Non-Destructive Method in Determining the Barley Grain Vitreousity

Авторы: Troshkin D.E., Chertov A.N., Gorbunova E.V., Meledina T.V., Sevastyanova L.V., Alekhin A.A. Опубликовано: 11.09.2021
Опубликовано в выпуске: #3(136)/2021  
DOI: 10.18698/0236-3933-2021-3-144-154

Раздел: Приборостроение, метрология и информационно-измерительные приборы и системы | Рубрика: Оптические и оптико-электронные приборы и комплексы  
Ключевые слова: machine vision, digital image, barley grain, vitreousity, uniformity

Possibilities of non-destructive express-evaluation of the barley grain vitreousity using machine vision and digital image processing methods were studied. The study was carried out with the proprietary design hardware and software complex on barley samples of three different varieties provided by the LLC "Nosters". Transmittance in the near IR wavelength range was used as the objective criterion in classifying grains as vitreous, partially vitreous and better use powdery. Classification group boundaries were determined empirically by the cross-section inspection method. The optimal filming mode was experimentally selected, and the algorithm for digital processing of grain images was developed in order to determine the number of better use powdery grains in a sample. In addition to classifying grains by vitreousity, the proposed approach also makes it possible to evaluate uniformity of a sample by this indicator and, thus, to identify a grain of higher quality. It was found out that grain orientation introduces an error of not more than 5 %, and high repeatability of the results and, as a consequence, accuracy of the algorithm are characterized by the variation coefficient of 1.1 %


[1] Repko N.V., Podolyak K.V., Smirnova E.V., et al. Condition of barley production in the Russian Federation. Nauchnyy zhurnal KubGAU [Scientific Journal of KubSAU], 2015, no. 106 (in Russ.). Available at: http://ej.kubagro.ru/2015/02/pdf/70.pdf

[2] Kulistikova T. Rosstat peresmotrel dannye po urozhayu--2019 [Rosstat revised data on the harvest--2019]. agroinvestor.ru: website. Available at: https://www.agroinvestor.ru/analytics/news/33304-rosstat-peresmotrel-dannye-po-urozhayu-2019 (accessed: 10.07.2020) (in Russ.).

[3] Bulgakov N.I. Biokhimiya soloda i piva [Biochemistry of malt and beer]. Moscow, Pishchevaya promyshlennost’ Publ., 1976.

[4] Rostovskaya M.F., Izvekova A.N., Izvekova N.N. Influence of malting parameters on the wheat malt quality. Pivo i napitki [Beer and Beverages], 2014, no. 4, pp. 54--56 (in Russ.).

[5] Polonskiy V.I., Sumina A.V. Barley corn water uptake related to its density. Vestnik KrasGAU [Bulletin of KSAU], 2011, no. 9, pp. 67--72 (in Russ.).

[6] Fu B.X., Wang K., Dupuis B., et al. Kernel vitreousness and protein content: relationship, interaction and synergistic effects on durum wheat quality. J. Cereal Sc., 2018, vol. 79, pp. 210--217. DOI: https://doi.org/10.1016/j.jcs.2017.09.003

[7] Sieber A.N., Wurschum T., Longin C.F.H. Vitreonsity, its stability and relationship to protein content in durum wheat. J. Cereal Sc., 2015, vol. 61, pp. 71--77. DOI: https://doi.org/10.1016/j.jcs.2014.10.008

[8] Polonskiy V.I., Sumina A.V. The assessment method of barley grain glassiness. Vestnik KrasGAU [Bulletin of KSAU], 2013, no. 3, pp. 33--36 (in Russ.).

[9] Rybalka A.I., Kopus’ M.M., Dontsov D.P. Modern tendencies of barley grain quality improvement. Agrarnyy vestnik Yugo-Vostoka [Agrarian Reporter of South-East], 2009, no. 3, pp. 18--21 (in Russ.).

[10] Vas’ko N.I., Kozachenko M.R., Solonechnyy P.N., et al. Endosperm vitreousness and protein content in grain of chaffy and naked barley cultivars. Zernobobovye i krupyanye kul’tury [Legumes and Groat Crops], 2018, no. 4, pp. 94--102 (in Russ.). DOI: https://doi.org/10.24411/2309-348X-2018-11056

[11] Zverev S.V., Pankrat’yeva I.A., Politukha O.V., et al. Glassiness as an indicator of the quality of wheat grain. Khranenie i pererabotka zerna, 2017, no. 11, pp. 33--34 (in Russ.).

[12] Chichti E., Carrere M., George M., et. al A wheat grain quantitative evaluation of vitreousness by light transmission analysis. J. Cereal Sc., 2018, vol. 83, pp. 58--62.

[13] Troshkin D.E., Gorbunova E.V., Alekhin A.A., et al. Quantitative assessment of wheat vitreous by the technical vision method. Khleboprodukty, 2019, no. 6, pp. 52--56 (in Russ.). DOI: https://doi.org/10.32462/0235-2508-2019-28-6-52-55

[14] Troshkin D., Chertov A., Gorbunova E., et al. A study of the influence of the orientation and arrangement features of wheat grains and their color on determination of the vitreousity. Proc. SPIE, 2019, vol. 11061. DOI: https://doi.org/10.1117/12.2526018

[15] Troshkin D.E., Gorbunova E.V., Chertov A.N., et al. Determination of wheat vitreonsity with machine vision in the near ir wavelength range. Izvestiya vysshikh uchebnykh zavedeniy. Priborostroenie [Journal of Instrument Engineering], 2020, vol. 63, no. 7, pp. 666--672 (in Russ.). DOI: https://doi.org/10.17586/0021-3454-2020-63-7-666-672

[16] Physical of defining grain quality: analysis of sources. Vestnik NGIEI [Bulletin NGIEI], 2013, no. 12, pp. 72--82 (in Russ.).

[17] Gonzalez R.C., Woods R.E., Eddins S.L. Digital image processing using MATLAB. Pearson Prentice Hall, 2004.

[18] Forsyth D.A., Ponce J. Computer vision: a modern approach. Prentice Hall, 2002.