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A Novel Convolutional-Recurrent Hybrid Network for Sunn Pest–Damaged Wheat Grain Detection

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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.

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Authors and Affiliations

  1. Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, 70100, Karaman, Turkey

    Kadir Sabanci & Muhammet Fatih Aslan

  2. Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100, Skierniewice, Poland

    Ewa Ropelewska

  3. Electrical and Electronics Engineering, Necmetin Erbakan University, 42100, Konya, Turkey

    Muhammed Fahri Unlersen

  4. Electrical and Electronics Engineering, Konya Technical University, 42100, Konya, Turkey

    Akif Durdu

Authors
  1. Kadir Sabanci
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  2. Muhammet Fatih Aslan
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  3. Ewa Ropelewska
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  4. Muhammed Fahri Unlersen
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  5. Akif Durdu
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Contributions

Kadir Sabanci: investigation, methodology, writing — reviewing and editing. Muhammet Fatih Aslan: investigation and supervision, editing. Ewa Ropelewska: investigation and supervision. Muhammed Fahri Unlersen: methodology and investigation. Akif Durdu: methodology and supervision.

Corresponding author

Correspondence to Kadir Sabanci.

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Conflict of Interest

Kadir Sabanci declares no conflict of interest. Muhammet Fatih Aslan declares no conflict of interest. Ewa Ropelewska declares no conflict of interest. Muhammed Fahri Unlersen declares no conflict of interest. Akif Durdu declares no conflict of interest.

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Sabanci, K., Aslan, M.F., Ropelewska, E. et al. A Novel Convolutional-Recurrent Hybrid Network for Sunn Pest–Damaged Wheat Grain Detection. Food Anal. Methods 15, 1748–1760 (2022). https://doi.org/10.1007/s12161-022-02251-0

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  • DOI: https://doi.org/10.1007/s12161-022-02251-0

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