Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics.

ABSTRACT:

Motivation

Developing new crop varieties with superior performance is highly important to ensure robust and sustainable global food security. The speed of variety development is limited by long field cycles and advanced generation selections in plant breeding programs. While methods to predict yield from genotype or phenotype data have been proposed, improved performance and integrated models are needed.

Results

We propose a machine learning model that leverages both genotype and phenotype measurements by fusing genetic variants with multiple data sources collected by unmanned aerial systems. We use a deep multiple instance learning framework with an attention mechanism that sheds light on the importance given to each input during prediction, enhancing interpretability. Our

SUBMITTER: Togninalli M 

PROVIDER: S-EPMC10246581 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature