Application of machine learning and genomics for orphan crop improvement

Tessa R. MacNish, Monica F. Danilevicz, Philipp E. Bayer, Mitchell S. Bestry and David Edwards ()
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Tessa R. MacNish: The University of Western Australia
Monica F. Danilevicz: The University of Western Australia
Philipp E. Bayer: The University of Western Australia
Mitchell S. Bestry: The University of Western Australia
David Edwards: The University of Western Australia

Nature Communications, 2025, vol. 16, issue 1, 1-10

Abstract: Abstract Orphan crops are important sources of nutrition in developing regions and many are tolerant to biotic and abiotic stressors; however, modern crop improvement technologies have not been widely applied to orphan crops due to the lack of resources available. There are orphan crop representatives across major crop types and the conservation of genes between these related species can be used in crop improvement. Machine learning (ML) has emerged as a promising tool for crop improvement. Transferring knowledge from major crops to orphan crops and using machine learning to improve accuracy and efficiency can be used to improve orphan crops.

Date: 2025
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DOI: 10.1038/s41467-025-56330-x

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