Supporting data for: Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics

Identifying and growing new crop varieties with the highest yield is of utmost importance to ensure robust and sustainable food supplies for the global population. Plant breeding programs benefit from increasing technological support but still rely on full growth cycle and manual yield measurement, hindering speed of development. While methods to predict yield have been proposed, satisfying levels of performance are still to be reached. In this study, we propose a new machine learning model that simultaneously leverages both genotype and phenotype measurement by fusing multiple sources of input data collected by unmanned aerial systems: longitudinal multispectral and thermal images, digital elevation models, along with single nucleotide polymorphisms (SNPs) measurements. To tackle the varying number of observations for each sample, we leverage a deep multiple instance learning framework with an attention mechanism that also allows us to shed light on the importance the trained model giv..., Plant Material and Field Layout
Spring wheat (Triticum aestivum L.) breeding lines of two different experiments, named as YT (Yield Trials, 27°22'57.6'' N, 109°55'34.7'' W) and EYT (Elite Yield Trials, 27°23'0.1'' N, 109°55'7.9'' W), were selected from the International Maize and Wheat Improvement Center (CIMMYT) wheat breeding program.  All the trials were planted in November 2017, at Norman E Borlaug Experiment Station in Ciudad Obregon, Sonora, Mexico during the 2017–18 season. The YT experiment consisted of 1800 unique spring wheat entries, while the EYT consisted of 1710 unique entries. Both experiments were arranged as the alpha lattice design and distributed within two blocks in YT and three blocks in EYT. The YT plots served as experimental units and were 1.7m × 3.4m in size, planted on two raised beds spaced 0.8m apart with paired rows on each bed at 0.15m spacing for each plot. The EYT plots were sown in flat and were 1.3m × 4m in size with six rows per plot.
UAS, Sensors, and..., The dataset includes six archived files, 2018_CIMMYT_YT_1.zip, 2018_CIMMYT_YT_2.zip, 2018_CIMMYT_YT_3.zip,2018_CIMMYT_YT_4.zip, 2018_CIMMYT_EYT.zip, and Genotypes.zip. The first five zipped files (YT_1 to YT_4 and EYT) zipped files contain all the phenomics data of the YT and EYT trials, while the last zipped file includes the genomics data.
The \"Table_Headers_Info.xlsx\" file explains the headers in each tabular data files.
2018 YT dataset
There are four sub-folders and a CSV file in this dataset.
The “DEM” folder stores the Digital Elevation Model (DEM) images of each breeding plot in Geo-Tiff format cropped from the DEM of the entire EYT field trial. The image file name indicates the plot ID.
The “Multispec” folder stores the reflectance orthorectified images in Geo-Tiff format cropped from orthorectified raw images captured by the MicaSence RedEdge camera. Due to its large scale, the YT field trials are separated into three sections, named “1-60”, “61-141”, and “142-320” respectively...