Ensemble Residual Network Features and Cubic-SVM Based Tomato Leaves Disease Classification System
Yükleniyor...
Dosyalar
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
International Information and Engineering Technology Association
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The need for automatic disease detection applications that can help farmers in the detection of agricultural product diseases is increasing day by day. Convolutional Neural Network (CNN) is a very popular field in image processing, recognition, and classification. It is seen that CNN architectures are used in the determination of agricultural products. In this study, 3 different ResNet architectures of the features automatically are used in the detection of tomato diseases. The most efficient features obtained from these architectures have been obtained by the NCA algorithm again. The features obtained have been trained with the Cubic SVM machine learning algorithm. Tomato leaves belonging to a total of 10 classes have been trained at 80% and a test performance rate of 98.2% has been achieved.
Açıklama
Anahtar Kelimeler
Deep Learning, NCA, Residual Network, Tomato Leaf Disease
Kaynak
Traitement du Signal
WoS Q Değeri
Q3
Scopus Q Değeri
Cilt
39
Sayı
1
Künye
Özyurt, F., Sert, E., & Avci, D. (2022). Ensemble Residual Network Features and Cubic-SVM Based Tomato Leaves Disease Classification System. Traitement du Signal, 39(1) 71-77.
