Integrating CBAM and Squeeze‐and‐Excitation Networks for Accurate Grapevine Leaf Disease Diagnosis
Yazarlar (1)
Dr. Öğr. Üyesi Yavuz ÜNAL Sinop Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Food Science and Nutrition (Q2) | ||
| Dergi ISSN | 2048-7177 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 06-2025 |
| Kabul Tarihi | 22-05-2025 | Yayınlanma Tarihi | 01-06-2025 |
| Cilt / Sayı / Sayfa | 13 / 6 / – | DOI | 10.1002/fsn3.70377 |
| Makale Linki | https://doi.org/10.1002/fsn3.70377 | ||
| UAK Araştırma Alanları |
Görüntü İşleme
Makine Öğrenmesi
Yapay Zeka
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| Özet |
| The vine plant holds significant importance beyond grape farming due to its diverse products. Various grape‐derived products, such as wine and molasses, highlight the vine plant's role as a valuable agricultural resource. Additionally, traditional cuisines around the world widely utilize grape leaves, contributing to their substantial economic value. However, diseases affecting grape leaves not only harm the plant and its yield but also render the leaves unsuitable for culinary use, leading to considerable economic losses for producers. Detecting diseases on grape leaves is a challenging and time‐consuming task when performed manually. Thus, developing a deep learning‐based model to automate the classification of grape leaf diseases is of critical importance. This study aims to classify the most common grape leaf diseases grape—scab (grape leaf blister mite) and downy mildew (grapevine downy mildew … |
| Anahtar Kelimeler |
| CBAM | deep learning | grapevine leaf disease | SENet |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 8 |
| Scopus | 15 |
| Google Scholar | 17 |