification for diabetic retinopathy by deep feature extraction based convolution neural network. J. Trends Comput. Sci. Smart Technol. (TCSST) 3(02), 81–94 (2021)

    Article  Google Scholar 

  5. K.M. Knausgård, A. Wiklund, T.K. Sørdalen, K.T. Halvorsen, A.R. Kleiven, L. Jiao, M. Goodwin, Temperate fish detection and classification: a deep learning based approach. Appl. Intell. 1–14 (2021)

    Google Scholar 

  6. R. Dhaya, Flawless identification of fusarium oxysporum in tomato plant leaves by machine learning algorithm. J. Innov. Image Process. (JIIP) 2(04), 194–201 (2020)

    Google Scholar 

  7. N.S. Abinaya, D. Susan, R. Kumar, Naive Bayesian fusion based deep learning networks for multi segmented classification of fishes in aquaculture industries. Eco. Inform. 61, 101248 (2021)

    Article  Google Scholar 

  8. X. Lan, J. Bai, M. Li, J. Li, Fish image classification using deep convolutional neural network, in Proceedings of the 2020 International Conference on Computers, Information Processing and Advanced Education, 2020, pp. 18–22

    Google Scholar 

  9. M.A. Iqbal, Z. Wang, Z.A. Ali, S. Riaz, Automatic fish species classification using deep convolutional neural networks. Wireless Pers. Commun. 116(2), 1043–1053 (2021)

    Article  Google Scholar 

  10. A. Salman, A. Jalal, F. Shafait, A. Mian, M. Shortis, J. Seager, E. Harvey, Fish species classification in unconstrained underwater environments based on deep learning. Limnol. Oceanogr. Methods 14(9), 570–585 (2016)

    Article  Google Scholar 

  11. U. Andayani, A. Wijaya, R.F. Rahmat, B. Siregar, M.F. Syahputra, Fish species classification using probabilistic neural network. J. Phys. Conf. Ser. 1235(1), 012094 (2019)

    Google Scholar 

  12. P. Menesatti, C. Costa, J. Aguzzi, Quality evaluation of fish by hyperspectral imaging, in Hyperspectral Imaging for Food Quality Analysis and Control (Academic Press, 2010), pp. 273–294

    Google Scholar 

  13. M.A. Kolmann, M. Kalacska, O. Lucanus, L. Sousa, D. Wainwright, J.P. Arroyo-Mora, M.C. Andrade, Hyperspectral data as a biodiversity screening tool can differentiate among diverse Neotropical fishes. Sci. Rep. 11(1), 1–15 (2021)

    Article  Google Scholar 

  14. J.S. Manoharan, Study of variants of extreme learning machine (ELM) brands and its performance measure on classification algorithm. J. Soft Comput. Paradigm (JSCP) 3(02), 83–95 (2021)

    Google Scholar 

  15. https://globalwetlandsproject.org/automated-analysis-of-aquatic-videos-accessible-machine-learning-tools-for-ecologists/

  16. P. Marti-Puig, A. Manjabacas, A. Lombarte, Automatic classification of morphologically similar fish species using their head contours. Appl. Sci. 10(10), 3408 (2020)

    Article  Google Scholar 

  17. E.M. Ditria, S. Lopez-Marcano, M. Sievers, E.L. Jinks, C.J. Brown, R.M. Connolly, Automating the analysis of fish abundance using object detection: optimizing animal ecology with deep learning. Front. Mar. Sci. 7, 429 (2020)

    Article  Google Scholar 

  18. B.S. Rekha, G.N. Srinivasan, S.K. Reddy, D. Kakwani, N. Bhattad, Fish detection and classification using convolutional neural networks, in International Conference on Computational Vision and Bio Inspired Computing (Springer, Cham, 2019), pp. 1221–1231

    Google Scholar 

  19. G. Olafsdottir, P. Nesvadba, C. Di Natale, M. Careche, J. Oehlenschläger, S.V. Tryggvadottir, R. Schubring, M. Kroeger, K. Heia, M. Esaiassen, A. Macagnano, Multisensor for fish quality determination. Trends Food Sci. Technol. 15(2), 86–93 (2004)

    Article  Google Scholar 

  20. M.R. García, M.L. Cabo, J.R. Herrera, G. Ramilo-Fernández, A.A. Alonso, E. Balsa-Canto, Smart sensor to predict retail fresh fish quality under ice storage. J. Food Eng. 197, 87–97 (2017)

    Google Scholar 

  21. X. Huang, J. Xin, J. Zhao, A novel technique for rapid evaluation of fish freshness using colorimetric sensor array. J. Food Eng. 105(4), 632–637 (2011)

    Article  Google Scholar 

  22. K. Dharmendra, S. Kumar, S.S. Rajput, An Intelligent System For Fish Freshness Quality Assessment Using Artificial Neural Network

    Google Scholar 

  23. M. Senapati, P.P. Sahu, Onsite fish quality monitoring using ultra-sensitive patch electrode capacitive sensor at room temperature. Biosens. Bioelectron. 168, 112570 (2020)

    Article  Google Scholar 

  24. N. Kaneki, H. Tanaka, T. Kurosaka, K. Shimada, Y. Asano, Measurement of fish freshness using potentiometric gas sensor. Sens. Mater. 15(8), 413–422 (2003)

    Google Scholar 

  25. E. Yavuzer, Determination of fish quality parameters with low cost electronic nose. Food Biosci. 41, 100948 (2021)

    Article  Google Scholar 

  26. V. Allken, N.O. Handegard, S. Rosen, T. Schreyeck, T. Mahiout, K. Malde, Fish species identification using a convolutional neural network trained on synthetic data. ICES J. Mar. Sci. 76(1), 342–349 (2019)

    Article  Google Scholar 

  27. S. Cui, Y. Zhou, Y. Wang, L. Zhai, Fish detection using deep learning, in Applied Computational Intelligence and Soft Computing (2020)

    Google Scholar 

  28. T. Vijayakumar, Comparative study of capsule neural network in various applications. J. Artif. Intell. 1(01), 19–27 (2019)

    Google Scholar 

  29. P. Hridayami, I.K.G.D. Putra, K.S. Wibawa, Fish species recognition using VGG16 deep convolutional neural network. J. Comput. Sci. Eng. 13(3), 124–130 (2019)

    Google Scholar 

  30. N. Petrellis, Measurement of fish morphological features through image processing and deep learning techniques. Appl. Sci. 11(10), 4416 (2021)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of ECE, SRM IST, Chennai, 6032031, India

    Sowmya Natarajan & Vijayakumar Ponnusamy

Authors
  1. Sowmya Natarajan
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  2. Vijayakumar Ponnusamy
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Corresponding author

Correspondence to Vijayakumar Ponnusamy .

Editor information

Editors and Affiliations

  1. Gnanmani College of Engineering and Technology, Namakkal, India

    Jennifer S. Raj

  2. Department of Computer Science, Kennesaw State University, Kennesaw, GA, USA

    Yong Shi

  3. Faculty of Communication Sciences, University of Teramo, Teramo, Italy

    Danilo Pelusi

  4. Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

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Natarajan, S., Ponnusamy, V. (2022). A Review on Fish Species Classification and Determination Using Machine Learning Algorithms. In: Raj, J.S., Shi, Y., Pelusi, D., Balas, V.E. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 458. Springer, Singapore. https://doi.org/10.1007/978-981-19-2894-9_49

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