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AI赋能杂粮智慧育种:现状、前沿与展望OA

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

中文摘要英文摘要

全球粮食安全正面临着人口持续增长、气候变化加剧和农业生态日益脆弱等多重压力.杂粮作物具有耐旱、耐贫瘠等特点,是营养价值高的储备粮,在构建多元化食物体系中具有战略价值.然而,科研投入不足和育种体系不完善,导致大量杂粮资源长期被边缘化.智慧育种通过生物技术与人工智能(Artificial intelligence,AI)深度融合,为突破杂粮育种瓶颈开辟了新路径.本文系统梳理智慧育种技术体系演进历程,探讨生物大数据以及 AI 在杂粮种质资源数字化、高通量表型组学、基因型-表型关联分析及智能决策系统中的应用;总结燕麦、谷子、荞麦、藜麦等典型杂粮作物智慧育种案例,探讨杂粮育种产业化路径,提出基础研究薄弱、体制机制障碍、人才资金约束等问题并分析相应对策.最后展望从头驯化、基因组设计育种、数字孪生育种等前沿研究方向.

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.

李旭凯;李亚军;唐娇艳;孔照胜

山西农业大学 山西省后稷实验室,山西 太原 030031山西农业大学 山西省后稷实验室,山西 太原 030031山西农业大学 山西省后稷实验室,山西 太原 030031山西农业大学 山西省后稷实验室,山西 太原 030031

农业科技

杂粮智慧育种人工智能表型组学种质资源生物大数据

Coarse-grain cropsSmart breedingArtificial intelligence(AI)PhenomicsGermplasm resourceBiological big data

《华南农业大学学报》 2026 (5)

769-778,10

山西省基础研究计划面上项目(202303021221097)山西省2024年度"三晋英才"计划科技创新青年拔尖人才项目(SJYC2024255)

10.7671/j.issn.1001-411X.202605014

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