AI-driven smart breeding of coarse grain crops: Current status, frontiers and perspectives

Abstract

Global food security is confronted with mounting pressures arising from continuous population growth, intensifying climate change, and increasing agro-ecological vulnerability. Coarse grain crops, characterized by strong tolerance to drought and poor soil conditions, serve as strategic reserve crops with high nutritional value in the development of diversified food systems. Nevertheless, insufficient research investment and underdeveloped breeding infrastructure have led to the long-term marginalization of many coarse-grain crop resources. Smart breeding, through the integration of biotechnology and artificial intelligence (AI), opens new pathways for breaking through the bottlenecks in coarse grain crops breeding. This review systematically outlined the evolution of smart breeding technologies and discussed the applications of biological big data and AI in the digitization of coarse-grain crop germplasm resources, high-throughput phenomics, genotype-phenotype association analysis, and intelligent decision-making systems. We synthesized representative smart breeding cases in oats, foxtail millet, buckwheat, quinoa, and other coarse grain crops, explored pathways for the industrialization of coarse-grain breeding, and identified persistent challenges—specifically, the shortfall in fundamental research, institutional frictions in the breeding innovation system, and structural constraints in human resources and funding mechanisms and proposed corresponding countermeasures. Finally, we provided an outlook on frontier directions including de novo domestication, genomic design breeding, and digital twins breeding.