Phenomic data-driven biological prediction of maize through field-based high throughput phenotyping integration with genomic data

High-throughput phenotyping (HTP) has expanded the dimensionality of data in plant research; however, HTP has resulted in few novel biological discoveries to date. Field-based HTP (FHTP), using small unoccupied aerial vehicles (UAVs) equipped with imaging sensors, can be deployed routinely to monitor segregating plant population interactions with the environment under biologically meaningful conditions. Here, flowering dates and plant height, important phenological fitness traits, were collected on 520 segregating maize recombinant inbred lines (RILs) in both irrigated and drought stress trials in 2018. Using UAV phenomic, single nucleotide polymorphism (SNP) genomic, as well as combined data, flowering times were predicted using several scenarios. Untested genotypes were predicted with 0.58, 0.59, and 0.41 prediction ability for anthesis, silking, and terminal plant height, respectively, using genomic data, but prediction ability increased to 0.77, 0.76, and 0.58 when phenomic and genomic data were used together. Using the phenomic data in a genome-wide association study, a heat-related candidate gene (GRMZM2G083810; hsp18f) was discovered using temporal reflectance phenotypes belonging to flowering times (both irrigated and drought) trials where heat stress also peaked. Thus, a relationship between plants and abiotic stresses belonging to a specific time of growth was revealed only through use of temporal phenomic data. Overall, this study showed that (i) it is possible to predict complex traits using high dimensional phenomic data between different environments, and (ii) temporal phenomic data can reveal a time-dependent association between genotypes and abiotic stresses, which can help understand mechanisms to develop resilient plants.

Methods
Two different UAV platforms were used in the study to observe the RIL population in both drought and irrigated trials. One was a rotary wing UAV, the DJI Phantom 3 Professional, equipped with an RGB sensor (12-megapixel DJI FC300X camera) and flown at 25 meters, providing approximately 1 cm per pixel resolution. The other was a fixed-wing Tuffwing UAV mapper equipped with a multispectral camera, the MicaSense RedEdge-MX, flown at 120 meters, yielding roughly 7.5 cm per pixel resolution.To create orthomosaics for each flight, we processed the raw images from each flight using Pix4Dmapper for RGB data and Agisoft PhotoScan for multispectral data. Subsequently, we conducted plot-based data extraction on each orthomosaic using FIELDimageR packge in R.