AUTHOR=Watanabe Kakeru , Guo Wei , Arai Keigo , Takanashi Hideki , Kajiya-Kanegae Hiromi , Kobayashi Masaaki , Yano Kentaro , Tokunaga Tsuyoshi , Fujiwara Toru , Tsutsumi Nobuhiro , Iwata Hiroyoshi 

TITLE=High-Throughput Phenotyping of Sorghum Plant Height Using an Unmanned Aerial Vehicle and Its Application to Genomic Prediction Modeling

JOURNAL=Frontiers in Plant Science

VOLUME=8

YEAR=2017

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2017.00421

DOI=10.3389/fpls.2017.00421

ISSN=1664-462X

ABSTRACT=<p>Genomics-assisted breeding methods have been rapidly developed with novel technologies such as next-generation sequencing, genomic selection and genome-wide association study. However, phenotyping is still time consuming and is a serious bottleneck in genomics-assisted breeding. In this study, we established a high-throughput phenotyping system for sorghum plant height and its response to nitrogen availability; this system relies on the use of unmanned aerial vehicle (UAV) remote sensing with either an RGB or near-infrared, green and blue (NIR-GB) camera. We evaluated the potential of remote sensing to provide phenotype training data in a genomic prediction model. UAV remote sensing with the NIR-GB camera and the 50th percentile of digital surface model, which is an indicator of height, performed well. The correlation coefficient between plant height measured by UAV remote sensing (PH<sub>UAV</sub>) and plant height measured with a ruler (PH<sub>R</sub>) was 0.523. Because PH<sub>UAV</sub> was overestimated (probably because of the presence of taller plants on adjacent plots), the correlation coefficient between PH<sub>UAV</sub> and PH<sub>R</sub> was increased to 0.678 by using one of the two replications (that with the lower PH<sub>UAV</sub> value). Genomic prediction modeling performed well under the low-fertilization condition, probably because PH<sub>UAV</sub> overestimation was smaller under this condition due to a lower plant height. The predicted values of PH<sub>UAV</sub> and PH<sub>R</sub> were highly correlated with each other (<italic>r</italic> = 0.842). This result suggests that the genomic prediction models generated with PH<sub>UAV</sub> were almost identical and that the performance of UAV remote sensing was similar to that of traditional measurements in genomic prediction modeling. UAV remote sensing has a high potential to increase the throughput of phenotyping and decrease its cost. UAV remote sensing will be an important and indispensable tool for high-throughput genomics-assisted plant breeding.</p>