A UAV‐based high‐throughput phenotyping approach to assess time‐series nitrogen responses and identify trait‐associated genetic components in maize

Abstract

Advancements in the use of genome‐wide markers have provided unprecedented opportunities for dissecting the genetic components that control phenotypic trait variation. However, cost‐effectively characterizing agronomically important phenotypic traits on a large scale remains a bottleneck. Unmanned aerial vehicle (UAV)‐based high‐throughput phenotyping has recently become a prominent method, as it allows large numbers of plants to be analyzed in a time‐series manner. In this experiment, 233 inbred lines from the maize (Zea mays L.) diversity panel were grown in the field under different nitrogen treatments. Unmanned aerial vehicle images were collected during different plant developmental stages throughout the growing season. A workflow for extracting plot‐level images, filtering images to remove nonfoliage elements, and calculating canopy coverage and greenness ratings based on vegetation indices (VIs) was developed. After applying the workflow, about 100,000 plot‐level image clips were obtained for 12 different time points. High correlations were detected between VIs and ground truth physiological and yield‐related traits. The genome‐wide association study was performed, resulting in n = 29 unique genomic regions associated with image extracted traits from two or more of the 12 total time points. A candidate gene Zm00001d031997, a maize homolog of the Arabidopsis HCF244 (high chlorophyll fluorescence 244), located underneath the leading single nucleotide polymorphisms of the canopy coverage associated signals were repeatedly detected under both nitrogen conditions. The plot‐level time‐series phenotypic data and the trait‐associated genes provide great opportunities to advance plant science and to facilitate plant breeding.

Association analyses of host genetics, root-colonizing microbes, and plant phenotypes under different nitrogen conditions in maize

“…3,313 rhizosphere samples from 230 replicated genotypes of the maize diversity panel ( Flint-Garcia et al, 2005 ) were collected from field experiments conducted under both +N and -N conditions (Materials and methods). At the time of sampling, visible phenotypic differences were observable between +N and -N plots as measured through aerial imaging (details are reported in Rodene et al, 2022 using the same experimental field). Paired-end 16 S sequencing produced 216,681,749 raw sequence reads representing 496,738 unique amplicon sequence variants (ASVs) (Materials and methods).…”

“…To mitigate false discoveries of GWAS, Bonferroni corrections were applied based on the effective number of independent SNPs (or effective SNP number) ( Li et al, 2012 ). The effective SNP number for the genetic marker set and population employed in this study was determined to be N=769,690 independent markers as described previously ( Rodene et al, 2022 ). Using an alpha value of 0.05, we determined a significance threshold of -log10(0.05/769,690)=7.2.…”

“…Measurement of the leaf traits was carried out as previously described ( Ge et al, 2019 ). Two aerial imaging traits, canopy coverage (CC) and excess green index (ExG), were collected on August 12, 2019, 11–13 days after rhizobiome sample collection ( Rodene et al, 2022 ).…”

“…two check genotypes (B73xMo17 and B37xMo17) were used in 2018, and a single check genotype (B73xMo17) was used in 2019. Plant growth across the field was determined uniform within quadrants using the checks as reported in a sister study on the same experimental field ( Rodene et al, 2022 ). Any subplots across the field that remained empty due to seed unavailability were filled with the check genotype as well.…”

Estimating the genetic parameters of yield-related traits under different nitrogen conditions in maize

“…Each block was further subdivided into three sub-blocks. Within each sub-blocks, two hybrid varieties B73×Mo17 and B37×Mo17 were planted randomly as check plants (see also (15; 16)).…”