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

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posted on 2023-02-07, 04:15 authored by Bikram Banerjee, Sameer Joshi, Emily Thoday-Kennedy, Raj K Pasam, Josquin TibbitsJosquin Tibbits, Matthew HaydenMatthew Hayden, German SpangenbergGerman Spangenberg, Surya KantSurya Kant
The 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.

History

Publication Date

2020-07-25

Journal

Journal of Experimental Botany

Volume

71

Issue

15

Pagination

12p. (p. 4604-4615)

Publisher

Oxford University Press

ISSN

0022-0957

Rights Statement

© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Experimental Biology. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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