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

• Published in: Food analytical methods Vol. 15; no. 6; pp. 1748 - 1760
• Main Authors: Sabanci, Kadir, Aslan, Muhammet Fatih, Ropelewska, Ewa, Unlersen, Muhammed Fahri, Durdu, Akif
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2022; Springer Nature B.V
• Subjects: Analytical Chemistry; Chemistry; Chemistry and Materials Science; Chemistry/Food Science; Damage detection; Deep learning; flour; Food Science; Hybrid structures; Image acquisition; Long short-term memory; Machine learning; Microbiology; neural networks; Pests; Transfer learning; Wheat; AlexNet; LSTM; BiLSTM; Transfer learning; Wheat classification; Sunn pest damaged wheat
• ISSN: 1936-9751; 1936-976X
• DOI: 10.1007/s12161-022-02251-0

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.