Detection of fish freshness using artificial intelligence methods
Yazarlar (3)
Dr. Öğr. Üyesi Elham Tahsın Yasın YASIN Selçuk Üniversitesi, Türkiye
Doç. Dr. Ilker Ali Ozkan Selçuk Üniversitesi, Türkiye
Murat Koklu Selçuk Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | European Food Research and Technology (Q2) | ||
| Dergi ISSN | 1438-2377 Dergi Bilgileri (2023) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 08-2023 |
| Kabul Tarihi | 14-04-2023 | Yayınlanma Tarihi | 27-04-2023 |
| Cilt / Sayı / Sayfa | 249 / 8 / 1979–1990 | DOI | 10.1007/s00217-023-04271-4 |
| Makale Linki | https://doi.org/10.1007/s00217-023-04271-4 | ||
| UAK Araştırma Alanları |
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| Özet |
| Fish is commonly acknowledged as a highly nutritious food in many regions worldwide, and humans have been consuming fish for centuries to meet their protein and nutritional requirements. The consumption of fresh fish offers numerous benefits, as they contain essential proteins and materials that may be challenging to obtain from alternative sources. However, the freshness of fish decreases after a few days. Humans can determine the freshness of fish by looking at its eyes, smelling it, and checking its gills. But, can machines do the same? This study proposes a novel approach to evaluate the freshness of fish using deep learning techniques. Despite the long-standing tradition of humans determining fish freshness by sensory analysis, the objective evaluation of fish freshness has been challenging. By employing deep learning algorithms (SqueezeNet and InceptionV3) to classify fish based on their freshness … |
| Anahtar Kelimeler |
| Deep Learning | Machine Learning | Fish Freshness | Transfer Learning | Classification | Skin Coloration | Fish Body |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 38 |
| Scopus | 62 |
| Google Scholar | 84 |