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 geno..., 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., , 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...高通量表型组学（High-throughput phenotyping, HTP）拓展了植物研究中数据的维度，但迄今为止，基于HTP的创新性生物学发现仍较为匮乏。基于田间的高通量表型组学（FHTP）借助搭载成像传感器的小型无人驾驶航空器（unoccupied aerial vehicles, UAVs），可在具备生物学意义的环境条件下，常规化监测分离植物群体与环境的互作。本研究于2018年在灌溉与干旱胁迫试验中，针对520个分离玉米重组自交系（RILs）采集了开花期与株高这两类重要的物候适应性性状数据。研究结合无人机表型组、单核苷酸多态性（single nucleotide polymorphism, SNP）基因组数据及二者的整合数据，通过多种场景预测开花时间：仅使用基因组数据时，未测试基因型的散粉期、吐丝期与终株高的预测精度分别为0.58、0.59与0.41；而结合表型组与基因组数据后，预测精度分别提升至0.77、0.76与0.58。 本研究采用两种不同的无人机平台，在干旱与灌溉试验中对重组自交系群体进行观测。其一为旋翼无人机DJI Phantom 3 Professional，搭载1200万像素DJI FC300X RGB相机，飞行高度25米，像素分辨率约为1厘米/像素；其二为固定翼Tuffwing无人机测绘系统，搭载MicaSense RedEdge-MX多光谱相机，飞行高度120米，像素分辨率约为7.5厘米/像素。 为生成各次飞行的正射影像图，本研究分别使用Pix4Dmapper处理RGB原始影像、Agisoft PhotoScan处理多光谱原始影像。随后，借助R语言中的FIELDimageR包对各正射影像图进行样地水平的数据提取。 本次公开的数据集包含以下内容： 1. 2018_RGB_Flights.zip 该压缩包包含由DJI Phantom 3 Professional无人机（搭载1200万像素DJI FC300X RGB相机，飞行高度25米）采集的19幅正射影像图，飞行日期以yyyy/mm/dd格式标注。其中前3幅正射影像（20180314、20180330与20180404）未用于数据分析，仅用于展示无植被覆盖的裸地场景。RGB飞行对应的播种后天数可参见图1。 2. 2018_Multi_flights.zip 该压缩包包含由固定翼Tuffwing无人机测绘系统（搭载MicaSense RedEdge-MX多光谱相机，飞行高度120米）采集的8幅正射影像图，飞行日期以mm/dd/yy格式标注。多光谱飞行对应的播种后天数可参见图1。 3. 2018_SHP.zip 该压缩包包含适用于干旱与灌溉试验的样地矢量形状文件，格式为ESRI Shapefile，用于……