Plant breeding is undergoing a profound transformation, shifting from a phenotype-based discipline to a datadriven science. This chapter explores the paradigm of genomic design as the cornerstone of this revolution. We detail how the integration of high-throughput genotyping (e.g., SNP arrays, sequencing) and phenotyping (e.g., UAVs, IoT sensors) generates massive, multidimensional datasets. The chapter provides a foundational overview of genomic prediction models, from traditional statistical methods like GBLUP and Bayesian approaches to advanced machine learning and deep learning algorithms that capture complex, nonlinear genotype–phenotype–environment interactions. We emphasize the critical role of integrating multi-omics data (genomics, transcriptomics, proteomics, metabolomics) and environmental information to build predictive models for complex traits such as yield, stress resilience, and nutritional quality. Furthermore, we examine precise breeding strategies, including genomic selection (GS), marker-assisted selection (MAS), and genome editing (CRISPR/Cas9), which leverage these predictions to accelerate the development of superior cultivars. The chapter also reviews essential computational tools, cloud-based platforms, and bioinformatics pipelines that manage and analyze this big data. Finally, we discuss the prevailing challenges – data standardization, ethical considerations, and model interpretability – and outline future directions, including the synergistic potential of synthetic biology and digital twins. This work serves as a comprehensive guide for leveraging data-driven genomic design to enhance the precision, efficiency, and sustainability of next-generation crop breeding programs, ultimately contributing to global food security.
