Scopus
🔓 Açık Erişim YÖKSİS Eşleşti
Maize seeds forecasting with hybrid directional and bi-directional long short-term memory models
Food Science and Nutrition · Şubat 2024
YÖKSİS Kayıtları
Maize seeds forecasting with hybrid directional and bi-directional long short-term memory models
FOOD SCIENCE & NUTRITION · 2024 SCI-Expanded
PROFESÖR HAKAN IŞIK →
Maize Seeds Forecasting with Hybrid Directional and Bi-Directional Long Short Term Memory Models
Food Science & Nutrition · 2024 SCI-Expanded
DOKTOR ÖĞRETİM ÜYESİ İLKAY ÇINAR →
Maize Seeds Forecasting with Hybrid Directional and Bi-Directional Long Short Term Memory Models
Food Science & Nutrition · 2024 SCI-Expanded
DOÇENT YAVUZ SELİM TAŞPINAR →
Maize Seeds Forecasting with Hybrid Directional and Bi-Directional Long Short Term Memory Models
Food Science & Nutrition · 2024 SCI-Expanded
DOÇENT MURAT KÖKLÜ →
Maize Seeds Forecasting with Hybrid Directional and Bi-Directional Long Short Term Memory Models
Food Science & Nutrition · 2024 SCI-Expanded
ÖĞRETİM GÖREVLİSİ RAMAZAN KURŞUN →
Maize seeds forecasting with hybrid directional and bi-directional long short-term memory models
FOOD SCIENCE & NUTRITION · 2024 SCI-Expanded
PROFESÖR ŞAKİR TAŞDEMİR →
Maize seeds forecasting with hybrid directional and bi-directional long short-term memory models
FOOD SCIENCE & NUTRITION · 2024 SCI-Expanded
DOÇENT ALİ YAŞAR →
Makale Bilgileri
DergiFood Science and Nutrition
Yayın TarihiŞubat 2024
Cilt / Sayfa12 · 786-803
Scopus ID2-s2.0-85176259257
Erişim🔓 Açık Erişim
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 classification success was obtained from the ResNet50+BiLSTM model with 98.10%.
Yazarlar (8)
1
Hakan Işik
2
Şakir Taşdemir
3
Yavuz Selim Taspinar
ORCID: 0000-0002-7278-4241
4
Ramazan Kursun
ORCID: 0000-0002-6729-1055
5
Ilkay Cinar
ORCID: 0000-0003-0611-3316
6
Ali Yasar
7
Elham Tahsin Yasin
ORCID: 0000-0003-3246-6000
8
Murat Koklu
ORCID: 0000-0002-2737-2360
Anahtar Kelimeler
classification
forecasting
hybrid CNN
maize seeds
purification
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
4
Atıf
8
Yazar
5
Anahtar Kelime