Maize seeds forecasting with hybrid directional and bi-directional long short-term memory models
Yazarlar (8)
Doç. Dr. Hakan Işık Selçuk Üniversitesi, Türkiye
Prof. Dr. Şakir TAŞDEMİR Sinop Üniversitesi, Türkiye
Doç. Dr. Yavuz Selim Taşpınar Selçuk Üniversitesi, Türkiye
Öğr. Gör. Ramazan Kurşun Selçuk Üniversitesi, Türkiye
Dr. Öğr. Üyesi İlkay Çınar Selçuk Üniversitesi, Türkiye
Doç. Dr. Ali Yaşar Selçuk Üniversitesi, Türkiye
Elham Tahsin Yasin
Selçuk Üniversitesi, Türkiye
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 | Food Science and Nutrition (Q2) | ||
| Dergi ISSN | 2048-7177 Dergi Bilgileri (2024) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 01-2024 |
| Cilt / Sayı / Sayfa | 12 / 2 / 786–803 | DOI | 10.1002/fsn3.3783 |
| Makale Linki | https://onlinelibrary.wiley.com/doi/10.1002/fsn3.3783 | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| The purity of the seeds is one of the important factors that increase the yield. For this reason, the classification of maize cultivars constitutes a significant problem. Within the scope of this study, six different classification models were designed to solve this problem. A special dataset was created to be used in the models designed for the study. The dataset contains a total of 14,469 images in four classes. Images belong to four different maize types, BT6470, CALIPOS, ES_ARMANDI, and HIVA, taken from the BIOTEK company. AlexNet and ResNet50 architectures, with the transfer learning method, were used in the models created for the image classification. In order to improve the classification success, LSTM (Directional Long Short‐Term Memory) and BiLSTM (Bi‐directional Long Short‐Term Memory) algorithms and AlexNet and ResNet50 architectures were hybridized. As a result of the classifications, the highest … |
| Anahtar Kelimeler |
| classification | forecasting | hybrid CNN | maize seeds | purification |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 16 |
| Scopus | 22 |
| Google Scholar | 39 |