Maize seeds forecasting with hybrid directional and bi‐directional long short‐term memory models
Yazarlar (8)
Doç. Dr. Hakan Isik Selçuk Üniversitesi, Türkiye
Prof. Dr. Şakir TAŞDEMİR Sinop Üniversitesi, Türkiye
Doç. Dr. Yavuz Selim Taspinar Selçuk Üniversitesi, Türkiye
Öğr. Gör. Ramazan Kursun Selçuk Üniversitesi, Türkiye
Dr. Öğr. Üyesi Ilkay Cinar Selçuk Üniversitesi, Türkiye
Doç. Dr. Ali Yasar Selçuk Üniversitesi, Türkiye
Murat Koklu Selçuk Üniversitesi, Türkiye
Makale Türü Açık Erişim Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
Dergi Adı Food Science and Nutrition (Q2)
Dergi ISSN 2048-7177 Dergi Bilgileri (2024)
Dergi Tarandığı Indeksler SCI-Expanded
Makale Dili İngilizce Basım Tarihi 02-2024
Kabul Tarihi 06-10-2023 Yayınlanma Tarihi 09-11-2023
Cilt / Sayı / Sayfa 12 / 2 / 786–803 DOI 10.1002/fsn3.3783
Makale Linki https://doi.org/10.1002/fsn3.3783
UAK Araştırma Alanları
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
Maize seeds forecasting with hybrid directional and bi‐directional long short‐term memory models

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