Application of machine learning and genomics for orphan crop improvement
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 representativ...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-56330-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832585590874308608 |
|---|---|
| author | Tessa R. MacNish Monica F. Danilevicz Philipp E. Bayer Mitchell S. Bestry David Edwards |
| author_facet | Tessa R. MacNish Monica F. Danilevicz Philipp E. Bayer Mitchell S. Bestry David Edwards |
| author_sort | Tessa R. MacNish |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bc807928e2954ff7b4e44ab21734b8fb |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-bc807928e2954ff7b4e44ab21734b8fb2025-01-26T12:41:48ZengNature PortfolioNature Communications2041-17232025-01-0116111010.1038/s41467-025-56330-xApplication of machine learning and genomics for orphan crop improvementTessa R. MacNish0Monica F. Danilevicz1Philipp E. Bayer2Mitchell S. Bestry3David Edwards4School of Biological Sciences, The University of Western AustraliaSchool of Biological Sciences, The University of Western AustraliaCentre for Applied Bioinformatics, The University of Western AustraliaSchool of Biological Sciences, The University of Western AustraliaSchool of Biological Sciences, The University of Western AustraliaAbstract 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.https://doi.org/10.1038/s41467-025-56330-x |
| spellingShingle | Tessa R. MacNish Monica F. Danilevicz Philipp E. Bayer Mitchell S. Bestry David Edwards Application of machine learning and genomics for orphan crop improvement Nature Communications |
| title | Application of machine learning and genomics for orphan crop improvement |
| title_full | Application of machine learning and genomics for orphan crop improvement |
| title_fullStr | Application of machine learning and genomics for orphan crop improvement |
| title_full_unstemmed | Application of machine learning and genomics for orphan crop improvement |
| title_short | Application of machine learning and genomics for orphan crop improvement |
| title_sort | application of machine learning and genomics for orphan crop improvement |
| url | https://doi.org/10.1038/s41467-025-56330-x |
| work_keys_str_mv | AT tessarmacnish applicationofmachinelearningandgenomicsfororphancropimprovement AT monicafdanilevicz applicationofmachinelearningandgenomicsfororphancropimprovement AT philippebayer applicationofmachinelearningandgenomicsfororphancropimprovement AT mitchellsbestry applicationofmachinelearningandgenomicsfororphancropimprovement AT davidedwards applicationofmachinelearningandgenomicsfororphancropimprovement |