Plant Genotype to Phenotype Prediction Using Machine Learning

Monica Furaste Danilevicz, Mitchell Gill, Robyn Anderson, Jacqueline Batley, Mohammed Bennamoun, Philipp E Bayer, David Edwards

Research output: Contribution to journalReview articlepeer-review

18 Citations (Scopus)


Genomic prediction tools support crop breeding based on statistical methods, such as the genomic best linear unbiased prediction (GBLUP). However, these tools are not designed to capture non-linear relationships within multi-dimensional datasets, or deal with high dimension datasets such as imagery collected by unmanned aerial vehicles. Machine learning (ML) algorithms have the potential to surpass the prediction accuracy of current tools used for genotype to phenotype prediction, due to their capacity to autonomously extract data features and represent their relationships at multiple levels of abstraction. This review addresses the challenges of applying statistical and machine learning methods for predicting phenotypic traits based on genetic markers, environment data, and imagery for crop breeding. We present the advantages and disadvantages of explainable model structures, discuss the potential of machine learning models for genotype to phenotype prediction in crop breeding, and the challenges, including the scarcity of high-quality datasets, inconsistent metadata annotation and the requirements of ML models.

Original languageEnglish
Article number822173
JournalFrontiers in Genetics
Publication statusPublished - 18 May 2022


Dive into the research topics of 'Plant Genotype to Phenotype Prediction Using Machine Learning'. Together they form a unique fingerprint.

Cite this