Bread and durum wheat classification using wavelet based image fusion
Yükleniyor...
Dosyalar
Tarih
2020
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Wiley
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
BACKGROUND Wheat, which is an essential nutrient, is an important food source for human beings because it is used in flour and feed production. As in many nutrients, wheat plays an important role in macaroni and bread production. The types of wheat used for both foods are different, namely bread and durum wheat. A strong separation of these two wheat types is important for product quality. This article differs from the traditional methods available for the identification of bread and durum wheat species. In this study, ultraviolet (UV) and white light (WL) images of wheat are obtained for both species. Wheat types in these images are classified by various machine learning (ML) methods. Afterwards, these images are fused by wavelet-based image fusion method. RESULTS The highest accuracy value calculated using only UV and only WL image is 94.8276% and these accuracies are obtained by Support Vector Machine (SVM) and multilayer perceptron (MLP) algorithms, respectively. However, this accuracy value is 98.2759% for the fusion image and both MLP and SVM achieved the same success. CONCLUSION Wavelet-based fusion has increased the classification accuracy of all three learning algorithms. It is concluded that the identification ability in the resulting fusion image is higher than the other two raw images. (c) 2020 Society of Chemical Industry
Açıklama
WOS:000551460000001
PubMed ID: 32608512
Anahtar Kelimeler
Bread Wheat, Durum Wheat, Machine Learning, Wavelet-Based İmage Fusion
Kaynak
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
Sayı
Künye
Sabanci, K., Aslan, M. F.,Durdu, A. (2020). Bread and durum wheat classification using wavelet based image fusion. Journal of the Science of Food and Agriculture.
