Scopus
YÖKSİS Eşleşti
Classification of Cicer arietinum varieties using MobileNetV2 and LSTM
European Food Research and Technology · Mayıs 2023
YÖKSİS Kayıtları
Classification of Cicer arietinum varieties using MobileNetV2 and LSTM
European Food Research and Technology (Springer Science and Business Media LLC) · 2023 SCI-Expanded
ARAŞTIRMA GÖREVLİSİ AHMET ERHARMAN →
Classification of Cicer arietinum varieties using MobileNetV2 and LSTM
European Food Research and Technology (Springer Science and Business Media LLC) · 2023 SCI-Expanded
DOÇENT ADEM GÖLCÜK →
Classification of Cicer arietinum varieties using MobileNetV2 and LSTM
European Food Research and Technology · 2023 SCI-Expanded
ARAŞTIRMA GÖREVLİSİ MÜCAHİD MUSTAFA SARITAŞ →
Classification of Cicer arietinum varieties using MobileNetV2 and LSTM
European Food Research and Technology · 2023 SCI-Expanded
ARAŞTIRMA GÖREVLİSİ AHMET ERHARMAN →
Classification of Cicer arietinum varieties using MobileNetV2 and LSTM
European Food Research and Technology · 2023 SCI-Expanded
DOÇENT ALİ YAŞAR →
Classification of Cicer arietinum varieties using MobileNetV2 and LSTM
European Food Research and Technology (Springer Science and Business Media LLC) · 2023 SCI-Expanded
ARAŞTIRMA GÖREVLİSİ MÜCAHİD MUSTAFA SARITAŞ →
Makale Bilgileri
DergiEuropean Food Research and Technology
Yayın TarihiMayıs 2023
Cilt / Sayfa249 · 1343-1350
Scopus ID2-s2.0-85148065708
Özet
Cicer arietinum is an important grain product in human nutrition with its high protein and high fiber content. In underdeveloped countries, people can meet the protein they need with cicer due to the difficulties in reaching meat products. Cicer productivity and usage purposes differ according to cicer varieties. Determining the appropriate seed variety is an important problem for agricultural producers. It is quite difficult to make a visual classification of varieties of cicer seeds because they are very similar to each other. In this study, two deep learning architectures using a computer vision system are proposed to overcome this problem. In the proposed architectures, there were 6 types of Cicer arietinum images whose input was obtained with this CV. The two proposed architectures are transfer learning in MobileNet-v2. In the first architecture, cicer images were classified by transfer learning with fine-tuning on pre-trained CNN (Convolutional Neural Network) models in MobileNet-v2. However, the second proposed architecture is hybrid as it includes a layer of Long Short Term Memory (LSTM) that also takes into account temporal features. In the classification of cicer varieties from cicer images, it is 92.3% in the first architecture and 92.97% in the second hybrid architecture. The results show that the proposed models achieve high success in classifying cicer images. This contributes to the studies in the literature with the high classification and deep architectural design of the study.
Yazarlar (4)
1
Adem Golcuk
ORCID: 0000-0002-6734-5906
2
Ali Yasar
3
Mucahid Mustafa Saritas
4
Ahmet Erharman
Anahtar Kelimeler
Cicer arietinum
Classification
Hybrid
LSTM
MobileNet-v2
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
8
Atıf
4
Yazar
5
Anahtar Kelime