Classification of Turkish hazelnut (Corylus colurna L.) varieties: a comparative study of YOLOv8 and fine-tuned vision transformer
Yazarlar (4)
Dr. Öğr. Üyesi Yavuz ÜNAL Sinop Üniversitesi, Türkiye
Elham Tahsin Yasin
Selçuk Üniversitesi, Türkiye
Selçuk Üniversitesi, Türkiye
Talha Alperen Çengel Selçuk Üniversitesi, Türkiye
Doç. Dr. Murat Köklü Selçuk Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Journal of Food Measurement and Characterization (Q2) | ||
| Dergi ISSN | 2193-4126 Dergi Bilgileri (2026) | ||
| Dergi Tarandığı Indeksler | SCI | ||
| Makale Dili | Türkçe | Basım Tarihi | 09-2026 |
| Kabul Tarihi | 05-12-2025 | Yayınlanma Tarihi | 09-01-2026 |
| Cilt / Sayı / Sayfa | 20 / 4 / 4463–4477 | DOI | 10.1007/s11694-025-03936-w |
| Makale Linki | https://doi.org/10.1007/s11694-025-03936-w | ||
| UAK Araştırma Alanları |
Görüntü İşleme
Makine Öğrenmesi
Yapay Zeka
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| Özet |
| Hazelnut is a shrubby plant well-suited to the temperate and humid climate of the Black Sea region, particularly known for its mild winters. Turkey plays a critical role in global hazelnut production. Hazelnuts are not only an essential agricultural product for the country’s economy but are also highly valued for their nutritional content, being particularly rich in healthy oils and proteins, and their associated health benefits. The classification of different hazelnut species using modern deep learning techniques aimed to automatically distinguish between eight commonly cultivated hazelnut types: caklidak, damat, devedisi, sivri, karafindik, palaz, tombul, and yagli. This study employed both YOLOv8 classification models and Vision Transformer (ViT) with fine-tuning to classify a dataset comprising 2,722 labeled images of these hazelnut types. Various configurations of YOLOv8 models were tested, specifically YOLOv8n-cls … |
| Anahtar Kelimeler |
| Hazelnut species | Turkish hazelnut | Vision transformer | YOLOv8, fine tuning |
| Atıf Sayıları | |
| Google Scholar | 1 |