Temporal Estimates of Crop Growth in Sorghum and Maize Breeding Enabled by Unmanned Aerial Systems. Issue 1 (1st February 2018)

• Record Type: Journal Article
• Authors: Pugh, N. Ace; Horne, David W.; Murray, Seth C.; Carvalho, Geraldo; Malambo, Lonesome; Jung, Jinha; Chang, Anjin; Maeda, Murilo; Popescu, Sorin; Chu, Tianxing; Starek, Michael J.; Brewer, Michael J.; Richardson, Grant; Rooney, William L.
• Is Part Of: Plant phenome journal. Volume 1:Issue 1(2018)
• Page Start: 1
• Page End: 10
• Publication Date: 2018-02-01
• Subjects: Phenotype -- Periodicals; Plant genetics -- Periodicals; Periodicals; 581.35
• DOI: 10.2135/tppj2017.08.0006
• Languages: English
• ISSNs: 2578-2703

Abstract

Abstract : To meet future world food and fiber demands, plant breeders must increase the rate of genetic improvement of important agricultural crops. One of the biggest obstacles now facing crop scientists is a phenotyping bottleneck. To ease this burden, the emerging technology of unmanned aerial systems (UAS) presents an exciting opportunity. To assess the utility of UAS, it is important to investigate their application across multiple crop species. Terminal plant height is of great importance to maize ( Zea mays L.) and sorghum [ Sorghum bicolor (L.) Moench] breeders and has been hypothesized to be useful but has been logistically impractical to measure in the field. In this study, we statistically analyzed in depth the ability of UAS to estimate height in sorghum (advanced and early generation material) and maize (optimal and late material) and the application of these estimates in breeding programs. We found that UAS explain genotypic variation similarly to ground‐truth methods and that the repeatability of the methodology is high ( R = 0.61–0.99), indicating effective differentiation of genotypes. Additionally, correlations between ground truth and UAS measurements were moderate to high for all materials ( r = 0.4–0.9). Finally, we present a novel application for the technology in the form of high‐resolution temporal growth curves. Using these UAS‐generated growth curves, new physiological insights can be obtained and new avenues of scientific investigation are possible.