High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response. (18th March 2020)

• Record Type: Journal Article
• Authors: Banerjee, Bikram P; Joshi, Sameer; Thoday-Kennedy, Emily; Pasam, Raj K; Tibbits, Josquin; Hayden, Matthew; Spangenberg, German; Kant, Surya
• Editors: Takahashi, Hideki
• Is Part Of: Journal of experimental botany. Volume 73:Number 15(2022)
• Page Start: 4604
• Page End: 4615
• Publication Date: 2020-03-18
• Subjects: Biomass -- chlorophyll -- image analysis -- nitrogen use efficiency -- vegetation indices -- wheat; Botany -- Periodicals; Botany, Experimental -- Periodicals; Plant physiology -- Periodicals; 580
• DOI: 10.1093/jxb/eraa143
• Languages: English
• ISSNs: 0022-0957

Abstract

Abstract : A novel vegetation index was derived to estimate chlorophyll and was shown to be effective for high-throughput phenotyping of wheat germplasm for nitrogen response. Abstract: 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.