Analysis of the variety of seeds quality Allium cristophii Trautv. with using digital morphometry

Relevance. Image analysis is an accessible method that can convert qualitative variables to quantitative variables. Computer imaging has been used in seed biology in a variety of ways, including testing emergence rate and identifying them. The paper examines the development in the field of computer image analysis that contribute to a better understanding of seed morphology in terms of their radial heterogeneity parameters: size, shape and color range. The size and shape of the seeds depends on the location of them in the inflorescence.

The aim of the work was measuring geometric indicators and analyzing the color characteristics of Allium cristophii seeds in the RGB system, due to the multi-tiered arrangement in the inflorescence.

Methods. TThe heterogeneous seeds A. cristophii Trautv were analyzed. From AllRussian Scientific Research Institute of Vegetable Growing biocollection – branch of Federal Scientific Vegetable Center. The morphometric and optical parameters of the seeds were measured by analyzing their images using the VideoTesT-Morphology software.

Results. Analysis of Christoph onion seeds heterogeneity showed that the length and width of the seeds from the lower tier were 3.301 and 2.681 mm, from the average – 3.295 and 2.605 mm and from the upper tier – 3.265 and 2.58 mm respectively. The average seed size from the lower tier was 2.99 mm, the average size was 2.95 mm and the lower tier was 2.92 mm. Statistically significant decrease of indicators over all color channels (according to RGB color model) from the lower tier - to the upper tier
was revealed. The tiered arrangement of flowers on the inflorescence is the cause of non-time maturation of Allium seeds. Operational ease, low cost commercial computer technology, and non- destructive seed analysis and sorting highlight the potential of this method for application in a seed laboratory.

1. Musayev F.B., Arkhipov M.V., Potrakhov N.N. X-ray based seed quality analysis of vegetable crops. Izvestiya of Timiryazev Agricultural Academy. 2014;(4):18-27. (In Russ.)

2. Bukharov A.F., Baleev D.N., Musaev F.B. Soft-ray radiography - effective method of identifying germlessness vegetable Umbrella cultures. Perm Agrarian Journal. 2015;(1(9):6-11. (In Russ.)

3. Priyatkin N.S., Arkhipov M.V., Gusakova L.P., Potrakhov N.N., Kropotov G.I., Tzibizov I.A., Vinerov I.A. Introsscopic methods of seed quality evaluation: state of problem and prospects of realization. Agrophysica. 2018;(2):29-39. (In Russ.)

4. Sandeep Varma V., Kanaka Durga K., Keshavulu K. Seed image analysis: its applications in seed science research. International Research Journal of Agricultural Sciences. 2013;1(2):30-36.

5. Kapadia V.N., Sasidharan N., Patil К. Seed Image Analysis and Its Application in Seed Science Research. Advances in Biotechnology and Microbiology. 2017;7(2):1-3.

6. Granitto, P.M., Verdes, P.F., and Ceccatto, H.A. Large-scale investigation of weed seed identification by machine vision. Comput. Electron. Agric. 2005;47;15–24. doi: 10.1016/j.compag.2004.10.003

7. Pourreza, A., Pourrezab, H., Abbaspour-Farda, M. H., and Sadrniaa, H. Identification of nine Iranian wheat seed varieties by textural analysis with image processing. Comput. Electron. Agric. 2012;83:102–108. doi: 10.1016/j.compag.2012.02.005

8. Tanabata, T., Shibaya, T., Hori, K., Ebana, K., and Yano, M. SmartGrain: highthroughput phenotyping software for measuring seed shape through image analysis. Plant Physiol. 2012;4:1871–1880. doi: 10.1104/pp.112.205120

9. Herridge, R.P., Day, R.C., Baldwin, S., and Macknight, R.C. Rapid analysis of seed size in Arabidopsis for mutant and QTL discovery. Plant Methods. 2011;7:3. doi: 10.1186/1746-4811-7-3

10. Whan, A.P., Smith, A.B., Cavanagh, C.R., Ral, J.P.F., Shaw, L.M., Howitt, C A., et al. GrainScan: a low cost, fast method for grain size and colour measurements. Plant Methods. 2014;10:1. doi: 10.1186/1746-4811-10-2310.4225/08/536302C43FC28

11. Bai, X.D., Cao, Z.G., Wang, Y., Yu, Z.H., Zhang, X.F., and Li, C.N. Crop segmentation from images by morphology modeling in the CIE L∗a∗b color space. Comput. Electron. Agric. 2013;99:21–34. doi: 10.1016/j.compag.2013.08.022

12. Wiesnerovб, D., and Wiesner, I. Computer image analysis of seed shape and seed color for flax cultivar description. Comput. Electron. Agric. 2008;61:126–135. doi: 10.1016/j.compag.2007.10.001

13. Chen, X., Xun, Y., Li, W., and Zhang, J. Combining discriminant analysis and neural networks for corn variety identification. Comput. Electron. Agric. 2010;71:48–53. doi: 10.1016/j.compag.2009.09.003

14. Zapotoczny, P. Discrimination of wheat grain varieties using image analysis and neural networks, Part I, single kernel texture. J. Cereal Sci. 2011;54:60–68. doi: 10.1016/j.jcs.2011.02.012

15. Novaro, P., Colucci, F., Venora, G., and D’egidio, M.G. Image analysis of whole grains: a noninvasive method to predict semolina yield in durum wheat. Cereal Chem. 2001;78:217–221. doi: 10.1094/CCHEM.2001.78.3.217

16. Tahir, A.R., Neethirajan, S., Jayas, D.S., Shahin, M.A., Symons, S.J., and White, N.D.G. Evaluation of the effect of moisture content on cereal grains by digital image analysis. Food Res. Int. 2007;40:1140–1145. doi: 10.1016/j.foodres.2007.06.009

17. Sapirstein, H.D., Neuman, M., Wright, E.H., Shwedyk, E., and Bushuk, W. An instrumental system for cereal grain classification using digital image analysis. J. Cereal Sci. 1987;6:3–14. doi: 10.1016/S0733-5210(87)80035-8

18. Miller, N.D., Haase, N.J., Lee, J., Kaeppler, S.M., de Leon, N., and Spalding, E.P. A robust, high-throughput method for computing maize ear, cob, and kernel attributes automatically from images. Plant J. 2016. doi: 10.1111/tpj.13320 [Epub ahead of print].

19. Sankaran, S., Wang, M., and Vandemark, G.J. Image-based rapid phenotyping of chickpeas seed size. Eng. Agric. Environ. Food. 2016;9:50–55. doi: 10.1016/j.eaef.2015.06.001

20. Huang, M., Wang, Q.G., Zhu, Q.B., Qin, J.W., and Huang, G. Review of seed quality and safety tests using optical sensing technologies. Seed Sci. Technol. 2015;43:337–366. doi: 10.15258/sst.2015.43.3.16

21. Williams, K., Munkvold, J., and Sorrells, M. Comparison of digital image analysis using elliptic fourier descriptors and major dimensions to phenotype seed shape in hexaploid wheat (Triticum aestivum L.). Euphytica. 2013;190:99–116. doi: 10.1007/s10681-012-0783-0

22. Cervantes, E., Martнn, J.J., and Saadaoui, E. Updated methods for seed shape analysis. Scientifica. 2016:5691825. doi: 10.1155/2016/5691825

23. Jahnke, S., Roussel, J., Hombach, T., Kochs, J., Fischbach, A., Huber, G., et al. phenoSeeder – a robot system for automated handling and phenotyping of individual seeds. Plant Physiol. 2016;172:1358–1370. doi: 10.1104/pp.16.01122

24. Roussel, J., Geiger, F., Fischbach, A., Jahnke, S., and Scharr, H. 3D surface reconstruction of plant seeds by volume carving: performance and accuracies. Front. Plant. Sci. 2016;7:745. doi: 10.3389/fpls.2016.00745

25. Strange, H., Zwiggelaar, R., Sturrock, C., Mooney, S.J., and Doonan, J.H. Automatic estimation of wheat grain morphometry from computed tomography data. Funct. Plant Biol. 2015;42:452–459. doi: 10.1071/FP14068

26. Musaev F.B., Soldatenko A.V., Baleev D.N., Priyatkin N.S., Shchukina P.A. Studies of vegetable seeds heterogeneity with use of computer image analysis. Agrophysica. 2019;(1):38-44. (In Russ.) DOI: 10.25695/AGRPH.2019.01.05

27. Musaev F.B., Pleitkin N.S., Arkhipov M.V., Schukina P.A., Bukharov A.F., Ivanova M.I. Digital morphometry of different quality seeds of vegetable crops. Potatoes and vegetables. 2018; (6): 35-37. (In Russ.)

28. Volkova G.A., Skupchenko L.A., Vokueva A.V., Skrotskaya O.V., Motorina N.A., Ryabinina M.L. Rare plant species in culture in the European North. Ekaterinburg: Ural Branch of the Russian Academy of Sciences. 2009. 154 p