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A virtual seed file: the use of multispectral image analysis in the management of genebank seed accessions

  • Michael Adsetts Edberg Hansen (a1), Fiona R. Hay (a2) and Jens Michael Carstensen (a1) (a3)
Abstract

We present a method for multispectral seed phenotyping as a fast and robust tool for managing genebank accessions. A multispectral vision system was used to take images of the seeds of 20 diverse varieties of rice (approximately 30 seeds for each variety). This was followed by extraction of feature information from the images. Multivariate analysis of the feature data was used to classify seed phenotypes according to accession. The proportion of correctly classified rice seeds was 93%. We conclude that the multispectral image analysis could play a role in comparing incoming seeds against existing accessions, identifying different seed types within a sample of seeds and/or in checking whether regenerated seeds match the original seeds.

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Corresponding author
*Corresponding author. E-mail: f.hay@irri.org
References
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AltmanNS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician 46: 175185.
GonzalezRC and WoodsRE (2007) Digital Image Processing. Prentice Hall, Upper Saddle River, New Jersey 07458.
LiuC, LiuW, LuX, ChenW, YangJ and ZhengL (2014) Nondestructive determination of transgenic Bacillus thuringiensis rice seeds (Oryza sativa L.) using multispectral imaging and chemometric methods. Food Chemistry 153: 8793.
McNallyKL, ChildKL, BohnertR, DavidsonRM, ZhaoK, UlatVJ, ZellerG, ClarkRM, HoenDR, BureauTE, StokowskiR, BallingerDG, FrazerKA, CoxDR, PadhukasahasramB, BustamanteCD, WeigelD, MackillDJ, BruskiewichRM, RätschG, BuellCR, LeungH and LeachJE (2009) Genomewide SNP variation reveals relationships among landraces and modern varieties of rice. Proceedings of the National Academy of Sciences 30: 1227312278.
OlesenMH, CarstensenJM and BoeltB (2011) Multispectral imaging as a potential tool for seed health testing of spinach (Spinacia oleracea L.). Seed Science and Technology 39: 140150.
OlesenMH, van DuijnB and BoeltB (2014) Introduction of new methods: spectral imaging. Seed Testing International 147: 1013.
OlesenMH, NikneshanP, ShresthaS, TadayyonA, DeleuranLC, BoeltB and GislumR (2015) Viability prediction of Ricinus cummunis L. seeds using multispectral imaging. Sensors 15: 45924604.
ShresthaS, DeleuranLC, OlesenMH and GislumR (2015) Use of multispectral imaging in varietal identification of tomato. Sensors 15: 44964512.
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Plant Genetic Resources
  • ISSN: 1479-2621
  • EISSN: 1479-263X
  • URL: /core/journals/plant-genetic-resources
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