AUTHOR=Jubair Sheikh , Domaratzki Mike 

TITLE=Crop genomic selection with deep learning and environmental data: A survey

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=5

YEAR=2023

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2022.1040295

DOI=10.3389/frai.2022.1040295

ISSN=2624-8212

ABSTRACT=<p>Machine learning techniques for crop genomic selections, especially for single-environment plants, are well-developed. These machine learning models, which use dense genome-wide markers to predict phenotype, routinely perform well on single-environment datasets, especially for complex traits affected by multiple markers. On the other hand, machine learning models for predicting crop phenotype, especially deep learning models, using datasets that span different environmental conditions, have only recently emerged. Models that can accept heterogeneous data sources, such as temperature, soil conditions and precipitation, are natural choices for modeling GxE in multi-environment prediction. Here, we review emerging deep learning techniques that incorporate environmental data directly into genomic selection models.</p>