Optimized feature selection using gray wolf and particle swarm algorithms for corn seed image classification
Yazarlar (8)
Dr. Öğr. Üyesi Elham Tahsın Yasın YASIN Torrens University Australia, Avustralya
Ewa Ropelewska
The National Institute of Horticultural Research, Polonya
The National Institute of Horticultural Research, Polonya
Öğr. Gör. Ramazan Kursun Selçuk Üniversitesi, Türkiye
Dr. Öğr. Üyesi Ilkay Cinar Selçuk Üniversitesi, Türkiye
Doç. Dr. Yavuz Selim Taspinar Selçuk Üniversitesi, Türkiye
Doç. Dr. Ali Yasar Selçuk Üniversitesi, Türkiye
Seyedali Mirjalili Selçuk Üniversitesi, Türkiye
Murat Koklu Selçuk Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Journal of Food Composition and Analysis (Q1) | ||
| Dergi ISSN | 0889-1575 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 09-2025 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-09-2025 |
| Cilt / Sayı / Sayfa | 145 / 1 / 107738– | DOI | 10.1016/j.jfca.2025.107738 |
| Makale Linki | https://doi.org/10.1016/j.jfca.2025.107738 | ||
| UAK Araştırma Alanları |
Bilgi Güvenliği ve Kriptoloji
Görüntü İşleme
Yapay Zeka
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| Özet |
| Corn, one of the agricultural products widely grown in the world, is an important nutrient for both humans and animals. Within the scope of this study, four corn cultivars (BT6470, Calipos, Es Armandi, and Hiva) licensed and produced by BIOTEK, were classified based on morphological, shape, and color features extracted from high-resolution RGB images. A dataset consisting of 14,469 individual seed images was constructed to support this classification task. A total of 106 features were extracted from each image and subsequently classified using three machine learning algorithms: Neural Network, Logistic Regression, and Random Forest. In the second stage, the Gray Wolf Optimizer (GWO) algorithm was applied to select and reduce the features to 44. In the third stage, 57 features were selected from the initial set using the Particle Swarm Optimization (PSO) algorithm. As a result, when the classification … |
| Anahtar Kelimeler |
| Corn | Machine learning | Classification | Feature selection | Optimization |
Science Direct
Optimized feature selection using gray wolf and particle swarm algorithms for corn seed image classification
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 6 |
| Scopus | 8 |
| Google Scholar | 11 |