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

Digital Phenotyping to Delineate Salinity Response in Safflower Genotypes

“…Our results show that overall, high-throughput image-based phenotyping techniques can be used to screen large germplasm populations, and identify candidate genotypes for further field evaluations. This protocol fits with previous research which has also demonstrated that early vegetative screens can provide insight into the performance and yield of germplasm at later stages (Krishnamurthy et al, 2007;Meng et al, 2017) or under stresses (Nguyen et al, 2019;Banerjee et al, 2020).…”

“…This suggests that ESB can be effectively used to estimate fresh and dry biomass, obviating the need for destructive harvesting. Biomass at vegetative stages, under abiotic stresses, have been shown to highly correlate to biomass production at maturity, illustrating that performance in vegetative screens is a good indicator of performance at yield (Nguyen et al, 2019;Banerjee et al, 2020). Biomass-based traits show high narrow-sense heritability, although they are controlled by additive gene effects, and strong links to yield performance, making them strong selection parameters in early vegetative screens (Moragues et al, 2006;Golkar, 2011;Yeilaghi et al, 2015).…”

Rice Transcriptome Analysis Reveals Nitrogen Starvation Modulates Differential Alternative Splicing and Transcript Usage in Various Metabolism-Related Genes

“…Therefore, by analyzing altered expression of various genes involved in N uptake, assimilation and remobilization along with studying the convergences of proteins and other metabolites during N starvation may provide more insights. Additionally, machine learning for highthroughput stress phenotyping tools will further advance our understandings of NUE trait in other cultivars along with rice [50]. S1: List of differentially expressed genes (DEGs), differentially alternative spliced (DAS) genes, differentially expressed transcripts (DETs), and differentially transcript usage (DTU) transcripts, in genotype, tissues and condition dependent comparative analysis, Table S2: Significant isoform switches among different samples, Table S3: Statistics of significant differentially expressed genes (DEGs), and differentially alternative spliced (DAS) genes, identified in genotype, tissues and condition dependent comparative analysis.…”

“…Therefore, by analyzing altered expression of various genes involved in N uptake, assimilation and remobilization along with studying the convergences of proteins and other metabolites during N starvation may provide more insights. Additionally, machine learning for high-throughput stress phenotyping tools will further advance our understandings of NUE trait in other cultivars along with rice [ 50 ].…”

Genomic Selection for Forest Tree Improvement: Methods, Achievements and Perspectives

“…An alternative method is HTP using various spectral cameras. These techniques can effectively evaluate the growth, biophysical, and biochemical performance of plants [115] and will help reduce the cost of phenotyping and improve breeding value prediction accuracy for forest trees.…”