Remote-Sensing-Combined Haplotype Analysis Using Multi-Parental Advanced Generation Inter-Cross Lines Reveals Phenology QTLs for Canopy Height in Rice

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.

Prediction of heading date, culm length, and biomass from canopy-height-related parameters derived from time-series UAV observations of rice

“…K was positively correlated with CL. This result is consistent with our previous data showing high correlation between CH and CL in several rice lines ( Ogawa et al., 2021b ). d 1 was positively correlated with DTH, ADW, and SLW.…”

“…Aerial observations were made about once a week as in our previous studies ( Ogawa et al., 2019 ; Ogawa et al., 2021a ; Ogawa et al., 2021b ). We used a Phantom 4 Pro UAV (P4P; DJI, Shenzhen, China) to capture RGB images with an onboard 20-megapixel camera that flew automatically at 1.0 m/s over the paddy field at an altitude of 10.3 m. DJI GS Pro software controlled the flight path and set the following photogrammetry conditions: capture mode, time interval; front overlap ratio, 80%; side overlap ratio, 80%; gimbal pitch angle, −90°, white balance, cloudy; aperture, auto; shutter, auto.…”

“…Estimation of plant height in maize inbred lines at four growth stages by UAV showed that temperate lines grew faster at early growth stages, but tropical lines grew faster at later growth stages (Wang et al, 2019b). Time-series observations of rice CH with UAV correlated highly with CH measured by hand, and revealed growth patterns and differences in functional stages of quantitative trait loci for CH (Ogawa et al, 2021b). Use of a cable-suspended phenotyping platform allowed the temperature response of CH in wheat lines to be clarified (Kronenberg et al, 2021).…”

“…In our previous study, rice CH estimated from UAV images was highly correlated with CL ( Ogawa et al., 2021b ), making it a potential predictor of yield and biomass. A promising approach to make the most of UAV-based CH data for rice phenotyping is time-series monitoring, in which remote sensing has an advantage by being non-invasive and non-destructive.…”

“…UAVs can carry several types of cameras, including RGB (red–green–blue), multispectral, and thermal infrared, to take images of crops ( Yang et al., 2017 ; Sakamoto et al., 2022 ). From RGB images, the 2D vegetation fraction and vertical canopy height (CH) can be extracted ( Ogawa et al., 2021a ; Ogawa et al., 2021b ). Vegetation indices, obtained by spectral analysis, such as the Normalized Difference Vegetation Index (NDVI), have been used for estimating nitrogen use efficiency ( Liang et al., 2021 ), drought resistance ( Jiang et al., 2021 ), and lodging ( Yadav et al., 2017 ; Singh et al., 2019 ), and for predicting biomass and yield ( Yue et al., 2017 ; Gong et al., 2018 ; Di Gennaro et al., 2019 ; Duan et al., 2019 ; Wang et al., 2019a ).…”