Fish Freshness Detection Through Artificial Intelligence Approaches: A Comprehensive Study
Fish 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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Hasan Eleroğlu
2024-02-01
|
| Series: | Turkish Journal of Agriculture: Food Science and Technology |
| Subjects: | |
| Online Access: | https://www.agrifoodscience.com/index.php/TURJAF/article/view/6670 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850161663619104768 |
|---|---|
| author | Sabire Kılıçarslan Meliha Merve Hız Çiçekliyurt Serhat Kılıçarslan |
| author_facet | Sabire Kılıçarslan Meliha Merve Hız Çiçekliyurt Serhat Kılıçarslan |
| author_sort | Sabire Kılıçarslan |
| collection | DOAJ |
| description | Fish 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. |
| format | Article |
| id | doaj-art-ff30963f395c4367a010cee7839bc76e |
| institution | OA Journals |
| issn | 2148-127X |
| language | English |
| publishDate | 2024-02-01 |
| publisher | Hasan Eleroğlu |
| record_format | Article |
| series | Turkish Journal of Agriculture: Food Science and Technology |
| spelling | doaj-art-ff30963f395c4367a010cee7839bc76e2025-08-20T02:22:45ZengHasan EleroğluTurkish Journal of Agriculture: Food Science and Technology2148-127X2024-02-0112229029510.24925/turjaf.v12i2.290-295.66705371Fish Freshness Detection Through Artificial Intelligence Approaches: A Comprehensive StudySabire Kılıçarslan0https://orcid.org/0009-0007-9299-7141Meliha Merve Hız Çiçekliyurt1https://orcid.org/0000-0003-4303-9717Serhat Kılıçarslan2https://orcid.org/0000-0001-9483-4425Çanakkale Onsekiz Mart University, Graduate School of Sciences, Department of Medical System BiologyÇanakkale Onsekiz Mart University, Faculty of Medicine, Department of Medical Medical BiologyBandirma Onyedi Eylül University, Faculty of Engineering, Department of Software EngineeringFish 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.https://www.agrifoodscience.com/index.php/TURJAF/article/view/6670machine learningtransfer learningfeature extractionfish freshness |
| spellingShingle | Sabire Kılıçarslan Meliha Merve Hız Çiçekliyurt Serhat Kılıçarslan Fish Freshness Detection Through Artificial Intelligence Approaches: A Comprehensive Study Turkish Journal of Agriculture: Food Science and Technology machine learning transfer learning feature extraction fish freshness |
| title | Fish Freshness Detection Through Artificial Intelligence Approaches: A Comprehensive Study |
| title_full | Fish Freshness Detection Through Artificial Intelligence Approaches: A Comprehensive Study |
| title_fullStr | Fish Freshness Detection Through Artificial Intelligence Approaches: A Comprehensive Study |
| title_full_unstemmed | Fish Freshness Detection Through Artificial Intelligence Approaches: A Comprehensive Study |
| title_short | Fish Freshness Detection Through Artificial Intelligence Approaches: A Comprehensive Study |
| title_sort | fish freshness detection through artificial intelligence approaches a comprehensive study |
| topic | machine learning transfer learning feature extraction fish freshness |
| url | https://www.agrifoodscience.com/index.php/TURJAF/article/view/6670 |
| work_keys_str_mv | AT sabirekılıcarslan fishfreshnessdetectionthroughartificialintelligenceapproachesacomprehensivestudy AT melihamervehızcicekliyurt fishfreshnessdetectionthroughartificialintelligenceapproachesacomprehensivestudy AT serhatkılıcarslan fishfreshnessdetectionthroughartificialintelligenceapproachesacomprehensivestudy |