Varietal identification of soybeans by statistical parameters of light absorption
https://doi.org/10.30766/2072-9081.2025.26.6.1422-1430
Abstract
The statistical parameters of the effective light absorption spectra of soybean seeds of various cultivars and ripeness groups were studied. The purpose of the research was to identify the varietal features and to develop an algorithm for varietal identification of soybeans according to the statistical parameters of absorption spectra during photoluminescence excitation. Spectral absorption characteristics were obtained using a CM2203 diffraction spectrofluorimeter in the wavelength range of λ = 230–600 nm. The statistical parameters of the spectra were calculated – mathematical expectation, variance, skewness and kurtosis. Effective light absorption during photoluminescence excitation occurs in the range from 300 nm to 550 nm with the main maxima at 420nm, 390 and 362 nm. Radiation absorption is caused by the presence of phenolic acids, carotenoids, riboflavin, as well as terpenoids, sporopollenin, lipofuscin, lignin, or flavonoids. The mathematical expectation and variance are determined with a relatively small relative error – no more than 1.2 and 7.6 %, respectively, and the errors in determining skewness and kurtosis can reach 16.1–22 %. By the magnitude of the asymmetry, the ‘Barguzin’ cultivar can be uniquely identified. The remaining studied cultivars can be identified with varying probability by all four statistical parameters. The ‘Vilana’ and ‘Vilana beta’ cultivars differ significantly in terms of variance. An algorithm for varietal identification based on the probabilistic identity of soybeans to known cultivars has been developed and a database of statistical parameters of soybean cultivars has been obtained. To increase accuracy, a combination of statistical and previously obtained integral absorption parameters is possible. In the future it is planned to increase the number of identified cultivars, clarify the ranges and develop an optoelectronic soybean varietal identification system.
Keywords
Supporting agencies: the research was carried out under the support of the Ministry of Science and Higher Education of the Russian Federation within the state assignment of the Federal Scientific Agroengineering Center VIM (theme No. FGUN-2025-0007).
About the Author
M. V. Belyakov
Federal Scientific Agroengineering Center VIM
Russian Federation
Russian Federation
Mikhail V. Belyakov, DSc in Engineering, chief researcher
1st Institutsky proezd, 5, Moscow, 109428
References
1. Nowacka M., Trusinska M., Chraniuk P., Drudi F., Lukasiewicz J., Nguyen N. P., Przybyszewska A. et al. Developments in Plant Proteins Production for Meat and Fish Analogues. Molecules. 2023;28(7):2966. DOI: https://doi.org/10.3390/molecules28072966
2. Veklenko V. I., Eremenko O. V. Forecasting of global and russian soybean exports. Vestnik Kurskoy gosudarstvennoy selskokhozyaystvennoy akademii = Vestnik of Kursk State Agricultural Academy. 2023;(9):222–228. (In Russ.). URL: https://www.elibrary.ru/item.asp?id=60016416
3. Lobachevskiy Ya. P., Dorokhov A. S. Digital technologies and robotic devices in the agriculture. Selskokhozyaystvennie mashini i tekhnologii = Agricultural Machinery and Technologies. 2021;15(4):6–10. (In Russ.). DOI: https://doi.org/10.22314/2073-7599-2021-15-4-6-10
4. Li H., Zhang L., Sun H., Rao Z., Ji H. Identification of soybean varieties based on hyperspectral imaging technology and one-dimensional convolutional neural network. Journal of Food Process Engineering. 2021;44(8):e13767. DOI: https://doi.org/10.1111/jfpe.13767
5. Ramazanova S. A., Savichenko V. G., Ustarkhanova E. G., Loginova E. D., Ramazanov R. N., Guchetl A. Kh. Search of the new SSR-loci of DNA for development of effective technology for soybean genotyping. Maslichnie kulturi. Nauchno-tekhnichesky byulleten Vserossiyskogo nauchno-issledovatelskogo instituta maslichnikh kultur = Oil crops. Scientific and technical Bulletin of VNIIMK. 2021;(4(188)):18–24. (In Russ.). DOI: https://doi.org/10.25230/2412-608X-2021-4-188-18-24
6. Bondarenko O. N., Ivany A. A., Penzin A. A. The experience of using the certification system in establishing the varietal affiliation of soybeans. Vestnik Chuvashskogo gosudarstvennogo agrarnogo universiteta = Vestnik Chuvash State Agrarian University. 2025;(1(32)):7–14. (In Russ.). DOI: https://doi.org/10.48612/vch/xxxa-gr7g-5kf7
7. Song Q., Liu A., Zhang S., Li R., Qiao S., He P. Quantum Dot Nanobead-Based Fluorescence-Linked Immunosorbent Assay for Detection of Glycinin in Soybeans and Soy Products. Molecules. 2022;27:3664. DOI: https://doi.org/10.3390/molecules27123664
8. Ravi R., Taheri A., Khandekar D., Millas R. Rapid Profiling of Soybean Aromatic Compounds Using Electronic Nose. Biosensors. 2019;9(2):66. DOI: https://doi.org/10.3390/bios9020066
9. Zubareva K. Yu., Belozerova A. V., Khrikina T. A. Assessment of physiological state of soybean plants by photometric diagnostics methods. Zernobobovie i krupyanie kulturi = Legumes and Groat Crops. 2024;(1(49)):28–36. (In Russ.). DOI: https://doi.org/10.24412/2309-348X-2024-1-28-36
10. Aleksandrova V. A., Lisenkova A. A. Identification of soybean variety by optical spectroscopy methods. Tovaroved prodovolstvennikh tovarov = Food Products Commodity Expert. 2025;2(247):84–87. (In Russ.). DOI: https://doi.org/10.33920/igt-01-2502-04
11. Ma R., Wang J., Zhao W., Guo H., Dai D., Yun Y. et al. Identification of Maize Seed Varieties Using MobileNetV2 with Improved Attention Mechanism CBAM. Agriculture. 2023;13(1):11. DOI: https://doi.org/10.3390/agriculture13010011
12. Singh T., Garg N. M., Iyengar S. R. S. Nondestructive identification of barley seeds variety using near-infrared hyperspectral imaging coupled with convolutional neural network. Journal of Food Process Engineering. 2021;44(10):e13821. DOI: https://doi.org/10.1111/jfpe.13821
13. Bu Y., Jiang X., Tian J., Hu X., Han L., Huang D., Luo H. Rapid nondestructive detecting of sorghum varieties based on hyperspectral imaging and convolutional neural network. Journal of Science Food and Agriculture. 2023;103(8):3970–3983. DOI: https://doi.org/10.1002/jsfa.12344
14. Sun J., Zhang L., Zhou X., Yao K., Tian Y., Nirere A. A method of information fusion for identification of rice seed varieties based on hyperspectral imaging technology. Journal of Food Process Engineering. 2021;44(9):e13797. DOI: https://doi.org/10.1111/jfpe.13797
15. Zhao X., Que H., Sun X., Zhu Q., Huang M. Hybrid convolutional network based on hyperspectral imaging for wheat seed varieties classification. Infrared Physics & Technology. 2022;125:104270. DOI: https://doi.org/10.1016/j.infrared.2022.104270
16. Zelentsov S. V., Moshnenko E. V., Bubnova L. A., Budnikov E. N., Trunova M. V., Ramazanova S. A. A Middleearly shade tolerant soybean cultivar Vilana beta. Maslichnie kulturi. Nauchno-tekhnichesky byulleten Vserossiyskogo nauchno-issledovatelskogo instituta maslichnikh kultur = Oil crops. Scientific and technical Bulletin of VNIIMK. 2020;(1(181)):140–146. (In Russ.). DOI: https://doi.org/10.25230/2412-608X-2020-1-181-140-146
17. Belyakov M. V. Optical luminescent properties of agricultural plants’ seeds. Agrofizika = Agrophysica. 2024;(4):72–78. (In Russ.). DOI: https://doi.org/10.25695/AGRPH.2024.04.09
18. Pan Y.-L. Detection and characterization of biological and other organic-carbon aerosol particles in atmosphere using fluorescence. Journal of Quantitative Spectroscopy and Radiative Transfer. 2015;150:12–35. DOI: https://doi.org/10.1016/j.jqsrt.2014.06.007
19. Matveeva T. A., Sarimov R. M., Persidskaya O. K., Andreevskaya V. M., Semenova N. A., Gudkov S. V. Application of Fluorescence Spectroscopy for Early Detection of Fungal Infection of Winter Wheat Grains. AgriEngineering. 2024;6(3):3137–3158. DOI: https://doi.org/10.3390/agriengineering6030179
20. Donaldson L. Autofluorescence in Plants. Molecules. 2020;25(10):2393. DOI: https://doi.org/10.3390/molecules25102393
21. Belyakov M. V., Lisenkova A. A. Sortovie osobennosti foto-lyuminestsentsii soi. Vestnik rossiyskoy selskokhozyaystvennoy nauki = Vestnik of the Russian agricultural science. 2025;(2):12–16. (In Russ.). DOI: https://doi.org/10.31857/S2500208225020032
Review
For citations:
Belyakov M.V. Varietal identification of soybeans by statistical parameters of light absorption. Agricultural Science Euro-North-East. 2025;26(6):1422-1430. (In Russ.) https://doi.org/10.30766/2072-9081.2025.26.6.1422-1430
Views:
11
Email this article 