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 |
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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s12161-022-02251-0.pdf Until 2030-01-01 | 2.72 MB | Adobe PDF | View/Open Request a copy |
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