Identification of durum wheat grains by using hybrid convolution neural network and deep features
Küçük Resim Yok
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer London Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Convolution neural network (CNN) is a deep learning technique widely used in object identification and classification. One of the objects that are identified and classified is grain products. We proposed a hybrid CNN model to identify the dataset obtained from 41 different durum wheat grains in the present study. A new deep feature set was created in the proposed model by combining Logits and Pool10 feature layers of the CNN models MobileNetV2 and SqueezeNet. This new feature set has been classified into the support vector machines (SVM) input. As a result of the experimental tests performed with the proposed hybrid model on the durum wheat data set, an accuracy rate of 91.89% was obtained. In addition, within the scope of this study, a unique durum wheat data set was publicly presented to researchers and added to the literature.
Açıklama
Anahtar Kelimeler
Convolution neural network (CNN), MobileNetV2, SqueezeNet, Deep features, Support vector machines (SVM)
Kaynak
Signal Image and Video Processing
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
16
Sayı
4
