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

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.

An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery

“…Although a previous investigation used a similar image-based UAV technology to calculate cotton height [43], no field validation was performed, so this methodology remained non-validated at the early growth stage. In this context, some authors have estimated plant height at a late stage, such as Watanabe et al [44], in barley fields just before harvest, and Varela et al [45], some weeks before maize flowering, obtaining lower or similar R 2 values than our results: 0.52 and 0.63, respectively. Comparatively, the coefficient of correlation increased when the experiment was conducted at later stage, e.g., Varela et al [45] achieved an R 2 of 0.79 in the abovementioned experiment, but used images taken at flowering stage, which denotes a positive relationship between both variables.…”

Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives

“…However, there are only six articles that explicitly include “Phenotyping” or “Phenomics” in the titles and keyword (Zaman-Allah et al, 2015; Gomez-Candon et al, 2016; Haghighattalab et al, 2016; Holman et al, 2016; Shi et al, 2016; Watanabe et al, 2017). The other literatures are closely related to crop phenotyping using UAV-RSPs but do not explicitly mention crop phenotype; the research focuses on one or more crop traits.…”

Quantitative Estimation of Wheat Phenotyping Traits Using Ground and Aerial Imagery

“…Consequently, these platforms are utilized for the assessment of nurseries and breeder plots [9]. The plot-wise characteristics that are usually targeted for capture are canopy vigour [10,11], canopy height [12], biomass [13], leaf area [14] or ground cover [15].…”

“…Measurement of plant height using a ruler has long been the traditional approach [12,16,17]. Assessment of plant height from images is a far more complex process as it necessitates the estimation of depth in physical units; in discipline terms, a so-called depth map is reconstructed from multiple images of a canopy taken from slightly different viewpoints.…”