Computerized visualization of seeds of Sepa subgenus (Allium L., Alliaceae – an effective tool to assess their quality

The authors presented the results of a study of the morphology of Allium seeds from the subgenus Cepa: Cepa section (Mill.) Prokh. A. fistulosum L., A. altaiсum Pall., A. galanthum Kar. & Kir., A. oschaninii O. Fedtsch., A. pskemense B. Fedtsch.; Schoenoprasum Dum. – A. altyncoliсum, A. ledebourianum, A. oliganthum, A. schoenoprasum L.; Condensatum N. Friesen – A. condensatum, from the biocollection of All-Russian Research Institute of Vegetable Growing – Branch of the Federal State Budgetary «Scientific Institution Federal Scientific Centre for Vegetable Growing») (Moscow region). Seeds were 2.74 – 3.50 mm long and 1.33 – 2.14 mm wide. The morphological characteristics of the seeds can be used as additional taxonomic indicators in the identification and classification of taxa within the Cepa subgenus of the genus Allium. The authors measured the morphometric and optical parameters of the seeds by image analysis using VideoTest-Morphology software developed at Argus Bio Ltd. (St. Petersburg). Seed digital images were obtained using an HP Scanjet 200 digital flatbed scanner, 600 dpi resolution, and JPG file format. Morphometric parameters of seeds were determined, including projection area (cm²), length, width, perimeter, average size (mm), average diameter Fere, factors of roundness, elongation, ellipse, rugosity (relative units), parameters of brightness, tonality, color saturation (relative units). Based on the results of the study, a series of distributions of species was compiled in descending order of each of the characters studied. Within the Cepa section, the seeds of A. pskemense had the maximum linear size, perimeter, and cross-sectional area. Within the Schoenoprasum section, the seeds of A. altyncolium had the maximum length. The maximum width, perimeter, cross-sectional area, and average diameter of Fere seeds were recorded for A. ledebourianum. In the Cepa section, the average RGB value in descending order was as follows: A. pskemense > A. galanthum > A. fistulosum > A. altaiсum > A. oschaninii. In the Schoenoprasum section, this series is as follows. A. schoenoprasum > A. ledebourianum > A. altyncoliсum > A. oliganthum.

1. Cope J.S., Corney D., Clark J.Y., Remagnino P., Wilkin P., Plant species identification using digital morphometrics: a review, Expert Syst. App., 2012, No. 39, рр. 7562–7573, DOI: 10.1016/j.eswa.2012.01.073.

2. Pinheiro F., De Barros F., Morphometric analysis of Epidendrum secundum (Orchidaceae) in southeastern Brazil, Nord. J. Bot., 2008, No. 25, рр. 129–136, DOI: 10.1111/j.0107- 055X.2007.00010.x.

3. Passardi F., Dobias J., Valério L., Guimil S., Penel C., Dunand C., Morphological and physiological traits of three major Arabidopsis thaliana accessions, J. Plant Physiol., 2007, No. 164, рр. 980–992, DOI: 10.1016/j.jplph.2006.06.008.

4. Vanderhoeven S., Hardy O., Vekemans X., Lefèbvre C., de Loose M., Lambinon J.A., Morphometric Study of Populations of the Centaurea jacea Complex (Asteraceae) in Belgium, Plant Biol., 2002, No. 4, рр. 403–412, DOI: 10.1055/s-2002-32327.

5. Chaloner W.G., McElwain J., The fossil plant record and global climatic change, Rev. Palaeobot. Palynol., 1997, No. 95, рр. 73–82, DOI: 10.1016/S0034-6667(96)00028-0.

6. Hemming J., Rath T., Computer-vision based weed identification under field condition using controlled lighting, J. Agric. Eng. Res., 2001, No. 78, рр. 233–243.

7. Ahmad I., Muhamin A., Naeem Islam M., Real-time specific weed recognition system using histogram analysis, Proc. World Acad. Sci. Eng. Technol., 2006, No. 16, рр. 145–148.

8. Tillet N.P., Hague T., Miles S.J. A field assessment of a potential method for weed and crop mapping geometry, Comput. Electron. Agric., 2001, No. 32, рр. 229–246.

9. 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, Comput. Electron. Agric., 2003, No. 39, рр. 157–171.

10. Karcher D.E., Rechardson M.D., Quantifying turf grass color using digital image analysis, Crop Sci., 2003, No. 43, рр. 943–951.

11. Aldea M., Frank T.D., Delucia E.H., A method for quantitative analysis for spatially variable physiological processes across leaf surfaces, Photosynth. Res., 2006, No. 90, рр. 161–172.

12. Dell’Aquila A., van der Shoor R., Jalink A., Application of chlorophyll fluorescence in sorting controlled deteriorated white cabbage (Brassica oleracea L.) seeds, Seed Science and Technology, 2002, No. 30, рр. 689–695.

13. Dell’Aquila A., Red-Green-Blue (RGB) colour density as a non-destructive marker in sorting deteriorated lentil (Lens culinaris Medik.) seeds, Seed Sci. & Technol., 2006, No. 34, рр. 609-619.

14. Dell’Aquila A., Application of a computer-aided image analysis system to evaluate seed germination under different environmental conditions, Ital. J. Agron., 2004, No. 8, рр. 51–62.

15. Dana W., Ivo W., Computer image analysis of seed shape and seed color of flax cultivar description, Comput. Electron. Agric., 2008, No. 61, рр. 126–135.

16. Honda H., Takikawa N., Noguchi H., Hanai T., Kobayashi T., Image analysis associated with a fuzzy neural network and estimation of shoot length of regenerated rice callus, J. Ferment. Bioeng., 1997, No. 84, рр. 342–347.

17. Berzin I., Mills D., Merchuk J.C., A non-destructive method for secondary metabolite determination in hairy root cultures, J. Chem. Eng. Jpn., 1999, No. 32, рр. 229–234.

18. Mahendra Prasad V.S.S., Dutta Gupta S., Trichromatic sorting of in vitro regenerated plants of gladiolus using adaptive resonance theory, Curr. Sci., 2004, No. 87, рр. 348–353.

19. Prasad V.S.S., Dutta Gupta S., Photometric clustering of regenerated plants of gladiolus by neural network and its biological validation, Comput. Electron. Agric., 2008, No. 60, рр. 8–17.

20. Yadav S.P., IbarakiY., Dutta Gupta S., Estimation of the chlorophyll content of micropropagated potato plants using RGB based image analysis, Plant Cell Tiss. Org. Cult., 2010, No. 100, рр. 183–188.

21. Dutta Gupta S., Ibaraki Y., Pattanayak A., Development of a digital image analysis method for real-time estimation of chlorophyll content in micropropagated potato plants, Plant Biotechnol. Rep., 2013, No. 7, рр. 91–97.

22. Musaev F.B., Arhipov M.V., Potrahov N.N., Izvestija Timirjazevskoj sel’skohozjajstvennoj akademii, 2014, No. 4, pp. 18–27. (In Russ.)

23. Buharov A.F., Baleev D.N., Musaev F.B., Permskij agrarnyj vestnik, 2015, No. 1 (9), pp. 6–11. (In Russ.)

24. Prijatkin N.S., Arhipov M.V., Gusakova L.P. [i dr.], Agrofizika, 2018, No. 2, pp. 29–39. (In Russ.)

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

26. Kapadia V.N., Sasidharan N., Patil K., Seed Image Analysis and Its Application in Seed Science Research, Advances in Biotechnology and Microbiology, 2017, Vol. 7, Iss. 2, рр. 1–3.

27. Granitto P.M., Verdes P.F., Ceccatto H.A., Large-scale investigation of weed seed identification by machine vision, Comput. Electron. Agric., 2005, No. 47, рр. 15–24, DOI: 10.1016/j.compag.2004.10.003.

28. Pourreza A., Pourrezab H., Abbaspour-Farda M.H., Sadrniaa H., Identification of nine Iranian wheat seed varieties by textural analysis with image processing, Comput. Electron. Agric., 2012, No. 83, рр. 102–108, DOI: 10.1016/j.compag.2012.02.005.

29. Tanabata T., Shibaya T., Hori K., Ebana K., Yano M., SmartGrain: high-throughput phenotyping software for measuring seed shape through image analysis, Plant Physiol., 2012, No. 4, рр. 1871–1880, DOI: 10.1104/pp.112.205120.

30. Herridge R.P., Day R.C., Baldwin S., Macknight R.C., Rapid analysis of seed size in Arabidopsis for mutant and QTL discovery, Plant Methods, 2011, No. 7, р. 3, DOI: 10.1186/1746-4811-7-3.

31. 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, No. 10, рp. 1, DOI: 10.1186/1746-4811-10-2310.4225/08/536302C43FC28.

32. 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, Comput. Electron. Agric., 2013, No. 99, рр. 21–34, DOI: 10.1016/j.compag.2013.08.022.

33. Wiesnerová D., Wiesner I., Computer image analysis of seed shape and seed color for flax cultivar description, Comput. Electron. Agric., 2008, No. 61, рр. 126–135, DOI: 10.1016/j.compag.2007.10.001.

34. Chen X., Xun Y., Li W., Zhang J., Combining discriminant analysis and neural networks for corn variety identification, Comput. Electron. Agric., 2010, No. 71, рр. 48–53, DOI: 10.1016/j.compag.2009.09.003.

35. Zapotoczny P., Discrimination of wheat grain varieties using image analysis and neural networks, Part I, single kernel texture, J. Cereal Sci., 2011, No. 54, рр. 60–68, DOI: 10.1016/j.jcs.2011.02.012.

36. Novaro P., Colucci F., Venora G., D’egidio M.G., Image analysis of whole grains: a noninvasive method to predict semolina yield in durum wheat, Cereal Chem., 2001, No. 78, рр. 217–221, DOI: 10.1094/CCHEM.2001.78.3.217.

37. Tahir A.R., Neethirajan S., Jayas D.S., Shahin M.A., Symons S.J., White N.D.G., Evaluation of the effect of moisture content on cereal grains by digital image analysis, Food Res. Int., 2007, No. 40, рр. 1140–1145, DOI: 10.1016/j.foodres.2007.06.009.

38. Sapirstein H.D., Neuman M., Wright E.H., Shwedyk E., Bushuk W., An instrumental system for cereal grain classification using digital image analysis, J. Cereal Sci., 1987, No. 6, рр. 3–14, DOI: 10.1016/S0733-5210(87)80035-8.

39. Miller N.D., Haase N.J., Lee J., Kaeppler S. M., de Leon N., Spalding E.P., A robust, highthroughput method for computing maize ear, cob, and kernel attributes automatically from images, Plant J., 2016, DOI: 10.1111/tpj.13320.

40. Sankaran S., Wang M., Vandemark G.J., Image-based rapid phenotyping of chickpeas seed size, Eng. Agric. Environ. Food, 2016, No. 9, рр. 50–55, DOI: 10.1016/j.eaef.2015.06.001.

41. 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 Sci. Technol., 2015, No. 43, рр. 337–366, DOI: 10.15258/sst.2015.43.3.16.

42. Williams K., Munkvold J., 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, No. 190, рр. 99–116, DOI: 10.1007/s10681-012-0783-0.

43. Cervantes E., Martín J.J., Saadaoui E., Updated methods for seed shape analysis, Scientifica, 2016, 5691825, DOI: 10.1155/2016/5691825.

44. 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, No. 172, рр. 1358–1370, DOI: 10.1104/pp.16.01122.

45. Roussel J., Geiger F., Fischbach A., Jahnke S., Scharr H., 3D surface reconstruction of plant seeds by volume carving: performance and accuracies, Front. Plant. Sci., 2016, No. 7, pp. 745, DOI: 10.3389/fpls.2016.00745.

46. Strange H., Zwiggelaar R., Sturrock C., Mooney S.J., Doonan J.H., Automatic estimation of wheat grain morphometry from computed tomography data, Funct. Plant Biol., 2015, No. 42, pp. 452–459, DOI: 10.1071/FP14068.

47. Friesen N., Fritsch R.M., Blattner F.R., Phylogeny and new infrageneric classification of Allium (Alliaceae) based on nuclear ribosomal DNA ITS sequences, Aliso, 2006, No. 22, рр. 372–395.

48. Neshati F., Fritsch R.M., Seed characters and testa sculptures of some Iranian Allium L. species (Alliaceae), Feddes Repert, 2009, No. 120, рр. 322–332.

49. 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, No. 49, рр. 145–220.

50. Fritsch R.M., Abbasi M., Taxonomic Review of Allium subg. Melanocrommyum in Iran, Germany, Leibniz-Institut für Pflanzengenetik und Kulturpflanzenforschung Gatersleben, 2013.

51. Ivanova M.I., Buharov A.F., Baleev D.N., Buharova A.R., Kashleva A.I., Seredin T.M., Razin O.A., Dostizhenija nauki i tehniki APK, 2019, T. 33, No. 5, pp. 47–50, DOI: 10.24411/0235-2451-2019-10511. (In Russ.)

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

53. Musaev F.B., Soldatenko A.V. [i dr.], Agrofizika, 2019, No. 1, рp. 38–44. (In Russ.)

54. Musaev F.B., Prijatkin N.S., Arhipov M.V. [i dr.], Kartofel’ i ovoshhi, 2018, No. 6, pp. 35–37. (In Russ.)

55. Choi Hyeok Jae, Giussani Liliana M., Jang Chang Gee, Oh Byoung Un, Cota-Sánchez J. Hugo., Systematics of disjunct northeastern Asian and northern North AmericanAllium (Amaryllidaceae), Botany, 2012, No. 90 (6), рр. 491–508, DOI:10.1139/b2012-031.

56. Lazcano-Ramírez H.G., Gómez-Felipe A., Díaz-Ramírez D., Durán-Medina Y., Sánchez-Segura L., de Folter S., Marsch-Martínez N., Non-destructive Plant Morphometric and Color Analyses Using an Optoelectronic 3D Color Microscope, Front. Plant Sci., 2018, No. 9, pp. 1409, DOI: 10.3389/fpls.2018.01409.

57. Kasajima I., Measuring plant colors, Plant Biotechnology, 2019, No. 36, рр. 63–75, DOI: 10.5511/plantbiotechnology.19.0322a.