首页|期刊导航|华中农业大学学报|多维数据链驱动与智能决策协同:智能育种技术体系的系统构建与遗传增益跃迁

多维数据链驱动与智能决策协同:智能育种技术体系的系统构建与遗传增益跃迁OA

Synergy of multi-dimensional data chain-driven and intelligent decision-making:systematic construction of intelligent breeding technology systems and transition of genetic gain

中文摘要英文摘要

智能育种作为现代种业技术革新的核心驱动力,正推动作物遗传改良从经验依赖型向数据驱动型转变.本文系统梳理了智能育种技术体系的构建路径及其在作物遗传改良中的应用进展,旨在为我国种业智能化转型提供理论依据与实践参考.基于高通量表型与基因型数据采集、人工智能算法决策、多组学平台集成等核心技术模块,结合国内外典型案例,综述了智能育种在复杂性状设计、种质资源挖掘与遗传增益预测等方面的突破性进展.智能育种通过构建"基因型-表型-环境"多维数据驱动模型,显著提升了育种效率与精准性.未来应加强生物-信息-工程交叉融合,开发知识嵌入型算法,构建全球协作平台,推动种质资源数字化共享与商业化育种体系重构,建议通过政策引导建立产学研协同机制,加速技术成果向产业端转化.

Intelligent breeding,as the core driving force of technological innovation in modern seed in-dustry,is facilitating the transformation of crop genetic improvement from experience-based approaches to data-driven paradigms.This article systematically reviewed the path of constructing the intelligent breeding technology framework and the progress on its application in crop genetic improvement to provide theoretical foundations and practical references for the intelligent transformation of seed industry in China.The achieved breakthroughs of intelligent breeding in designing complex trait,mining germplasm resource,and predict-ing genetic gain were summarized based on combining key technical modules including the acquisition of da-ta from high-throughput phenotyping and genotyping,the decision-making empowered by artificial intelli-gence(AI)algorithm and the integration of multi-omics platform with typical case studies at home and abroad.Intelligent breeding significantly improved the efficiency and precision of breeding by constructing multi-dimensional data-driven models that integrate genotype,phenotype,and environment(G×P×E).In the future,we should strengthen the interdisciplinary integration of biology,information and engineer-ing,develop knowledge-embeddedalgorithms,establish a global collaboration platform,promote the digi-tal sharing of germplasm resources and the reconstruction of commercial breeding systems.It is recommend-ed to establish policy-guided mechanisms to deepen industry-academia-research collaboration,thereby ac-celerating the translation of technological innovations into the industrial end.

张之奇;王兴涛;任端阳;曹文福;王建军

山西农业大学生态农牧研究所,朔州 036001山西农业大学生态农牧研究所,朔州 036001山西农业大学生态农牧研究所,朔州 036001山西农业大学生态农牧研究所,朔州 036001山西农业大学生态农牧研究所,朔州 036001

农业科技

智能育种人工智能算法基因编辑虚拟育种模拟智能挖掘遗传增益深度学习模型

intelligent breedingartificial intelligence(AI)algorithmsgene editingsimulation of virtual breedingintelligent mininggenetic gainmodel of deep learning

《华中农业大学学报》 2026 (3)

1-17,17

国家重点研发计划项目(2024YFD1201305)国家联合攻关项目子课题(NK202307020403)山西农业大学科研项目计划项目(CXGC202449)

10.13300/j.cnki.hnlkxb.2026.03.001

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