Classification of hazelnuts according to their quality using deep learning algorithms

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CZECH JOURNAL OF FOOD SCIENCES, vol.40, pp.240-248, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 40
  • Publication Date: 2022
  • Doi Number: 10.17221/21/2022-cjfs
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, CAB Abstracts, Compendex, Food Science & Technology Abstracts, Veterinary Science Database, Directory of Open Access Journals
  • Page Numbers: pp.240-248
  • Keywords: artificial intelligence, image processing, food quality, computer science, marketing, NEURAL-NETWORKS
  • Yozgat Bozok University Affiliated: Yes


Hazelnut is a product with high nutritional and economic value. In maintaining the quality value of hazelnut, the classification process is of great importance. In the present day, the quality classification of hazelnuts and other crops is performed in general manually, and so it is difficult and costly. Performing this classification with modern agricultural techniques is much more important in terms of quality. This study was based on a model intended to detect hazelnut quality. The model is about the establishment of an artificial intelligence-based classification system that can detect the hidden defects of hazelnuts. In the developed model, the visuals used in the dataset are divided into training and test groups. In the model, hazelnuts are divided into 5 classes according to their quality using AlexNet architecture and modern deep learning (DL) techniques instead of traditional hazelnut classification methods. In this model developed based on artificial intelligence, a very good approach was presented with the accurate classification of 99%. Moreover, the values regarding precision and recall were also determined at 98.7% and 99.6%, respectively. This study is important in terms of becoming widespread information technology use and computer-assisted applications in the agricultural economics field such as product classification, quality, and control.