Beyond Data: Artificial intelligence, knowledge graphs, and the next revolution in wheat breeding

As a cornerstone of global food security, wheat (Triticum aestivum) faces unprecedented pressure from a growing population and a changing climate. However, traditional breeding approaches are increasingly insufficient to address the genetic complexity required to achieve substantial gains in yield and resilience. This review highlights key advances in the generation of large-scale, standardized datasets through the integration of high-throughput genotyping and multidimensional phenotyping. We explore how multi-omics integration and knowledge graph-based frameworks transform heterogeneous data into actionable breeding knowledge. In addition, we examine the pivotal role of artificial intelligence (AI) and machine learning in enhancing predictive modeling, refining genomic selection, and enabling intelligent decision-making. These advances underpin the emerging paradigm of Breeding 5.0, which leverages data-driven innovation and closed-loop iterative cycles. Looking ahead, multimodal AI and personalized breeding strategies will be critical for developing sustainable systems capable of ensuring global food security under climate change.