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

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

doi:10.3389/fpls.2017.00421

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

Front Plant Sci. 2017 Mar 28:8:421. doi: 10.3389/fpls.2017.00421. eCollection 2017.

Authors

Affiliations

¹ Laboratory of Biometry and Bioinformatics, Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo Tokyo, Japan.
² Institute for Sustainable Agro-ecosystem Services, Graduate School of Agricultural and Life Sciences, The University of Tokyo Tokyo, Japan.
³ Air4D Co., Ltd. Tokyo, Japan.
⁴ Laboratory of Plant Molecular Genetics, Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo Tokyo, Japan.
⁵ Bioinformatics Laboratory, Department of Life Sciences, School of Agriculture, Meiji University Kanagawa, Japan.
⁶ Earthnote Co., Ltd. Okinawa, Japan.
⁷ Laboratory of Plant Nutrition and Fertilizers, Department of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, The University of Tokyo Tokyo, Japan.

Abstract

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_UAV) and plant height measured with a ruler (PH_R) was 0.523. Because PH_UAV was overestimated (probably because of the presence of taller plants on adjacent plots), the correlation coefficient between PH_UAV and PH_R was increased to 0.678 by using one of the two replications (that with the lower PH_UAV value). Genomic prediction modeling performed well under the low-fertilization condition, probably because PH_UAV overestimation was smaller under this condition due to a lower plant height. The predicted values of PH_UAV and PH_R were highly correlated with each other (r = 0.842). This result suggests that the genomic prediction models generated with PH_UAV 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.

Keywords: UAV remote sensing; genomic prediction; high-throughput phenotyping; near-infrared (NIR); sorghum plant height.