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Simple and Effective Techniques for Automatic Fish Species Classification Using Image Processing and Deep Learning.

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Title: Simple and Effective Techniques for Automatic Fish Species Classification Using Image Processing and Deep Learning.
Authors: Kuswantori, Ari1 (AUTHOR), Nunak, Navaphattra2 (AUTHOR) navaphattra.nu@kmitl.ac.th, Suthanupaphwut, Worapanya2 (AUTHOR), Schleining, Gerhard3 (AUTHOR), Tangsrirat, Worapong4 (AUTHOR), Suesut, Taweepol4 (AUTHOR) taweepol.su@kmitl.ac.th, Hazra, Arpan (AUTHOR)
Source: Journal of Electrical & Computer Engineering. 8/28/2025, Vol. 2025, p1-14. 14p.
Subjects: Classification of fish, Deep learning, Image processing, Feature extraction, Aquaculture, Automation, Computer vision
Abstract: The advancement of automation in the fish industry, a critical segment of the food sector, has become increasingly relevant in light of the growing global population and the impacts of climate change and global warming. Enhancing productivity through automation is essential to mitigate the looming threat of food scarcity. In this context, automatic fish classification using computer vision has garnered significant attention, with various studies exploring both complex and simple approaches. While complex methods have shown promising results, simpler approaches often fall short in performance. This study proposes a simple yet effective method that highlights key distinguishing features of fish—namely, body shape and scale patterns—for species classification. The Lanczos resampling technique is employed to crop, resize, and focus on the features, enabling a lightweight deep learning model to effectively learn and classify fish species. With the right conceptual framework, appropriate feature extraction techniques, and an efficient deep learning architecture, the proposed method addresses the classification challenge in a straightforward yet effective manner. Experimental evaluations using the Fish‐Pak dataset, comprising six aquaculture fish species, and the KMITL Fish dataset, containing eight species, demonstrate the effectiveness of the method, achieving accuracy rates of 97.16% and 98.59%, respectively. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Electrical & Computer Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
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  Data: Simple and Effective Techniques for Automatic Fish Species Classification Using Image Processing and Deep Learning.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Electrical+%26+Computer+Engineering%22">Journal of Electrical & Computer Engineering</searchLink>. 8/28/2025, Vol. 2025, p1-14. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Classification+of+fish%22">Classification of fish</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Aquaculture%22">Aquaculture</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: The advancement of automation in the fish industry, a critical segment of the food sector, has become increasingly relevant in light of the growing global population and the impacts of climate change and global warming. Enhancing productivity through automation is essential to mitigate the looming threat of food scarcity. In this context, automatic fish classification using computer vision has garnered significant attention, with various studies exploring both complex and simple approaches. While complex methods have shown promising results, simpler approaches often fall short in performance. This study proposes a simple yet effective method that highlights key distinguishing features of fish—namely, body shape and scale patterns—for species classification. The Lanczos resampling technique is employed to crop, resize, and focus on the features, enabling a lightweight deep learning model to effectively learn and classify fish species. With the right conceptual framework, appropriate feature extraction techniques, and an efficient deep learning architecture, the proposed method addresses the classification challenge in a straightforward yet effective manner. Experimental evaluations using the Fish‐Pak dataset, comprising six aquaculture fish species, and the KMITL Fish dataset, containing eight species, demonstrate the effectiveness of the method, achieving accuracy rates of 97.16% and 98.59%, respectively. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Electrical & Computer Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1155/jece/8896674
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      – Code: eng
        Text: English
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        PageCount: 14
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      – SubjectFull: Classification of fish
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Image processing
        Type: general
      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: Aquaculture
        Type: general
      – SubjectFull: Automation
        Type: general
      – SubjectFull: Computer vision
        Type: general
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      – TitleFull: Simple and Effective Techniques for Automatic Fish Species Classification Using Image Processing and Deep Learning.
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            NameFull: Kuswantori, Ari
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            NameFull: Suthanupaphwut, Worapanya
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              M: 08
              Text: 8/28/2025
              Type: published
              Y: 2025
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