Prediction of fish quality level with machine learning
Yazarlar (2)
Emre Yavuzer Kırşehir Ahi Evran Üniversitesi, Türkiye
Dr. Öğr. Üyesi Memduh KÖSE Kırşehir Ahi Evran Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | International Journal of Food Science and Technology (Q2) | ||
| Dergi ISSN | 0950-5423 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 08-2022 |
| Cilt / Sayı / Sayfa | 57 / 8 / 5250–5255 | DOI | 10.1111/ijfs.15853 |
| Makale Linki | http://dx.doi.org/10.1111/ijfs.15853 | ||
| Özet |
| In this study, sea bream, sea bass, anchovy and trout were captured and recorded using a digital camera during refrigerated storage for 7 days. In addition, their total viable counts (TVC) were determined on a daily basis. Based on the TVC, each fish was classified as ‘fresh’ when it was <5 log cfu per g, and as ‘not fresh’ when it was >7 log cfu per g. They were uploaded on a web-based machine learning software called Teachable Machine (TM), which was trained about the pupils and heads of the fish. In addition, images of each species from different angles were uploaded to the software in order to ensure the recognition of fish species by TM. The data of the study indicated that the TM was able to distinguish fish species with high accuracy rates and achieved over 86% success in estimating the freshness of the fish species tested. |
| Anahtar Kelimeler |
| Food identification | fresh fish | machine learning | quality changes | teachable machine |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 18 |
| SCOPUS | 24 |
| Google Scholar | 28 |