Almond (Prunus dulcis) varieties classification with genetic designed lightweight CNN architecture
Yazarlar (3)
Arş. Gör. Mustafa Yurdakul Kirikkale Üniversitesi, Türkiye
Dr. Öğr. Üyesi İrfan Atabaş Kirikkale Üniversitesi, Türkiye
Prof. Dr. Şakir TAŞDEMİR Sinop 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 | Türkçe | Basım Tarihi | 05-2024 |
| Cilt / Sayı / Sayfa | 250 / 10 / 2625–2638 | DOI | 10.1007/s00217-024-04562-4 |
| Makale Linki | https://doi.org/10.1007/s00217-024-04562-4 | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| Almond (Prunus dulcis) is a nutritious food with a rich content. In addition to consuming as food, it is also used for various purposes in sectors such as medicine, cosmetics and bioenergy. With all these usages, almond has become a globally demanded product. Accurately determining almond variety is crucial for quality assessment and market value. Convolutional Neural Network (CNN) has a great performance in image classification. In this study, a public dataset containing images of four different almond varieties was created. Five well-known and light-weight CNN models (DenseNet121, EfficientNetB0, MobileNet, MobileNet V2, NASNetMobile) were used to classify almond images. Additionally, a model called 'Genetic CNN', which has its hyperparameters determined by Genetic Algorithm, was proposed. Among the well-known and light-weight CNN models, NASNetMobile achieved the most successful result … |
| Anahtar Kelimeler |
| Almond classification | Convolutional neural networks | Deep learning | Genetic algorithm | Optimization |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 22 |
| Scopus | 24 |
| Google Scholar | 37 |