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Application of machine learning and genomics for orphan crop improvement

Author

Listed:
  • Tessa R. MacNish

    (The University of Western Australia
    The University of Western Australia)

  • Monica F. Danilevicz

    (The University of Western Australia
    The University of Western Australia
    The University of Western Australia)

  • Philipp E. Bayer

    (The University of Western Australia
    The University of Western Australia
    Minderoo Foundation)

  • Mitchell S. Bestry

    (The University of Western Australia
    The University of Western Australia)

  • David Edwards

    (The University of Western Australia
    The University of Western Australia)

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.

Suggested Citation

  • Tessa R. MacNish & Monica F. Danilevicz & Philipp E. Bayer & Mitchell S. Bestry & David Edwards, 2025. "Application of machine learning and genomics for orphan crop improvement," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-56330-x
    DOI: 10.1038/s41467-025-56330-x
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    References listed on IDEAS

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    1. Chia-Yi Cheng & Ying Li & Kranthi Varala & Jessica Bubert & Ji Huang & Grace J. Kim & Justin Halim & Jennifer Arp & Hung-Jui S. Shih & Grace Levinson & Seo Hyun Park & Ha Young Cho & Stephen P. Moose , 2021. "Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    2. Shifeng Cheng & Cong Feng & Luzie U. Wingen & Hong Cheng & Andrew B. Riche & Mei Jiang & Michelle Leverington-Waite & Zejian Huang & Sarah Collier & Simon Orford & Xiaoming Wang & Rajani Awal & Gary B, 2024. "Harnessing landrace diversity empowers wheat breeding," Nature, Nature, vol. 632(8026), pages 823-831, August.
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