Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-12-03T19:16:23.508Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  19 August 2015

Michael Adsetts Edberg Hansen ,
Fiona R. Hay  and
Jens Michael Carstensen
Michael Adsetts Edberg Hansen
Affiliation:
Videometer A/S, DK-2970 Hørsholm, Denmark
Fiona R. Hay*
Affiliation:
International Rice Research Institute, Los Baños, Laguna, Philippines
Jens Michael Carstensen
Affiliation:
Videometer A/S, DK-2970 Hørsholm, Denmark Technical University of Denmark, DK-2800 Lyngby, Denmark
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

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.

Keywords

genebank managementmultispectral image analysisriceseed phenotype
Type
Short Communications
Information
Plant Genetic Resources , Volume 14 , Issue 3 , September 2016 , pp. 238 - 241
Copyright
Copyright © NIAB 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Altman, NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician 46: 175–185.Google Scholar
Gonzalez, RC and Woods, RE (2007) Digital Image Processing. Prentice Hall, Upper Saddle River, New Jersey 07458.Google Scholar
Liu, C, Liu, W, Lu, X, Chen, W, Yang, J and Zheng, L (2014) Nondestructive determination of transgenic Bacillus thuringiensis rice seeds (Oryza sativa L.) using multispectral imaging and chemometric methods. Food Chemistry 153: 87–93.CrossRefGoogle ScholarPubMed
McNally, KL, Child, KL, Bohnert, R, Davidson, RM, Zhao, K, Ulat, VJ, Zeller, G, Clark, RM, Hoen, DR, Bureau, TE, Stokowski, R, Ballinger, DG, Frazer, KA, Cox, DR, Padhukasahasram, B, Bustamante, CD, Weigel, D, Mackill, DJ, Bruskiewich, RM, Rätsch, G, Buell, CR, Leung, H and Leach, JE (2009) Genomewide SNP variation reveals relationships among landraces and modern varieties of rice. Proceedings of the National Academy of Sciences 30: 12273–12278.CrossRefGoogle Scholar
Olesen, MH, Carstensen, JM and Boelt, B (2011) Multispectral imaging as a potential tool for seed health testing of spinach (Spinacia oleracea L.). Seed Science and Technology 39: 140–150.CrossRefGoogle Scholar
Olesen, MH, van Duijn, B and Boelt, B (2014) Introduction of new methods: spectral imaging. Seed Testing International 147: 10–13.Google Scholar
Olesen, MH, Nikneshan, P, Shrestha, S, Tadayyon, A, Deleuran, LC, Boelt, B and Gislum, R (2015) Viability prediction of Ricinus cummunis L. seeds using multispectral imaging. Sensors 15: 4592–4604.CrossRefGoogle ScholarPubMed
Shrestha, S, Deleuran, LC, Olesen, MH and Gislum, R (2015) Use of multispectral imaging in varietal identification of tomato. Sensors 15: 4496–4512.CrossRefGoogle ScholarPubMed