Breeding 5.0: Artificial intelligence (AI)‐decoded germplasm for accelerated crop innovation

Abstract

Crop breeding technologies are vital for global food security. While traditional methods have improved yield, stress tolerance, and nutrition, rising challenges such as climate instability, land loss, and pest pressure now demand new solutions. This study introduces the Breeding 5.0 framework, driven by artificial intelligence (AI) and robotics, marking a shift from empirical selection to intelligent systems. Central to this transformation is AI's emerging ability to deeply "understand germplasm," not merely by identifying genetic markers but also by decoding its architecture, plasticity, regulatory logic, and environmental interactions. This germplasm intelligence enables predictive trait modeling, optimized parental design, and targeted selection. We define four technical paradigms enabling this shift: (i) Multimodal data integration to bridge genotype and phenotype; (ii) Omni-simulated environments for virtual performance testing; (iii) Peopleless data capture for scalable precision; and (iv) Expert, explainable AI for biologically grounded decisions. Together, these technologies algorithmically convert germplasm into actionable breeding insights, accelerating the full cycle from ideal plant type design to elite line development. We further propose the "breeding flywheel," a self-reinforcing system that continuously amplifies phenotypic gains and refines breeding strategies, thereby enabling faster and smarter crop improvement to ensure a sustainable food future.