Fish Freshness Detection Through Artificial Intelligence Approaches: A Comprehensive Study

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dc.authoridKılıçarslan, Sabire / 0009-0007-9299-7141
dc.authoridHız Çiçekliyurt, Meliha Merve / 0000-0003-4303-9717
dc.contributor.authorKılıçarslan, Sabire
dc.contributor.authorHız Çiçekliyurt, Meliha Merve
dc.contributor.authorKılıçarslan, Serhat
dc.date.accessioned2025-01-27T19:26:39Z
dc.date.available2025-01-27T19:26:39Z
dc.date.issued2024
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractFish is regarded as an important protein source in human nutrition due to its high concentration of omega-3 fatty acids In traditional global cuisine, fish holds a prominent position, with seafood restaurants, fish markets, and eateries serving as popular venues for fish consumption. However, it is imperative to preserve fish freshness as improper storage can lead to rapid spoilage, posing risks of potential foodborne illnesses. To address this concern, artificial intelligence techniques have been utilized to evaluate fish freshness, introducing a deep learning and machine learning approach. Leveraging a dataset of 4476 fish images, this study conducted feature extraction using three transfer learning models (MobileNetV2, Xception, VGG16) and applied four machine learning algorithms (SVM, LR, ANN, RF) for classification. The synergy of Xception and MobileNetV2 with SVM and LR algorithms achieved a 100% success rate, highlighting the effectiveness of machine learning in preventing foodborne illness and preserving the taste and quality of fish products, especially in mass production facilities.
dc.identifier.doi10.24925/turjaf.v12i2.290-295.6670
dc.identifier.endpage295
dc.identifier.issn2148-127X
dc.identifier.issue2
dc.identifier.startpage290
dc.identifier.trdizinid1254395
dc.identifier.urihttps://doi.org/10.24925/turjaf.v12i2.290-295.6670
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1254395
dc.identifier.urihttps://hdl.handle.net/20.500.12428/15576
dc.identifier.volume12
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherTurkish Science and Technology Publishing (TURSTEP)
dc.relation.ispartofTurkish Journal of Agriculture - Food Science and Technology (TURJAF)
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TRD_20250125
dc.subjectMachine Learning
dc.subjectTransfer Learning
dc.subjectFeature Extraction
dc.subjectFish Freshness
dc.titleFish Freshness Detection Through Artificial Intelligence Approaches: A Comprehensive Study
dc.typeArticle

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