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Advancing artificial intelligence to help feed the world

Nature Biotechnology volume 41, pages 1188–1189 (2023)Cite this article

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Artificial intelligence (AI) has enabled recent step changes in some fields, most notably in predicting three-dimensional structures of proteins and producing generative text on diverse topics1,2. These applications use deep learning models trained on colossal datasets to make their predictions. Crop and livestock breeders are now producing billions of data points through the generation of phenotypic, genomic and multiomic datasets. To breed crops and animals for more productive and sustainable food production systems, it will be critical to use these large datasets in a smarter way.

Setting a breeding goal has traditionally been achieved by defining the traits that contribute to farmers’ profit and then deriving economic weights from bioeconomic models to produce a selection index. Some traits, particularly those associated with risk or changing climates and consumer preferences, do not fit particularly well into bioeconomic models (for example, disease resistance or sustainability traits such as drought tolerance in crops, and methane emissions or welfare traits in livestock). The ‘1,000 minds’ approach determines the relative ranking of alternatives when multiple objectives must be considered at the same time, by asking stakeholders to consider a series of pairwise comparisons regarding the importance of the competing objectives3. The 1,000 minds approach has been used to weight traits in the selection index on the basis of farmers preferences4,5. As breeding becomes increasingly focused on production of food with traits preferred by consumers, such as flavor of products from crops or animal welfare, machine learning could play an increasing role in setting the breeding goal. For example, the power of large language models to embed text in vectors and then generate outputs as functions of these vectors could translate online surveys sent to millions of consumers to derive future trait index weightings.

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Fig. 1: Tasks for designing breeding programs for livestock and crops.

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Acknowledgements

The authors gratefully acknowledge funding from the Australian Research Council for linkage project LP 70100317 ‘Faststack’.

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Authors and Affiliations

  1. Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, Queensland, Australia

    Ben J. Hayes, Chensong Chen, Owen Powell, Eric Dinglasan, Kira Villiers & Lee T. Hickey

  2. Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia

    Kathryn E. Kemper

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  1. Ben J. Hayes
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  3. Owen Powell
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Contributions

B.J.H.: conceived the concept, drafted the manuscript. C.C.: contributed to manuscript drafting and the manuscript concept. O.P., E.D., K.V. and K.E.K.: contributed to manuscript drafting. L.T.H.: conceived the concept, designed and prepared the figure, contributed to manuscript drafting.

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Correspondence to Ben J. Hayes.

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Hayes, B.J., Chen, C., Powell, O. et al. Advancing artificial intelligence to help feed the world. Nat Biotechnol 41, 1188–1189 (2023). https://doi.org/10.1038/s41587-023-01898-2

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