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Measurement and Analysis of Detecting Fish Freshness Levels Using Deep Learning Method

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 18 No. 4 (2024) · pp. 359–370

DOI
10.22146/ijccs.95054
ISSN
2460-7258, 1978-1520
Published
2024-10-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Subjective and objective tests used to determine the fish deterioration process require specialized skills and time, making them inefficient for use by the general public in markets. The quality of fish products in markets is not always guaranteed, so consumers must determine their suitability. Deep learning can be used to analyze images and automatically and accurately detect the freshness of fish. This study aims to evaluate the efficiency of deep learning models in detecting fish freshness and implementing them into an Android application for public use. "Image datasets and pH tests were collected as references for the postmortem phase over a 24-hour period, with hourly checks on three fish species (Rachycentron canadum, Trachinotus blochi, and Lates calcarifer). Data were classified into three classes, pre-rigor/fresh, rigor mortis/semi-fresh, and post-rigor/not fresh. The dataset was divided using the 10-fold cross-validation method and analyzed using YOLOv5 and Faster R-CNN algorithms. The study results showed that YOLOv5 had higher average values for each metric compared to Faster R-CNN. Dataset 8 in YOLOv5 showed precision of 99.4%, recall of 98.1%, f1-score of 98.7%, accuracy of 99.3%, and mAP of 99.3%. The YOLOv5 model for dataset 8 was selected for implementation in the Android application due to its high metric values. This application effectively provides information on fish freshness detection and confidence scores.

Keywords

Collaboration network of the authors

7 authors · 9 co-authorship links · 4 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • IPB University
  • Politeknik Pertanian Negeri Payakumbuh
  • University of National Education
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Indra LaksmanaPoliteknik Pertanian Negeri Payakumbuh1
Ervizal A.M. ZuhudIPB University1
Ngakan Nyoman Kutha KrisnawijayaUniversity of National Education1
Bib Paruhum SilalahiIPB University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Measurement and Analysis of Detecting Fish Freshness Levels Using Deep Learning Method</dc:title>
	<dc:creator>Anas, Dhea Fajriati</dc:creator>
	<dc:creator>Jaya, Indra</dc:creator>
	<dc:creator>Herdiyeni, Yeni</dc:creator>
	<dc:subject xml:lang="en-US">Marine Technology</dc:subject>
	<dc:subject xml:lang="en-US">Fish Freshness; YOLOv5; Faster-RCNN; Object Detection; Android Appliaction; pH</dc:subject>
	<dc:description xml:lang="en-US">Subjective and objective tests used to determine the fish deterioration process require specialized skills and time, making them inefficient for use by the general public in markets. The quality of fish products in markets is not always guaranteed, so consumers must determine their suitability. Deep learning can be used to analyze images and automatically and accurately detect the freshness of fish. This study aims to evaluate the efficiency of deep learning models in detecting fish freshness and implementing them into an Android application for public use. "Image datasets and pH tests were collected as references for the postmortem phase over a 24-hour period, with hourly checks on three fish species (Rachycentron canadum, Trachinotus blochi, and Lates calcarifer). Data were classified into three classes, pre-rigor/fresh, rigor mortis/semi-fresh, and post-rigor/not fresh. The dataset was divided using the 10-fold cross-validation method and analyzed using YOLOv5 and Faster R-CNN algorithms. The study results showed that YOLOv5 had higher average values for each metric compared to Faster R-CNN. Dataset 8 in YOLOv5 showed precision of 99.4%, recall of 98.1%, f1-score of 98.7%, accuracy of 99.3%, and mAP of 99.3%. The YOLOv5 model for dataset 8 was selected for implementation in the Android application due to its high metric values. This application effectively provides information on fish freshness detection and confidence scores.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
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	<dc:date>2024-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/95054</dc:identifier>
	<dc:identifier>10.22146/ijccs.95054</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 18, No 4 (2024): October; 359-370</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/95054/40364</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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