Leveraging AI and integrated genomic-enviromic prediction for intelligent sugarcane breeding

Traditional sugarcane breeding, reliant on phenotypic selection, is being transformed by genomic tools. However, the crop's highly polyploid genome and significant genotype-by-environment interactions pose challenges that conventional models cannot adequately address. Although the integrated genomic-enviromic prediction (iGEP) framework offers a promising path forward, its application to a complex clonal crop such as sugarcane requires significant extension. This review provides the first comprehensive road map for implementing iGEP in sugarcane, systematically addressing its unique biological constraints, and synthesizes a tailored "three-model" computational framework (genetic, environmental, and phenotypic) to decode polyploid allelic dosage, quantify high-resolution environmental drivers through an "isoenvironment" design, and predict clonal performance. In addition, we describe extensions of artificial intelligence (AI) and iGEP models to leverage clonal propagation, optimize multi-trait selection, and overcome perennial ratoon dynamics. Finally, we present a phased road map for construction of an AI model, outlining a transformative path from digitization to synthetic design. By combining cutting-edge predictive analytics with the distinctive biology of sugarcane, this work establishes a new paradigm for accelerating genetic gain in this vital crop and offers a transferable strategy for other species with complex genomes.