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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