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

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 appl...