Summary
This narrative review synthesises recent multi-omics technologies to characterise abiotic stress tolerance mechanisms in millet crops, addressing the limitation that single-layer molecular approaches cannot resolve interconnected regulatory networks governing stress adaptation. The review highlights how integrating diverse omics data with artificial intelligence and machine learning can predict genotype-phenotype relationships and support precision breeding, with prospects for developing next-generation millet cultivars with enhanced climate resilience, yield and nutritional value whilst supporting sustainable agriculture.
Regional applicability
The findings are globally applicable to millet breeding and cultivation. For United Kingdom conditions, millet remains a minor crop, but the multi-omics and predictive breeding frameworks reviewed are transferable to UK cereal crops (barley, wheat) facing similar abiotic stress challenges under climate change, and could inform UK breeding programmes and agricultural research strategy.
Key measures
Multi-omics platforms (genomics, transcriptomics, proteomics, metabolomics, epigenomics, ionomics, phenomics); stress-responsive genes and regulatory pathways; adaptive metabolic processes; AI/ML predictive models; digital phenotyping outputs
Outcomes reported
The review synthesises multi-omics approaches (genomics, transcriptomics, proteomics, metabolomics, epigenomics, ionomics, phenomics) to identify abiotic stress tolerance mechanisms in millets. It examines how AI/ML integration with multi-omics data can improve genotype-phenotype prediction and inform precision breeding strategies for climate-resilient millet cultivars.
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