Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/2431
Title: A Novel Convolutional-Recurrent Hybrid Network for Sunn Pest-Damaged Wheat Grain Detection
Authors: Sabancı, Kadir
Aslan, Muhammet Fatih
Ropelewska, Ewa
Ünlerşen, Muhammed Fahri
Durdu, Akif
Keywords: AlexNet
LSTM
BiLSTM
Sunn pest damaged wheat
Transfer learning
Wheat classification
Durum-Wheat
Classification
Proteinase
Hemiptera
Quality
Issue Date: 2022
Publisher: Springer
Abstract: The sunn pest-damaged (SPD) wheat grains negatively affect the flour quality and cause yield loss. This study focuses on the detection of SPD wheat grains using deep learning. With the created image acquisition mechanism, healthy and SPD wheat grains are displayed. Image preprocessing steps are applied to the captured raw images, then data augmentation is performed. The augmented image data is given as an input to two different deep learning architectures. In the first architecture, transfer learning application is made using AlexNet. The second architecture is a hybrid structure, obtained by adding the bidirectional long short-term memory (BiLSTM) layer to the first architecture. In terms of accuracy, the performance of the non-hybrid and hybrid architectures that are presented in the study is determined as 98.50% and 99.50%, respectively. High classification success and innovative deep learning structure are the features of this study that distinguish it from previous studies.
URI: https://doi.org/10.1007/s12161-022-02251-0
https://hdl.handle.net/20.500.13091/2431
ISSN: 1936-9751
1936-976X
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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