Machine learning in agriculture: from silos to marketplaces.

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Abstract

The increasing global population combined with climate change present a major challenge for agriculture. Most crops have been bred to perform in specific environments, and with the time required to produce new varieties, it is unlikely that breeders will be able to adapt varieties to the changing climate (Abberton et al., 2016). There is an urgent need to develop new approaches to accelerate the production of high performing resilient crop varieties. Crop breeding has seen many changes in recent decades, from the application of molecular markers, through to genetically modified, and more recently, genome edited crops (Scheben et al., 2017). However, these approaches are often limited by our lack of understanding of the genomic basis for complex traits, even with the deluge of data being generated by new genome sequencing and phenomics technologies. New approaches are required to translate this explosion of data into improved crop varieties.
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