Fisheye freshness detection using common deep learning algorithms and machine learning methods with a developed mobile application
Yazarlar (3)
Muslume Beyza Yildiz
Selçuk Üniversitesi, Türkiye
Selçuk Üniversitesi, Türkiye
Dr. Öğr. Üyesi Elham Tahsın Yasın YASIN 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 (2024) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 07-2024 |
| Kabul Tarihi | 03-02-2024 | Yayınlanma Tarihi | 18-04-2024 |
| Cilt / Sayı / Sayfa | 250 / 7 / 1919–1932 | DOI | 10.1007/s00217-024-04493-0 |
| Makale Linki | https://doi.org/10.1007/s00217-024-04493-0 | ||
| UAK Araştırma Alanları |
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| Özet |
| Fish is commonly ingested as a source of protein and essential nutrients for humans. To fully benefit from the proteins and substances in fish it is crucial to ensure its freshness. If fish is stored for an extended period, its freshness deteriorates. Determining the freshness of fish can be done by examining its eyes, smell, skin, and gills. In this study, artificial intelligence techniques are employed to assess fish freshness. The author’s objective is to evaluate the freshness of fish by analyzing its eye characteristics. To achieve this, we have developed a combination of deep and machine learning models that accurately classify the freshness of fish. Furthermore, an application that utilizes both deep learning and machine learning, to instantly detect the freshness of any given fish sample was created. Two deep learning algorithms (SqueezeNet, and VGG19) were implemented to extract features from image data. Additionally … |
| Anahtar Kelimeler |
| Classification | Deep Learning | Feature Extraction | Fisheye | Fish Freshness | Machine Learning |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 29 |
| Scopus | 44 |
| Google Scholar | 70 |