DOI: 10.5061/dryad.1vhhmgqwm
title: Phenomic data-driven biological prediction of maize through field-based high throughput phenotyping integration with genomic data

description: 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.
description: 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 <em>FIELDimageR</em> packge in R.
description: Datasets included: 1. 2018\_RGB\_Flights.zip This file contains 19
 orthomosaics captured by the DJI Phantom 3 Professional, equipped with an
 RGB sensor (12-megapixel DJI FC300X camera), and flown at an altitude of
 25 meters. The flight dates are designated as yyyy/mm/dd. The first three
 orthomosaics (20180314, 20180330, and 20180404) were not utilized and were
 included to display the bare ground without vegetation. The days after
 planting corresponding to the RGB flight times can be seen in Figure 1. 2.
 2018\_Multi\_flights.zip This file contains 8 orthomosaics captured by a
 fixed-wing Tuffwing UAV mapper equipped with a multispectral camera, the
 MicaSense RedEdge-MX, flown at an altitude of 120 meters. The flight date
 is designated as mm/dd/yy. The days after planting corresponding to the
 multispectral flight times can be seen in Figure 1. 3. 2018\_SHP.zip This
 file contains a plot-based shape files for drought and irrigated trials in
 ESRI shapefile format and designed for **RGB orthomosaics**. The compiled
 geospatial dataset comprises four components: .shp, .shx, .prj, and .dbf
 extensions. The drought trials are denoted as 'YYCD,' while the
 irrigated trials are denoted as 'YYCI.' For the drought trial,
 the files 2018\_YYCD.dbf, 2018\_YYCD.prj, 2018\_YYCD.shp, and
 2018\_YYCD.shx are utilized, and for the irrigated trial, the
 corresponding files are 2018\_YYCI.dbf, 2018\_YYCI.prj, 2018\_YYCI.shp,
 and 2018\_YYCI.shx. These shape files should be viewed alongside the RGB
 orthomosaics to observe the plots in both drought and irrigated trials.
 Notably, there are no space between the shape files 2018\_YYCD.shp and
 2018\_YYCI.shp. However, spaces are introduced between shape files across
 rows and ranges in experimental design, by additional shape files with
 "\_buff" extensions. Specifically, for the drought trial, the
 files 2018\_YYCD\_buff.dbf, 2018\_YYCD\_buff.prj, 2018\_YYCD\_buff.shp,
 and 2018\_YYCD\_buff.shx are generated, and for the irrigated trial, the
 corresponding files are 2018\_YYCI\_buff.dbf, 2018\_YYCI\_buff.prj,
 2018\_YYCI\_buff.shp, and 2018\_YYCI\_buff.shx. These shape files, with
 spaces between them, should be viewed alongside the RGB orthomosaics to
 analyze the plots in the respective trials. In this study, the
 2018\_YYCD\_buff.shp and 2018\_YYCI\_buff.shp files were utilized. 4.
 2018\_SHP2.zip This file contains a plot-based shape files for drought and
 irrigated trials in ESRI shapefile format and designed for **Multispectral
 orthomosaics**. The compiled geospatial dataset comprises four components:
 .shp, .shx, .prj, and .dbf extensions. The drought trials are denoted as
 'YYCD,' while the irrigated trials are denoted as
 'YYCI.' For the drought trial, the files 2018\_YYCD.dbf,
 2018\_YYCD.prj, 2018\_YYCD.shp, and 2018\_YYCD.shx are utilized, and for
 the irrigated trial, the corresponding files are 2018\_YYCI.dbf,
 2018\_YYCI.prj, 2018\_YYCI.shp, and 2018\_YYCI.shx. These shape files
 should be viewed alongside the Multispectral orthomosaics to observe the
 plots in both drought and irrigated trials. Notably, there are no space
 between the shape files 2018\_YYCD.shp and 2018\_YYCI.shp. However, spaces
 are introduced between shape files across rows and ranges in experimental
 design, by additional shape files with "\_buff" extensions.
 Specifically, for the drought trial, the files 2018\_YYCD\_buff.dbf,
 2018\_YYCD\_buff.prj, 2018\_YYCD\_buff.shp, and 2018\_YYCD\_buff.shx are
 generated, and for the irrigated trial, the corresponding files are
 2018\_YYCI\_buff.dbf, 2018\_YYCI\_buff.prj, 2018\_YYCI\_buff.shp, and
 2018\_YYCI\_buff.shx. These shape files, with spaces between them, should
 be viewed alongside the RGB orthomosaics to analyze the plots in the
 respective trials. In this study, the 2018\_YYCD\_buff.shp and
 2018\_YYCI\_buff.shp files were utilized. 5. Phenomic\_data.zip This file
 contains the four phenomic data. 'YYCD18\_MULTI.csv' contains 83
 VIs plus red, green, blue, red-edge and NIR bands belonging to each plot
 in drought trial along with the planting information.
 'YYCI18\_MULTI.csv' contains 83 VIs plus red, green, blue,
 red-edge and NIR bands belonging to each plot in irrigated trial along
 with the planting information. 'YYCD18\_RGB.csv' contains 32 VIs
 plus red, green and blue bands belonging to each plot in drought trial
 along with the planting information. 'YYCI18\_RGB.csv' contains
 32 VIs plus red, green and blue bands belonging to each plot in irrigated
 trial along with the planting information. Please see the supplementary
 table 1 for vegetation indices (VIs), it is attached here as well.
 Equation 1 in the article was used to analyze each phenomic data stored
 here. 6. To view the orthomosaics and shape files, it is recommended to
 use QGIS (https://www.qgis.org/en/site/#), which is a free and open source
 geographic information system software.