Abstract
High-throughput phenotyping systems with unmanned aerial vehicles (UAVs) enable observation of crop lines in the field. In this study, we show the ability of time-course monitoring of canopy height (CH) to identify quantitative trait loci (QTLs) and to characterise their pleiotropic effect on various traits. We generated a digital surface model from low-altitude UAV-captured colour digital images and investigated CH data of rice multi-parental advanced generation inter-cross (MAGIC) lines from tillering and heading to maturation. Genome-wide association studies (GWASs) using the CH data and haplotype information of the MAGIC lines revealed 11 QTLs for CH. Each QTL showed haplotype effects on different features of CH such as stage-specificity and constancy. Haplotype analysis revealed relationships at the QTL level between CH and, vegetation fraction and leaf colour [derived from UAV red–green–blue (RGB) data], and CH and yield-related traits. Noticeably, haplotypes with canopy lowering effects at qCH1-4, qCH2, and qCH10-2 increased the ratio of panicle weight to leaf and stem weight, suggesting biomass allocation to grain yield or others through growth regulation of CH. Allele mining using gene information with eight founders of the MAGIC lines revealed the possibility that qCH1-4 contains multiple alleles of semi-dwarf 1 (sd1), the IR-8 allele of which significantly contributed to the “green revolution” in rice. This use of remote-sensing-derived phenotyping data into genetics using the MAGIC lines gives insight into how rice plants grow, develop, and produce grains in phenology and provides information on effective haplotypes for breeding with ideal plant architecture and grain yield.
Highlights
Crops dramatically change during their cultivation in the field in terms of mass, morphology, and colours, and these changes are similar when grown in the same cultivation region and season
To increase rice yields in breeding studies, genetic approaches using a population with natural variation and artificial mutation lines have largely focused on genes related to panicle number (PN), grain number per panicle, and grain weight (Xing and Zhang, 2010; Miura et al, 2011; Yin et al, 2021)
We showed that vegetation fraction (VF) calculated from unmanned aerial vehicles (UAVs) imagery is related to shoot dry weight during the vegetative stage in rice when observed in a population of multi-parental inter-mated lines [known as multi-parental advanced generation inter-cross (MAGIC) lines] named JapanMAGIC (JAM) (Ogawa et al, 2021)
Summary
Crops dramatically change during their cultivation in the field in terms of mass, morphology, and colours, and these changes are similar when grown in the same cultivation region and season. These phenological aspects in crops are possibly acquired through domestication (Gong, 2020; Lu et al, 2020) and are important to farmers in terms of efficient seasonal farming activities and maximisation of yields in a single harvest. One reason is that manual time-series measurement of crop growth is time-consuming and laborious. Another reason is that remotesensing technology for observing crops has not been popular and familiar for breeding researchers. The advent of lowcost, user-friendly unmanned aerial vehicles (UAVs) is changing this situation
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