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. 2019 Mar 5;9(1):3458.
doi: 10.1038/s41598-019-39448-z.

Dynamic plant height QTL revealed in maize through remote sensing phenotyping using a high-throughput unmanned aerial vehicle (UAV)

Affiliations

Dynamic plant height QTL revealed in maize through remote sensing phenotyping using a high-throughput unmanned aerial vehicle (UAV)

Xiaqing Wang et al. Sci Rep. .

Abstract

Plant height (PH) is a key factor in maize (Zea mays L.) yield, biomass, and plant architecture. We investigated the PH of diverse maize inbred lines (117 temperate lines, 135 tropical lines) at four growth stages using unmanned aerial vehicle high-throughput phenotypic platforms (UAV-HTPPs). We extracted PH data using an automated pipeline based on crop surface models and orthomosaic model. The correlation between UAV and manually measured PH data reached 0.95. Under temperate field conditions, temperate maize lines grew faster than tropical maize lines at early growth stages, but tropical lines grew faster at later growth stages and ultimately became taller than temperate lines. A genome-wide association study identified 68 unique quantitative trait loci (QTLs) for seven PH-related traits, and 35% of the QTLs coincided with those previously reported to control PH. Generally, different QTLs controlled PH at different growth stages, but eight QTLs simultaneously controlled PH and growth rate at multiple growth stages. Based on gene annotations and expression profiles, we identified candidate genes controlling PH. The PH data collected by the UAV-HTPPs were credible and the genetic mapping power was high. Therefore, UAV-HTPPs have great potential for use in studies on PH.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Field high-throughput phenotyping for plant height. (A) Digitally designed graphs depicting maize plants at four growth stages. Graphs were constructed using PlantCAD-maize software. (B) Unmanned aerial vehicle equipment and plant height extraction process. (C) Dynamic plant height and quantitative trait loci (QTL) dissection. Whole procedure included trait variation analysis and genome-wide association study.
Figure 2
Figure 2
Linear relationship between plant height estimated from unmanned aerial vehicle (UAV) data and that measured manually at three growth stages. Blue solid line shows regression line, grey shadow represents 99% confidence interval.
Figure 3
Figure 3
Correlation coefficient matrix among seven plant height-related traits. Yellow and blue indicate positive and negative correlations, respectively. Size of circle is proportional to correlation coefficient (number).
Figure 4
Figure 4
Plant height and related trait variations between temperate (TEM) and tropical (TST) populations at four growth stages. Blue and red box represent TEM and TST populations, respectively. Line in box plots shows median value. Box edges represent first and third quartiles, and dots outside whiskers represent value over 1.5 × interquartile range. Stars indicate that phenotypic distribution is significantly different (P < 0.05).
Figure 5
Figure 5
Genome-wide association study for plant height at four stages among temperate (TEM), tropical (TST), and both (BOTH) maize groups. Different colours represent different chromosomes. Dotted line is threshold. Single nucleotide polymorphisms (SNPs) above the threshold showed significant association.
Figure 6
Figure 6
Genome-wide association study for growth rate at three time intervals among temperate (TEM), tropical (TST), and both (BOTH) maize groups. Different colours represent different chromosomes. Dotted line is the threshold. Single nucleotide polymorphisms (SNPs) above threshold showed significant association.

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