The Use of Image-Spectroscopy Technology as a Diagnostic Method for Seed Health Testing and Variety Identification

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  • Martina Vrešak, Faculty of Agriculture and Life Sciences, Institute for Organic Farming, University of Maribor, Slovenia
  • Merete Halkjaer Olesen
  • ,
  • René Gislum
  • Franc Bavec, Faculty of Agriculture and Life Sciences, Institute for Organic Farming, University of Maribor, Slovenia
  • Johannes Ravn Jørgensen

Application of rapid and time-efficient health diagnostic and identification technology in the seed industry chain could accelerate required analysis, characteristic description and also ultimately availability of new desired varieties. The aim of the study was to evaluate the potential of multispectral imaging and single kernel near-infrared spectroscopy (SKNIR) for determination of seed health and variety separation of winter wheat (Triticum aestivum L.) and winter triticale (Triticosecale Wittm. & Camus). The analysis, carried out in autumn 2013 at AU-Flakkebjerg, Denmark, included nine winter triticale varieties and 27 wheat varieties provided by the Faculty of Agriculture and Life Sciences Maribor, Slovenia. Fusarium sp. and black point disease-infected parts of the seed surface could successfully be distinguished from uninfected parts with use of a multispectral imaging device (405-970 nm wavelengths). SKNIR was applied in this research to differentiate all 36 involved varieties based on spectral differences due to variation in the chemical composition. The study produced an interesting result of successful distinguishing between the infected and uninfected parts of the seed surface. Furthermore, the study was able to distinguish between varieties. Together these components could be used in further studies for the development of a sorting model by combining data from multispectral imaging and SKNIR for identifying disease(s) and varieties.

Original languageEnglish
JournalPLOS ONE
Volume11
Issue3
Pages (from-to)e0152011
ISSN1932-6203
DOIs
Publication statusPublished - 24 Mar 2016

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