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 (PHUAV) and plant height measured with a ruler (PHR) was 0.523. Because PHUAV was overestimated (probably because of the presence of taller plants on adjacent plots), the correlation coefficient between PHUAV and PHR was increased to 0.678 by using one of the two replications (that with the lower PHUAV value). Genomic prediction modeling performed well under the low-fertilization condition, probably because PHUAV overestimation was smaller under this condition due to a lower plant height. The predicted values of PHUAV and PHR were highly correlated with each other (r = 0.842). This result suggests that the genomic prediction models generated with PHUAV 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.
Highlights
Improving the throughput of phenotyping in the field is a big challenge in plant genetics, physiology, and breeding
To assess the accuracy of plant height measurements, we compared the correlation between plant height measured by UAV remote sensing (PHUAV) and plant height measured with a ruler (PHR) obtained with the two cameras at the 50th, 75th, 90th, and 99th percentiles of digital surface model (DSM) values (Figure 4)
root mean square difference (RMSD) between PHUAV and PHR was lower for the NIR-GB than RGB camera: (0.649 vs. 0.883 (50th percentile), 0.626 vs. 0.827 (75th), 0.628 vs. 0.792 (90th), and 0.665 vs. 0.759 (99th)
Summary
Improving the throughput of phenotyping in the field is a big challenge in plant genetics, physiology, and breeding. The emergence of the next-generation sequencing technologies enables us to obtain genome-wide DNA polymorphism data for a large number of samples and rapidly (Mardis, 2007; Davey et al, 2011) Statistical methods, such as genome-wide association study (GWAS; Brachi et al, 2011; Korte and Farlow, 2013; Huang and Han, 2014) and genomic selection (GS; Meuwissen et al, 2001; Jannink et al, 2010) allow us to associate DNA polymorphism data, which is extremely high-dimensional, to phenotypic variations in agronomic traits. Because phenotypic data is necessary for genomics-assisted breeding, it is the first priority to develop a high-throughput phenotyping method
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call