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

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          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.

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          The impact of next-generation sequencing technology on genetics.

          If one accepts that the fundamental pursuit of genetics is to determine the genotypes that explain phenotypes, the meteoric increase of DNA sequence information applied toward that pursuit has nowhere to go but up. The recent introduction of instruments capable of producing millions of DNA sequence reads in a single run is rapidly changing the landscape of genetics, providing the ability to answer questions with heretofore unimaginable speed. These technologies will provide an inexpensive, genome-wide sequence readout as an endpoint to applications ranging from chromatin immunoprecipitation, mutation mapping and polymorphism discovery to noncoding RNA discovery. Here I survey next-generation sequencing technologies and consider how they can provide a more complete picture of how the genome shapes the organism.
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                Author and article information

                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                28 March 2017
                : 8
                1Laboratory of Biometry and Bioinformatics, Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo Tokyo, Japan
                2Institute for Sustainable Agro-ecosystem Services, Graduate School of Agricultural and Life Sciences, The University of Tokyo Tokyo, Japan
                3Air4D Co., Ltd. Tokyo, Japan
                4Laboratory of Plant Molecular Genetics, Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo Tokyo, Japan
                5Bioinformatics Laboratory, Department of Life Sciences, School of Agriculture, Meiji University Kanagawa, Japan
                6Earthnote Co., Ltd. Okinawa, Japan
                7Laboratory of Plant Nutrition and Fertilizers, Department of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, The University of Tokyo Tokyo, Japan
                Author notes

                Edited by: Julie Dickerson, Iowa State University, USA

                Reviewed by: David Mayerich, University of Houston, USA; Jagadish Rane, Indian Council of Agricultural Research, India

                *Correspondence: Hiroyoshi Iwata, aiwata@ 123456mail.ecc.u-tokyo.ac.jp

                This article was submitted to Technical Advances in Plant Science, a section of the journal Frontiers in Plant Science

                Copyright © 2017 Watanabe, Guo, Arai, Takanashi, Kajiya-Kanegae, Kobayashi, Yano, Tokunaga, Fujiwara, Tsutsumi and Iwata.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Page count
                Figures: 7, Tables: 0, Equations: 2, References: 38, Pages: 11, Words: 0
                Funded by: Japan Science and Technology Agency 10.13039/501100002241
                Plant Science
                Original Research


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