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

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 es...

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Autores principales: Watanabe, Kakeru, Guo, Wei, Arai, Keigo, Takanashi, Hideki, Kajiya-Kanegae, Hiromi, Kobayashi, Masaaki, Yano, Kentaro, Tokunaga, Tsuyoshi, Fujiwara, Toru, Tsutsumi, Nobuhiro, Iwata, Hiroyoshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5368247/
https://www.ncbi.nlm.nih.gov/pubmed/28400784
http://dx.doi.org/10.3389/fpls.2017.00421
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author Watanabe, Kakeru
Guo, Wei
Arai, Keigo
Takanashi, Hideki
Kajiya-Kanegae, Hiromi
Kobayashi, Masaaki
Yano, Kentaro
Tokunaga, Tsuyoshi
Fujiwara, Toru
Tsutsumi, Nobuhiro
Iwata, Hiroyoshi
author_facet Watanabe, Kakeru
Guo, Wei
Arai, Keigo
Takanashi, Hideki
Kajiya-Kanegae, Hiromi
Kobayashi, Masaaki
Yano, Kentaro
Tokunaga, Tsuyoshi
Fujiwara, Toru
Tsutsumi, Nobuhiro
Iwata, Hiroyoshi
author_sort Watanabe, Kakeru
collection PubMed
description 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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spelling pubmed-53682472017-04-11 High-Throughput Phenotyping of Sorghum Plant Height Using an Unmanned Aerial Vehicle and Its Application to Genomic Prediction Modeling Watanabe, Kakeru Guo, Wei Arai, Keigo Takanashi, Hideki Kajiya-Kanegae, Hiromi Kobayashi, Masaaki Yano, Kentaro Tokunaga, Tsuyoshi Fujiwara, Toru Tsutsumi, Nobuhiro Iwata, Hiroyoshi Front Plant Sci Plant Science 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. Frontiers Media S.A. 2017-03-28 /pmc/articles/PMC5368247/ /pubmed/28400784 http://dx.doi.org/10.3389/fpls.2017.00421 Text en Copyright © 2017 Watanabe, Guo, Arai, Takanashi, Kajiya-Kanegae, Kobayashi, Yano, Tokunaga, Fujiwara, Tsutsumi and Iwata. http://creativecommons.org/licenses/by/4.0/ 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.
spellingShingle Plant Science
Watanabe, Kakeru
Guo, Wei
Arai, Keigo
Takanashi, Hideki
Kajiya-Kanegae, Hiromi
Kobayashi, Masaaki
Yano, Kentaro
Tokunaga, Tsuyoshi
Fujiwara, Toru
Tsutsumi, Nobuhiro
Iwata, Hiroyoshi
High-Throughput Phenotyping of Sorghum Plant Height Using an Unmanned Aerial Vehicle and Its Application to Genomic Prediction Modeling
title High-Throughput Phenotyping of Sorghum Plant Height Using an Unmanned Aerial Vehicle and Its Application to Genomic Prediction Modeling
title_full High-Throughput Phenotyping of Sorghum Plant Height Using an Unmanned Aerial Vehicle and Its Application to Genomic Prediction Modeling
title_fullStr High-Throughput Phenotyping of Sorghum Plant Height Using an Unmanned Aerial Vehicle and Its Application to Genomic Prediction Modeling
title_full_unstemmed High-Throughput Phenotyping of Sorghum Plant Height Using an Unmanned Aerial Vehicle and Its Application to Genomic Prediction Modeling
title_short High-Throughput Phenotyping of Sorghum Plant Height Using an Unmanned Aerial Vehicle and Its Application to Genomic Prediction Modeling
title_sort high-throughput phenotyping of sorghum plant height using an unmanned aerial vehicle and its application to genomic prediction modeling
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5368247/
https://www.ncbi.nlm.nih.gov/pubmed/28400784
http://dx.doi.org/10.3389/fpls.2017.00421
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