Yield prediction through integration of genetic, environment, and management data through deep learning.

ABSTRACT: Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied toward this goal. Here we predict maize yield using deep neural networks, compare the efficacy of 2 model development methods, and contextualize model performance using conventional linear and machine learning models. We examine the usefulness of incorporating interactions between disparate data types. We find deep learning and best linear unbiased predictor (BLUP) models with interactions had the best overall performance. BLUP models achieved the lowest average error, but deep learning models

SUBMITTER: Kick DR 

PROVIDER: S-EPMC10085787 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature