New integrated prediction strategy could transform crop breeding using big data and AI
In short: Researchers have proposed a new smart breeding scheme called integrated genomic-enviromic prediction (iGEP). By combining big data, artificial intelligence, and multi-source information—including genomics, phenomics, and environmental data—this approach aims to predict plant performance more precisely than traditional methods.
The study proposes a framework that integrates 3D information profiles of genotype, phenotype, and environment. While traditional breeding often focuses on genetics, this model emphasizes the importance of 'envirotypic' data, which has been largely neglected. As a review and proposal of a new scheme, it outlines how machine and deep learning can implement these complex, multidimensional datasets.
This is a theoretical proposal for a new breeding paradigm, so its success depends on the future availability and integration of large-scale spatiotemporal datasets.
Why it matters: Integrating environmental data with genetics via AI could significantly enhance our ability to design better crops.
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