Leveraging AI and integrated genomic-environmic 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 (G×E) pose challenges that conventional models cannot adequately address. While the Integrated Genomic-Environmic Prediction (iGEP) framework provides a viable path forward, its application to a complex clonal crop like sugarcane requires profound extension. This review provides the first comprehensive roadmap for implementing iGEP in sugarcane, systematically addressing its unique biological constraints, and synthesizes a tailored "Three-Model" computational framework (Genetic, Environmental, Phenotypic) to decode polyploid allelic dosage, quantify high-resolution environmental drivers via "isoenvironment" design, and predict clonal performance. Furthermore, we detail the extension 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 roadmap to build an AI, outlining a transformative path from digitization to synthetic design. By bridging 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.