Replication Data for: Multimodal Deep Learning Methods Enhance Genomic Prediction of Wheat Breeding

In plant breeding research, several statistical machine learning methods have been developed and studied for assessing the genomic prediction (GP) accuracy of unobserved phenotypes. To increase the GP accuracy of unobserved phenotypes while simultaneously accounting for the complexity of genotype × environment interaction (GE), deep learning (DL) neural networks have been developed.These analyses can potentially include phenomics data obtained through imaging. The two datasets included in this study contain phenomic, phenotypic, and genotypic data for a set of wheat materials. They have been used to compare a novel DL method with conventional GP models.The results of these analyses are reported in the accompanying journal article.

Bibliographic Details

Main Authors:	Montesinos-López, Abelardo, Rivera Amado, Alma Carolina, Pinto, Francisco, Piñera Chavez, Francisco Javier, Gonzalez, David, Reynolds, Matthew, Pérez-Rodríguez, Paulino, Li, Huihui, Montesinos-López, Osval A., Crossa, Jose
Other Authors:	Dreher, Kate
Format:	Experimental data biblioteca
Language:	English
Published:	CIMMYT Research Data & Software Repository Network 2023
Subjects:	Agricultural Sciences, Plant Breeding, Grain yield, Thousand grain weight, Canopy normalized difference vegetation index, Agricultural research, Triticum aestivum, Wheat,
Online Access:	https://hdl.handle.net/11529/10548885
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