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Analysis of morphometric and optical parameters of seeds of the subgenus cepa (Allium L., Alliaceae) by digital scanning

https://doi.org/10.26898/0370-8799-2022-2-3

Abstract

The results of the study of seed morphology from the subgenus Cepa: section Cepa (Mill.) Prokh. - Allium fistulosum L., A. altaiсum Pall., A. galanthum Kar. & Kir., A. oschaninii O. Fedtsch., A. pskemense B. Fedtsch.; section Schoenoprasum Dum. - A. altyncoliсum, A. ledebourianum, A. oliganthum, A. schoenoprasum L.; section Condensatum N. Friesen - A. condensatum are presented. Morphological characters of seeds can be used as additional taxonomic indicators in the identification and distinction of taxa within the subgenus Cepa of the genus Allium. The seeds were 2.74-3.50 mm long and 1.33-2.14 mm wide. The morphometric and optical parameters of seeds were measured by analyzing images using software. Digital images of seeds were obtained using an HP Scanjet 200 digital flatbed scanner, 600 dpi resolution, JPG file format. Morphometric and optical parameters of seeds were determined, including projection area (cm2), length, width, perimeter, mean size (mm), average feret diameter, factors of roundness, elongation, ellipse, indentation (relative units), parameters of brightness, tonality, color saturation (relative units). According to the results of the study, a series of distribution of species in descending order for each of the studied traits are formed. Within the Cepa section, A. pskemense seeds had the maximum linear size, perimeter, and cross-sectional area. Among the representatives of Schoenoprasum section, the maximum length of the seeds was found in A. altyncoliсum. Maximum width, perimeter, cross-sectional area, average feret diameter of the seeds were recorded in A. ledebourianum. In the Cepa section, the average RGB value in descending order was as follows: A. pskemense > A. galanthum > A. fistulosum > altaiсum > A. oschaninii. In the Schoenoprasum section this series has the form: A. schoenoprasum > A. ledebourianum > A. altyncoliсum > A. oliganthum.

About the Authors

F. B. Musaev
Federal Scientific Vegetable Cente
Russian Federation

Farkhad B. Musaev, Doctor of Science in Agriculture, Lead Researcher

143080

14, Selekzionnaya St.

VNIISSOK settl.

Odinzovo district

Moscow region



N. S. Priyatkin
Agrophysical Research Institute
Russian Federation

Nikolay S. Priyatkin, Candidate of Science in Engineering, Senior Researcher, Section Leader

Saint-Petersburg



M. I. Ivanova
All-Russian Research Institute of Vegetable Growing – branch of the Federal Scientific Vegetable Center
Russian Federation

Mariya I. Ivanova, Doctor of Science in Agriculture, Professor, Laboratory Head

Moscow region



A. F. Bukharov
All-Russian Research Institute of Vegetable Growing – branch of the Federal Scientific Vegetable Center
Russian Federation

Alexander F. Bukharov, Doctor of Science in Agriculture, Laboratory Head

Moscow region



A. I. Kashleva
All-Russian Research Institute of Vegetable Growing – branch of the Federal Scientific Vegetable Center
Russian Federation

Anna I. Kashleva, Candidate of Science in Agriculture, Senior Researcher

Moscow region



References

1. Ahmad I., Muhamin A., Naeem Islam M. Realtime specific weed recognition system using histogram analysis, Proc // World academy of science, engineering and technology. 2006. N 16. Р. 145–148.

2. Aitkenhead M. J., Dalgetty I. A., Mullins C. E., McDonald A. J. S., St. Rachan, N. J. C. Weed and crop discrimination using image analysis and artificial intelligence methods // Computers and Electronics in Agriculture. 2003. N 39. Р. 157–171.

3. Karcher D. E., Rechardson M. D. Quantifying turf grass color using digital image analysis // Crop Science. 2003. N 3. Р. 943–951.

4. Aldea M., Frank T. D., Delucia E. H. A method for quantitative analysis for spatially variable physiological processes across leaf surfaces // Photosynthesis Research. 2006. N 90. Р. 161–172.

5. Dana W., Ivo W. Computer image analysis of seed shape and seed color of flax cultivar description // Computers and Electronics in Agriculture. 2008. N 61. Р. 126–135.

6. Musaev F. B., Priyatkin N. S., Shchukina P. A., Ivanova M. I., Jafarov I. H., Nowar M. Geometrical parameters and colour index of chive (Allium schoenoprasum) seed // Research on Crops. 2020. Vol. 21. N 4. Р. 775–782.

7. Musaev F. B., Ivanova M. I., Prijatkin N. S., Kuznec S. V. Digital morphometry of onion seeds. Ovoshchi Rossii = Vegetable crops of Russia, 2021, no. 3, pp. 44–48. (In Russian).

8. Kapadia V. N., Sasidharan N., Patil К. Seed Image Analysis and Its Application in Seed Science Research // Advances in Biotechnology and Microbiology. 2017. Vol. 7. Iss. 2. Р. 1–3.

9. Tanabata T., Shibaya T., Hori K., Ebana K., Yano M. Smart Grain: high-throughput phenotyping software for measuring seed shape through image analysis // Plant physiology. 2012. N 4. Р. 1871–1880. DOI: 10.1104/pp.112.205120.

10. Whan A. P., Smith A. B., Cavanagh C. R., Ral J. P. F., Shaw L. M., Howitt C. A. Grain-Scan: a low cost, fast method for grain size and colour measurements // Plant Methods. 2014. N 10. Р. 1. URL: https://plantmethods.biomedcentral.com/articles/10.1186/1746-4811-10-23.

11. Bai X. D., Cao Z. G., Wang Y., Yu Z. H., Zhang X. F., Li C. N. Crop segmentation from images by morphology modeling in the CIE L*a*b color space // Computers and Electronics in Agriculture. 2013. N 99. Р. 21–34. DOI: 10.1016/j.compag.2013.08.022.

12. Zapotoczny P. Discrimination of wheat grain varieties using image analysis and neural networks, Part I, single kernel texture // Journal of Cereal Science. 2011. N 54. Р. 60–68. DOI: 10.1016/j.jcs.2011.02.012.

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

14. Huang M., Wang Q. G., Zhu Q. B., Qin J. W., Huang G. Review of seed quality and safety tests using optical sensing technologies // Seed Science and Technology. 2015. N 43. Р. 337–366. DOI: 10.15258/sst.2015.43.3.16.

15. Cervantes E., Martín J. J., Saadaoui E. Updated methods for seed shape analysis // Scientifica. 2016. N 42. P. 1569–1825. DOI: 10.1155/2016/5691825.

16. Roussel J., Geiger F., Fischbach A., Jahnke S., Scharr H. 3D surface reconstruction of plant seeds by volume carving: performance and accuracies // Frontiers in Plant Science. 2016. N 7. P. 745. DOI: 10.3389/fpls.2016.00745.

17. Strange H., Zwiggelaar R., Sturrock C., Mooney S. J., Doonan J. H. Automatic estimation of wheat grain morphometry from computed tomography data // Functional Plant Biology. 2015. N 42. Р. 452–459. DOI: 10.1071/FP14068.

18. Fritsch R. M., Blattner F. R., Gurushidze M. New classification of Allium L. subg. Melanocrommyum (Webb & Berthel) Rouy (Alliaceae) based on molecular and morphological characters // Phyton. 2010. N 49. Р. 145–220.

19. Ivanova M. I., Buharov A. F., Baleev D. N., Buharova A. R., Kashleva A. I., The biochemical composition of Allium L. leaves under the environmental conditions of the Moscow region. Dostizhenija nauki i tehniki APK = Achievements of Science and Technology of AIC, 2019, vol. 33, no. 5, рp. 47–50. (In Russian).

20. Bednorz L., Krzymińska A., Czarna A. Seed morphology and testa sculptures of some Allium L. species (Alliaceae) // Acta Agrobotanica. 2011. Vol. 64 (2). Р. 33–38.

21. Choi H. J., Giussani L. M., Jang C. G., Oh B. U., Cota-Sánchez J. Hugo. Systematics of disjunct northeastern Asian and northern North American Allium (Amaryllidaceae) // Botany. 2012. N 90 (6). Р. 491–508. DOI: 10.1139/b2012-031.

22. Lakin G. V. Biometrics: Moscow, Vysshaja shkola Publ., 1990, 352 p. (In Russian).

23. Kasajima I. Measuring plant colors // Plant Biotechnology. 2019. N 36. Р. 63–75. DOI: 10.5511/plantbiotechnology.19.0322a.


Review

For citations:


Musaev F.B., Priyatkin N.S., Ivanova M.I., Bukharov A.F., Kashleva A.I. Analysis of morphometric and optical parameters of seeds of the subgenus cepa (Allium L., Alliaceae) by digital scanning. Siberian Herald of Agricultural Science. 2022;52(2):22-31. https://doi.org/10.26898/0370-8799-2022-2-3

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ISSN 0370-8799 (Print)
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