Quantifying senescence in bread wheat using multispectral imaging from an unmanned aerial vehicle and QTL mapping.

Environmental stresses from climate change can alter source-sink relations during plant maturation, leading to premature senescence and decreased yields. Elucidating the genetic control of natural variations in senescence in wheat (Triticum aestivum) can be accelerated using recent developments in unmanned aerial vehicle (UAV) based imaging techniques. Here, we describe the use of UAVs to quantify senescence in wheat using vegetative indices (VIs) derived from multispectral images. We detected senescence with high heritability, as well as its impact on grain yield, in a doubled-haploid population and parent cultivars at various growth time points after anthesis in the field. Selecting for slow senescence using a combination of different UAV-based VIs was more effective than using a single ground-based vegetation index. We identified 28 quantitative trait loci (QTLs) for vegetative growth, senescence, and grain yield using a 660K single-nucleotide polymorphism (SNP) array. Seventeen of these new QTLs for VIs from UAV-based multispectral imaging were mapped on chromosomes 2B, 3A, 3D, 5A, 5D, 5B, and 6D; these QTLs have not been reported previously using conventional phenotyping methods. This integrated approach allowed us to identify an important, previously unreported, senescence-related locus on chromosome 5D that showed high phenotypic variation (up to 18.1%) for all UAV-based VIs at all time points during grain filling. This QTL was validated for slow senescence by developing kompetitive allele-specific PCR (KASP) markers in a natural population. Our results suggest that UAV-based high-throughput phenotyping is advantageous for temporal assessment of the genetics underlying senescence in wheat.