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

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  • Martina Vrešak
  • Merete Halkjaer Olesen
  • René Gislum
  • Franc Bavec
  • Johannes Ravn Jørgensen

Abstract

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.

Suggested Citation

  • Martina Vrešak & Merete Halkjaer Olesen & René Gislum & Franc Bavec & Johannes Ravn Jørgensen, 2016. "The Use of Image-Spectroscopy Technology as a Diagnostic Method for Seed Health Testing and Variety Identification," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-10, March.
    • Handle: RePEc:plo:pone00:0152011
    DOI: 10.1371/journal.pone.0152011
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    Cited by:

    1. Carlos Henrique Queiroz Rego & Fabiano França-Silva & Francisco Guilhien Gomes-Junior & Maria Heloisa Duarte de Moraes & André Dantas de Medeiros & Clíssia Barboza da Silva, 2020. "Using Multispectral Imaging for Detecting Seed-Borne Fungi in Cowpea," Agriculture, MDPI, vol. 10(8), pages 1-12, August.
    2. Xingpeng Li & Hongzhe Jiang & Xuesong Jiang & Minghong Shi, 2021. "Identification of Geographical Origin of Chinese Chestnuts Using Hyperspectral Imaging with 1D-CNN Algorithm," Agriculture, MDPI, vol. 11(12), pages 1-19, December.
    3. Frédéric Kosmowski & Tigist Worku, 2018. "Evaluation of a miniaturized NIR spectrometer for cultivar identification: The case of barley, chickpea and sorghum in Ethiopia," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-17, March.
    4. Andrew Ogolla Egesa & Maria Teresa Davidson & Héctor E. Pérez & Kevin Begcy, 2024. "Biochemical and Physical Screening Using Optical Oxygen-Sensing and Multispectral Imaging in Sea Oats Seeds," Agriculture, MDPI, vol. 14(6), pages 1-16, May.
    5. Anders Krogh Mortensen & René Gislum & Johannes Ravn Jørgensen & Birte Boelt, 2021. "The Use of Multispectral Imaging and Single Seed and Bulk Near-Infrared Spectroscopy to Characterize Seed Covering Structures: Methods and Applications in Seed Testing and Research," Agriculture, MDPI, vol. 11(4), pages 1-18, April.

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