Researchers have mapped out a new AI-powered framework to dramatically improve sugarcane breeding by combining genetic data, environmental data, and machine learning — potentially accelerating how quickly breeders can develop better-performing varieties.
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A 'three-model' computational framework was designed to simultaneously decode sugarcane's complex polyploid genetics, map fine-scale environmental conditions, and predict how individual plant clones will perform in the field.
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The proposed 'isoenvironment' design allows breeders to group field sites by environmental similarity, enabling more precise prediction of genotype-by-environment interactions that have long confounded conventional breeding models.
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AI extensions to the framework are designed to exploit sugarcane's clonal propagation biology and perennial ratoon growth cycles — two biological traits that standard genomic prediction tools were not built to handle.
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