Fast Analysis of Maize Kernel Plumpness Characteristics Through Micro-CT Technology

  • Meng Shao
  • Ying Zhang
  • Jianjun Du
  • Xiaodi Pan
  • Liming Ma
  • Jinglu Wang
  • Dennis Böhmer
  • Xinyu GuoEmail author
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 545)


Maize plumpness believed to reflect yield and quality of maize products. Convenient and accurate methods may help identification of maize quality for produces and germplasm resources for breeding. In this study, the 3D reconstruction of maize kernel based on micro-CT technology was introduced to detect anatomical difference between diverse classes of maize kernel. Void spaces measurements constructed for whole maize (Zea mays L.) kernel gives a more accurate volume measurement for density calculations by means of a package of commercial softwares. Moreover, the ratio of cavity and porosity of the entire kernel were calculated based on the 3D CT images. Kernel density, cavities, porosity, and other phenotypic characteristics were closely related to seed plumpness classification. Compared with previous methods, our method significantly improves the calculation accuracy of kernel volume, cavity and porosity, and which is expected to be useful for efficient maize kernel plumpness classification.


Micro-CT Image segmentation Kernel plumpness Porosity Cavity 


  1. 1.
    Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. J. Mol. Biol. 147, 195–197 (1981)CrossRefGoogle Scholar
  2. 2.
    Lee, K.M., et al.: Corroborative study on maize quality, dry-milling and wet-milling properties of selected maize hybrids. J. Agric. Food Chem. 55(26), 10751–10763 (2007)CrossRefGoogle Scholar
  3. 3.
    Duarte, A.P., Mason, S.C., Jackson, D.S., Kiehl, J.D.C.: Grain quality of Brazilian maize genotypes as influenced by nitrogen level. Crop Sci. 45, 1958–1964 (2005)CrossRefGoogle Scholar
  4. 4.
    Dorsey-Redding, C., Crjr, H., Johnson, L.A., Fox, S.R.: Relationships among maize quality factors. Cereal Chem. 68, 602–605 (1992)Google Scholar
  5. 5.
    Mason, S.C., D’Croz-Mason, N.E.: Agronomic practices influence maize grain quality. J. Crop Prod. 5, 75–91 (2002)CrossRefGoogle Scholar
  6. 6.
    Jaeger, S.L., et al.: Influence of corn hybrid traits on digestibility and the efficiency of gain in feedlot cattle. J. Anim. Sci. 84(7), 1790–1800 (2006)CrossRefGoogle Scholar
  7. 7.
    Wu, Y.V., Bergquist, R.R.: Relation of corn density to yields and density of normal and opaque-2 maize grain as influenced by of dry milling products. Cereal Chem. 68, 542–544 (1991)Google Scholar
  8. 8.
    Hang, A., Obert, D., Gironella, A.I.N., Burton, C.S.: Barley amylose and β-glucan: their relationships to protein, agronomic traits, and environmental factors. Crop Sci. 47, 1754–1760 (2007)CrossRefGoogle Scholar
  9. 9.
    Federal Grain Inspection Service: Test weight per bushel apparatus. In: Grain Inspection Handbook. Book II. General Information, pp. 1–16. U.S. Department of Agriculture, Washington, DC (1988)Google Scholar
  10. 10.
    Pomeranz, Y., Hall, G.E., Czuchajowska, Z., Martin, C.R., Lai, F.S.: Test weight, hardness, and breakage susceptibility of yellow dent corn hybrids. Cereal Chem. 63, 349 (1986)Google Scholar
  11. 11.
    Zheng, Y.K., Wang, Z., Gu, Y.J.: Development and function of caryopsis transport tissues in maize, sorghum and wheat. Plant Cell Rep. 33, 1023–1031 (2014)CrossRefGoogle Scholar
  12. 12.
    Watson, S.A.: Structure and composition. In: Watson, S.A., Ramstad, P.E. (eds.) Corn Chemistry and Technology, pp. 53–82. American Association of Cereal Chemists, Inc., St. Paul (1987)Google Scholar
  13. 13.
    Robutti, J.L., Hoseney, R.C., Wassom, C.E.: Modified opaque-2 corn endosperms. ll. Structure viewed with a scanning electron microscope. Am. Assoc. Cereal Chem. 51, 173–180 (1974)Google Scholar
  14. 14.
    De Carvalho, M.L.M., Van Aelst, A.C., Van Eck, J.W., Hoekstra, F.A.: Pre-harvest stress cracks in maize (Zea mays L.) kernels as characterized by visual, X-ray and low temperature scanning electron microscopical analysis: effect on kernel quality. Seed Sci. Res. 9, 227–236 (1999)Google Scholar
  15. 15.
    Huber, K.C., BeMiller, J.N.: Channels of maize and sorghum starch granules. Carbohydr. Polym. 41(3), 269–276 (2000)CrossRefGoogle Scholar
  16. 16.
    Gustin, J.L., et al.: Analysis of maize (Zea mays) kernel density and volume using micro-computed tomography and single-kernel near infrared spectroscopy. J. Agric. Food Chem. 61, 10872–10880 (2013)CrossRefGoogle Scholar
  17. 17.
    Guelpa, A., Plessis, A.D., Kidd, M., Manley, M.: Non-destructive estimation of maize (Zea mays L.) kernel hardness by means of an X-ray micro-computed tomography (μCT) density calibration. Food Bioprocess Technol. 8, 1419–1429 (2015)CrossRefGoogle Scholar
  18. 18.
    Cnudde, V., Boone, M.: High-resolution X-ray computed tomography in geosciences: a review of the current technology and applications. Earth-Sci. Rev. 123, 1–17 (2013)CrossRefGoogle Scholar
  19. 19.
    Landis, E.N., Keane, D.T.: X-ray microtomography. Mater. Charact. 61, 1305–1316 (2010)CrossRefGoogle Scholar
  20. 20.
    Dhondt, S., Vanhaeren, H., Van Loo, D., Cnudde, V., Inze, D.: Plant structure visualization by high-resolution X-ray computed tomography. Trends Plant Sci. 15, 419–422 (2010)CrossRefGoogle Scholar
  21. 21.
    Blonder, B., De Carlo, F., Moore, J., Rivers, M., Enquist, B.J.: X-ray imaging of leaf venation networks. New Phytol. 196, 1274–1282 (2012)CrossRefGoogle Scholar
  22. 22.
    Xue, Y.L., et al.: Investigation of characteristic microstructures of wild ginseng by X-ray phase contrast microscopy. Acta Physica Sinica 59(8), 5496 (2010)Google Scholar
  23. 23.
    Dhondt, S., Vanhaeren, H., Van Loo, D., Cnudde, V., Inzé, D.: Plant structure visualization by high-resolution X-ray computed tomography. Trends Plant Sci. 15(8), 419–422 (2010)CrossRefGoogle Scholar
  24. 24.
    Stuppy, W.H., Maisano, J.A., Colbert, M.W., Rudall, P.J., Rowe, T.B.: Three-dimensional analysis of plant structure using high-resolution X-ray computed tomography. Trends Plant Sci. 8(1), 2–6 (2003)CrossRefGoogle Scholar
  25. 25.
    Dell’Aquila, A.: Development of novel techniques in conditioning, testing and sorting seed physiological quality. Seed Sci. Technol. 37(3), 608–624 (2009)CrossRefGoogle Scholar
  26. 26.
    Cloetens, P., Mache, S.M., Lerbs-Mache, S.: Quantitative phasetomography of arabidopsis seeds reveals intercellular void network. Proc. Natl. Acad. Sci. U.S.A. 103, 14626–14630 (2006)CrossRefGoogle Scholar
  27. 27.
    Verboven, P., et al.: Void space inside the developing seed of Brassica napus and the modelling of its function. New Phytol. 199(4), 936–947 (2013)CrossRefGoogle Scholar
  28. 28.
    Rousseau, D., et al.: Fast virtual histology using X-ray in-line phase tomography: application to the 3D anatomy of maize developing seeds. Plant Methods 11, 55 (2015)CrossRefGoogle Scholar
  29. 29.
    Lim, K.S., Barigou, M.: X-ray micro-computed tomography of cellular food products. Food Res. Int. 37, 1001–1012 (2004)CrossRefGoogle Scholar
  30. 30.
    Chawanji, A., Baldwin, A., Brisson, G., Webster, E.: Use of X-ray micro tomography to study the microstructure of loose-packed and compacted milk powders. J. Microsc. 248, 49–57 (2012)CrossRefGoogle Scholar
  31. 31.
    Donis-González, I.R., Guyer, D.E., Fulbright, D.W., Pease, A.: Postharvest noninvasive assessment of fresh chestnut (Castanea spp.) internal decay using computer tomography images. Postharvest Biol. Technol. 94, 14–25 (2014)CrossRefGoogle Scholar
  32. 32.
    Van Dalen, G., Nootenboom, P., Van Vliet, L.J.: 3D imaging and analysis of porous cereal products using X-ray microtomography. Image Anal. Stereol. 26(3), 169–177 (2007)CrossRefGoogle Scholar
  33. 33.
    Chaunier, L., Della Valle, G., Lourdin, D.: Relationships between texture, mechanical properties and structure of cornflakes. Food Res. Int. 40(4), 493–503 (2007)CrossRefGoogle Scholar
  34. 34.
    Babin, P., et al.: Fast X-ray tomography analysis of bubble growth and foam setting during breadmaking. J. Cereal Sci. 43, 393–397 (2006)CrossRefGoogle Scholar
  35. 35.
    Baker, D.R., et al.: An introduction to the application of X-ray microtomography to the three-dimensional study of igneous rocks. Lithos 148, 262–276 (2012)CrossRefGoogle Scholar
  36. 36.
    Guessasma, S., Hedjazi, L.: On the fragmentation of airy cereal products exhibiting a cellular structure: mechanical characterisation and 3D finite element computation. Food Res. Int. 49, 242–252 (2012)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Meng Shao
    • 1
    • 2
    • 3
  • Ying Zhang
    • 1
    • 2
    • 3
  • Jianjun Du
    • 1
    • 2
    • 3
  • Xiaodi Pan
    • 1
    • 2
    • 3
  • Liming Ma
    • 1
    • 2
    • 3
  • Jinglu Wang
    • 1
    • 2
    • 3
  • Dennis Böhmer
    • 4
  • Xinyu Guo
    • 1
    • 2
    • 3
    Email author
  1. 1.Beijing Key Lab of Digital PlantBeijingChina
  2. 2.Beijing Research Center for Information Technology in AgricultureBeijingChina
  3. 3.Beijing Academy of Agriculture and Forestry SciencesBeijingChina
  4. 4.University of BonnBonnGermany

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