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.

Details

Title
Dynamic plant height QTL revealed in maize through remote sensing phenotyping using a high-throughput unmanned aerial vehicle (UAV)
Author
Wang Xiaqing 1 ; Zhang Ruyang 1 ; Song, Wei 1 ; Han, Liang 2 ; Liu, Xiaolei 3 ; Sun, Xuan 1 ; Luo Meijie 1 ; Chen, Kuan 1 ; Zhang, Yunxia 1 ; Yang, Hao 4 ; Yang, Guijun 4 ; Zhao Yanxin 1   VIAFID ORCID Logo  ; Zhao Jiuran 1 

 Beijing Academy of Agriculture & Forestry Sciences, Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Center, Beijing, China (GRID:grid.418260.9) (ISNI:0000 0004 0646 9053) 
 Beijing Research Center for Information Technology in Agriculture, Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing, China (GRID:grid.418260.9); Shanxi Datong University, College of Architecture and Geomatics Engineering, Datong, China (GRID:grid.440639.c) (ISNI:0000 0004 1757 5302) 
 Huazhong Agricultural University, Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Wuhan, China (GRID:grid.35155.37) (ISNI:0000 0004 1790 4137) 
 Beijing Research Center for Information Technology in Agriculture, Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing, China (GRID:grid.418260.9) 
Publication year
2019
Publication date
Dec 2019
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2188201884
Copyright
This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.