High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response

Article

Banerjee, Bikram P., Joshi, Sameer, Thoday-Kennedy, Emily, Pasam, Raj K., Tibbits, Josquin, Hayden, Matthew, Spangenberg, German and Kant, Surya. 2020. "High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response." Journal of Experimental Botany. 71 (15), pp. 4604-4615. https://doi.org/10.1093/jxb/eraa143
Article Title

High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response

ERA Journal ID2604
Article CategoryArticle
AuthorsBanerjee, Bikram P., Joshi, Sameer, Thoday-Kennedy, Emily, Pasam, Raj K., Tibbits, Josquin, Hayden, Matthew, Spangenberg, German and Kant, Surya
Journal TitleJournal of Experimental Botany
Journal Citation71 (15), pp. 4604-4615
Number of Pages12
Year2020
PublisherOxford University Press
Place of PublicationUnited Kingdom
ISSN0022-0957
1460-2431
Digital Object Identifier (DOI)https://doi.org/10.1093/jxb/eraa143
Web Address (URL)https://academic.oup.com/jxb/article/71/15/4604/5809332
AbstractThe development of crop varieties with higher nitrogen use efficiency is crucial for sustainable crop production. Combining high-throughput genotyping and phenotyping will expedite the discovery of novel alleles for breeding crop varieties with higher nitrogen use efficiency. Digital and hyperspectral imaging techniques can efficiently evaluate the growth, biophysical, and biochemical performance of plant populations by quantifying canopy reflectance response. Here, these techniques were used to derive automated phenotyping of indicator biomarkers, biomass and chlorophyll levels, corresponding to different nitrogen levels. A detailed description of digital and hyperspectral imaging and the associated challenges and required considerations are provided, with application to delineate the nitrogen response in wheat. Computational approaches for spectrum calibration and rectification, plant area detection, and derivation of vegetation index analysis are presented. We developed a novel vegetation index with higher precision to estimate chlorophyll levels, underpinned by an image-processing algorithm that effectively removed background spectra. Digital shoot biomass and growth parameters were derived, enabling the efficient phenotyping of wheat plants at the vegetative stage, obviating the need for phenotyping until maturity. Overall, our results suggest value in the integration of high-throughput digital and spectral phenomics for rapid screening of large wheat populations for nitrogen response.
KeywordsBiomass; chlorophyll; image analysis; nitrogen use efficiency; vegetation indices; wheat
ANZSRC Field of Research 2020300406. Crop and pasture improvement (incl. selection and breeding)
310806. Plant physiology
Byline AffiliationsAgriculture Victoria
La Trobe University
University of Melbourne
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