10.14489/td.2026.08.pp.071-082

2026, 08 August

DOI: 10.14489/td.2026.08.pp.071-082

Platov Yu. T., Lysenkova A. A., Rassulov V. A., Zelentsov S. V., Beletsky S. L., Platova R. A.
THE METHOD OF USING MICROFOCUS X-RAY TECHNIQUE FOR CLASSIFICATION OF SOYBEAN SEEDS BY QUALITY CATEGORIES
(pp. 71-82)

Abstract. Microfocus radiography, a non-destructive testing method that allows for the detection of obvious and hidden defects by visualizing the internal structure of soybean seeds, is underutilized. This article presents a methodology for using microfocus radiography data to classify soybean seeds into quality categories. A PRDU-02 radiographic system was used to obtain X-ray images of Irbis soybean seeds. The image processing algorithm included two approaches to identifying differences between defect-free and defective seeds based on pixel intensity in grayscale X-ray images. Principal component analysis and discriminant principal component analysis were used to construct models for classifying soybeans into quality categories. The two image analysis approaches yielded equivalent models for classifying soybeans into quality categories, indicating the interchangeability of these approaches to X-ray image analysis. The resulting model for classifying soybean seeds using X-ray images allows for the rapid, real-time, non-destructive testing of seed quality categories in accordance with the requirements of GOST 17109‒88 for food industry purposes.

Keywords: non-destructive methods, microfocus X-ray, soybean seeds, seed defects, classification model, principal component analysis, discriminant analysis.

+ - About the Authors Click to collapse

Yu. T. Platov, A. A. Lysenkova (Russian University of Economics, Moscow, Russia) E-mail: Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра. , Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра.
V. A. Rassulov (All-Russian Scientific-Research Institute of Mineral Resources named after N. M. Fedorovsky, Moscow, Russia) E-mail: Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра.
S. V. Zelentsov (V. S. Pustovoit All-Russian Research Institute of Oil Crops, Krasnodar, Russia) E-mail: Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра.
S. L. Beletsky (V. M. Gorbatov Federal Research Center for Food Systems of Russian Academy of Sciences, Moscow, Russia) E-mail: Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра.
R. A. Platova (Russian University of Economics, Moscow, Russia) E-mail: Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра.

+ - References Click to collapse

1. Liu, A., Cheng, S. S., Yung, W. S., et al. (2022). Genetic regulations of the oil and protein contents in soybean seeds and strategies for improvement. Advances in Botanical Research, 102, 259–293.
2. Zinsmeister, J., Leprince, O., & Buitink, J. (2020). Molecular and environmental factors regulating seed longevity. Biochem., 477(2), 305–323.
3. Zhao, G., Quan, L., Li, H., et al. (2021). Real time recognition system of soybean seed full surface defects based on deep learning. Computers and Electronics in Agriculture, 187, Article 106230.
4. Arif, M. A. R., Tripodi, P., Waheed, M. Q., et al. (2023). Genetic analyses of seed longevity in Capsicum annuum L. in cold storage conditions. Plants, 12(6), Article 1321.
5. Jiménez González, C., Agrasar, A. M. T., Mallo, F., et al. (2023). Red seaweed proteins: Valuable marine origin compounds with encouraging applications. Algal Research, 75, Article 103262.
6. Bueno, A. F., Colmenarez, Y. C., Carnevalli, R. A., & Sutil, W. P. (2023). Benefits and perspectives of adopting soybean IPM: The success of a Brazilian programme. Plant Health Cases, 1–16.
7. Prisyazhnaya, I. M., & Prisyazhnaya, S. P. (2023). Study of soya grain damage during harvesting and part time work. International Research Journal, (12), 160.
8. Jimoh, K. A., Hashim, N., Shamsudin, R., et al. (2023). Recent advances in the drying process of grains. Food Engineering Reviews, 15, 548–576.
9. Wang, F., Xi, Z., Yang, J., & Yang, X. (2013). Research progress of grain quality nondestructive testing methods. In Computer and Computing Technologies in Agriculture VI (CCTA 2012) (Vol. 393). IFIP Advances in Information and Communication Technology.
10. Bianchini, V. d. M., Mascarin, G. M., Silva, L. C. A. S., et al. (2021). Multispectral and X ray images for characterization of Jatropha curcas L. seed quality. Plant Methods, 17(9).
11. Porsch, F. (2020). Automated seed testing by 3D X ray computed tomography. Seed Science and Technology, 48(1).
12. Belan, P. A., de Macedo, R. A. G., Alves, W. A. L., et al. (2020). Machine vision system for quality inspection of beans. International Journal of Advanced Manufacturing Technology, 111, 3421–3435.
13. Liu, X., Zhang, Z., Li, Y., et al. (2025). Early stage detection of maize seed germination based on RGB image and machine vision. Smart Agricultural Technology, 11, Article 100927.
14. Tu, S., Wan, Z., Liang, G., et al. (2021). A novel approach to discriminate transgenic soybean seeds based on terahertz spectroscopy. Optik, 242, Article 167089.
15. Wang, L., Huang, Z., & Wang, R. (2021). Discrimination of cracked soybean seeds by near infrared spectroscopy and random forest variable selection. Infrared Physics & Technology, 115, Article 103731.
16. Belyakov, M. V., & Lysenkova, A. A. (2025). Varietal characteristics of soybean photoluminescence. Vestnik rossiyskoy sel'skokhozyaystvennoy nauki, (2), 12–16. [in Russian language].
17. ElMasry, G., ElGamal, R., Mandour, N., et al. (2020). Emerging thermal imaging techniques for seed quality evaluation: Principles and applications. Food Research International, 131, Article 109025.
18. Suresh, K., Kumari, S., Singh, S. K., et al. (2025). Electronic nose (e nose): Principle and advances for seed quality assessment. Food Control, Article 111291.
19. Cai, J. S., Zhu, Y. Y., Ma, R. H., et al. (2021). Effects of roasting level on physicochemical, sensory, and volatile profiles of soybeans using electronic nose and HS SPME GC–MS. Food Chemistry, 340, Article 127880.
20. Platov, Yu. T., Lysenkova, A. A., Zelentsov, S. V., et al. (2025). Non destructive testing for detecting hidden defects in soybean seeds using digital radiography and machine learning methods. Kontrol'. Diagnostika, 28(9), 43–54. [in Russian language].
21. Arkhipov, M. V., Priyatkin, N. S., Gusakova, L. P., et al. (2019). X ray computer methods for studying the structural integrity of seeds and their importance in modern seed science. Technical Physics, 64, 582–592.
22. Pinto, T. L. F., Cícero, S. M., França Neto, J. B., & Forti, V. A. (2009). An assessment of mechanical and stink bug damage in soybean seed using X ray analysis test. Seed Science and Technology, 37(1), 110–120.
23. Da Silva, D. D., de Freitas, E. D. G., Abud, H. F., & Gomes, D. G. (2024). Applying YOLOv8 and X ray morphology analysis to assess the vigor of Brachiaria brizantha cv. Xaraes seeds. Agriengineering, 6(2), 869–880.
24. De Medeiros, A. D., Zavala León, M. J., da Silva, L. J., et al. (2020). Relationship between internal morphology and physiological quality of pepper seeds during fruit maturation and storage. Agronomy Journal, 112, 25–35.
25. Kavelenova, L., Roguleva, N., Yankov, N., et al. (2021). Assessment of the quality of seeds formed in situ and ex situ as a mandatory element of maintaining seed banks of rare plants. In E3S Web of Conferences (APEEM 2021), 265.
26. Roguleva, N. O., Yankov, N. V., Zhavkina, T. M., et al. (2022). Radiographic express assessment of seed quality as a promising method in seed science of woody introduced species. Byulleten' Gosudarstvennogo Nikitskogo botanicheskogo sada, (144), 51–56. [in Russian language].
27. Pessoa, H. P., Copati, M. G. F., Azevedo, A. M., et al. (2023). Combining deep learning and X ray imaging technology to assess tomato seed quality. Scientia Agricola, 80, e20220121.
28. Arkhipov, M. V., Priyatkin, N. S., Gusakova, L. P., et al. (2020). Method of microfocal radiography for detecting hidden defects in seeds of forest tree species and other vascular plants. Zhurnal tekhnicheskoy fiziki, 90(2), 338–346. [in Russian language].
29. Sevastyanova, A. D., Bazarnova, Yu. G., & Priyatkin, N. S. (2024). Method of microfocal radiography for detecting hidden defects and assessing the quality of nuts of cultivated varieties. Vestnik Mezhdunarodnoy akademii kholoda, (2), 64–71. [in Russian language].
30. Hamdy, S., Charrier, A., Corre, L. L., et al. (2024). Toward robust and high throughput detection of seed defects in X ray images via deep learning. Plant Methods, 20(1), Article 63.
31. Chen, X., He, W., Ye, Z., et al. (2024). Soybean seed pest damage detection method based on spatial frequency domain imaging combined with RL SVM. Plant Methods, 20(1), Article 130.
32. Komyshev, E. G., Genaev, M. A., Busov, I. D., et al. (2023). Determination of the melanin and anthocyanin content in barley grains by digital image analysis using machine learning methods. Vavilovskii Zhurnal Genet Selektsii, 27(7), 859–868. [in Russian language].
33. Fordellone, M., Bellincontro, A., & Mencarelli, F. (2019). Partial least squares discriminant analysis: A dimensionality reduction method to classify hyperspectral data. Statistica Applicata: Italian Journal of Applied Statistics, 31(2). https://doi.org/10.26398/IJAS.0031-010
34. Morozova, M., Elizarova, T., & Pleteneva, T. (2013). Discriminant analysis and Mahalanobis distance (NIR diffuse reflectance spectra) in the assessment of drug’s batch to batch dispersion and quality threshold establishment. European Scientific Journal, *9*(27), 8–25.
35. Brereton, R. G. (2015). The Mahalanobis distance and its relationship to principal component scores. Journal of Chemometrics, 29(3), 143–145.
36. Rodionova, O. Y., & Pomerantsev, A. L. (2006). Chemometrics: Achievements and prospects. Russian Chemical Reviews, 75(4), 271–287.

+ - Purchase digital version of a single article Click to collapse

This article is available in electronic format (PDF).

The cost of a single article is 700 rubles. (including VAT 20%). After you place an order within a few days, you will receive following documents to your specified e-mail: account on payment and receipt to pay in the bank.

After depositing your payment on our bank account we send you file of the article by e-mail.

To order articles please copy the article doi:

10.14489/td.2026.08.pp.071-082

and fill out the form