Nondestructive Detection of Hazelnut Defects Using X-ray Imaging and Deep Learning-Based Segmentation

Sultan Murat Yılmaz; Serap Çakar Kaman; Erkan Güler

doi:10.15832/ankutbd.1719208

Nondestructive Detection of Hazelnut Defects Using X-ray Imaging and Deep Learning-Based Segmentation

Abstract

The hazelnut (Corylus avellana L.) is a strategic agricultural product of high economic and nutritional value. Turkiye is the world leader in hazelnut production and export. The quality of hazelnuts is critically essential for international competitiveness and economic returns. The most common method for determining quality in the literature and industry is calculating the percentage of hazelnut kernels based on the ratio of sound kernels. However, this method relies on human observation, which can lead to errors and inconsistencies. In particular, this observation-based method sometimes fails to detect defects in the hazelnut kernel. Recently, nondestructive quality determination methods, particularly X-ray imaging technology, have allowed for the quick and accurate detection of internal and external defects in food products. However, the hazelnut industry has only recently started to adopt these technologies and deep learning–based computer vision methods. Significant advantages in terms of accuracy and efficiency for analyzing the quality of hazelnuts, recognizing objects, and detecting defects are offered by nondestructive food processing technologies and artificial intelligence-based approaches. This study created and made publicly available a unique dataset of X-ray images for defect detection in hazelnut kernels. Defect detection was performed using deep learning–based object detection algorithms, including YOLOv5, YOLOv8, Faster R CNN, and SSD, whereas segmentation was achieved using K-means clustering, yielding successful results. Consequently, with joint usage of defect detection and segmentation, the developed method provides a more objective, accurate, and reliable process for assessing hazelnut quality.

Keywords

Supporting Institution

This study was supported within the scope of the Giresun University Scientific Research Projects Coordination Unit project numbered FEN-BAP-A-110225-24.

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Details

Primary Language

English

Subjects

Artificial Intelligence (Other), Precision Agriculture Technologies, Post Harvest Horticultural Technologies (Incl. Transportation and Storage), Basic Food Processes, Sustainable Agricultural Development

Journal Section

Research Article

Authors

Sultan Murat Yılmaz ^*
0000-0001-6819-6669
Türkiye

Serap Çakar Kaman
0000-0002-3682-0831
Türkiye

Erkan Güler
0000-0001-7225-0859
Türkiye

Publication Date

July 28, 2026

Submission Date

June 13, 2025

Acceptance Date

February 28, 2026

Published in Issue

Year 2026 Volume: 32 Number: 3

DOI

https://doi.org/10.15832/ankutbd.1719208

IZ

https://izlik.org/JA78TW68TC

Cite

RIS / Bibtex

APA

Yılmaz, S. M., Çakar Kaman, S., & Güler, E. (2026). Nondestructive Detection of Hazelnut Defects Using X-ray Imaging and Deep Learning-Based Segmentation. Journal of Agricultural Sciences, 32(3), 718-736. https://doi.org/10.15832/ankutbd.1719208

AMA

1.Yılmaz SM, Çakar Kaman S, Güler E. Nondestructive Detection of Hazelnut Defects Using X-ray Imaging and Deep Learning-Based Segmentation. J Agr Sci-Tarim Bili. 2026;32(3):718-736. doi:10.15832/ankutbd.1719208

Chicago

Yılmaz, Sultan Murat, Serap Çakar Kaman, and Erkan Güler. 2026. “Nondestructive Detection of Hazelnut Defects Using X-Ray Imaging and Deep Learning-Based Segmentation”. Journal of Agricultural Sciences 32 (3): 718-36. https://doi.org/10.15832/ankutbd.1719208.

EndNote

Yılmaz SM, Çakar Kaman S, Güler E (July 1, 2026) Nondestructive Detection of Hazelnut Defects Using X-ray Imaging and Deep Learning-Based Segmentation. Journal of Agricultural Sciences 32 3 718–736.

IEEE

[1]S. M. Yılmaz, S. Çakar Kaman, and E. Güler, “Nondestructive Detection of Hazelnut Defects Using X-ray Imaging and Deep Learning-Based Segmentation”, J Agr Sci-Tarim Bili, vol. 32, no. 3, pp. 718–736, July 2026, doi: 10.15832/ankutbd.1719208.

ISNAD

Yılmaz, Sultan Murat - Çakar Kaman, Serap - Güler, Erkan. “Nondestructive Detection of Hazelnut Defects Using X-Ray Imaging and Deep Learning-Based Segmentation”. Journal of Agricultural Sciences 32/3 (July 1, 2026): 718-736. https://doi.org/10.15832/ankutbd.1719208.

JAMA

1.Yılmaz SM, Çakar Kaman S, Güler E. Nondestructive Detection of Hazelnut Defects Using X-ray Imaging and Deep Learning-Based Segmentation. J Agr Sci-Tarim Bili. 2026;32:718–736.

MLA

Yılmaz, Sultan Murat, et al. “Nondestructive Detection of Hazelnut Defects Using X-Ray Imaging and Deep Learning-Based Segmentation”. Journal of Agricultural Sciences, vol. 32, no. 3, July 2026, pp. 718-36, doi:10.15832/ankutbd.1719208.

Vancouver

1.Sultan Murat Yılmaz, Serap Çakar Kaman, Erkan Güler. Nondestructive Detection of Hazelnut Defects Using X-ray Imaging and Deep Learning-Based Segmentation. J Agr Sci-Tarim Bili. 2026 Jul. 1;32(3):718-36. doi:10.15832/ankutbd.1719208