植物学报

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智能育种:从高通量表型到基因组选择的整合与应用

王创新1, 2,李建琪1 , 张颖1, 韩栋1, 房静1, 赵恩光1, 黄华1, 2, 达玲玲1*, 张继1, 2*   

  1. 1西北师范大学生命科学学院, 兰州 730070; 2西北师范大学新农村发展研究院, 兰州 730070


  • 收稿日期:2025-08-30 修回日期:2025-11-14 出版日期:2025-12-16 发布日期:2025-12-16
  • 通讯作者: 达玲玲, 张继
  • 基金资助:
    甘肃省科技创新人才计划(No.25RCKA018)

Smart Breeding: Integration and Application from High-Throughput Phenotyping to Genome Selection

Chuangxin Wang1, 2, Jianqi Li,1, Ying Zhang1, Dong Han1, Jing Fang1, Enguang Zhao1, Hua Huang1, 2, Lingling Da1*, Ji Zhang1, 2*   

  1. 1College of Life Sciences, Northwest Normal University, Lanzhou 730070, China; 2Institute of New Rural Development, Northwest Normal University, Lanzhou 730070, China

  • Received:2025-08-30 Revised:2025-11-14 Online:2025-12-16 Published:2025-12-16
  • Contact: Lingling Da, Ji Zhang

摘要: 随着全球气候变暖与人口激增, 作物的育种需要朝着更高精度、高效率和可持续性迈进。在人工智能飞速发展的浪潮下, 基于人工智能的智能育种应运而生, 代表了植物育种前沿, 其借助结合机器学习和深度学习的高通量表型获取、以全基因组关联分析为基础的基因组选择和多组学大数据分析等技术, 实现遗传信息的整合与编辑及表型与基因组的结合, 将为农业育种带来革命性变化, 提升作物生产力、改善质量并改良性状。展望未来, 精准农业和个性化育种是重要发展方向, 聚焦环境适应性和气候变化的策略有助于应对环境挑战。智能育种的前沿技术, 能够显著提高育种效率与精准度, 将塑造植物育种的未来发展方向。

关键词: 智能育种, 高通量表型, 机器学习, 基因组选择, 大数据分析

Abstract: With global warming and population growth accelerating, crop breeding must advance towards greater precision, efficiency and sustainability. Riding the wave of rapid AI development, AI-driven Smart Breeding has emerged as the cutting edge of plant breeding. Leveraging technologies such as high-throughput phenotyping through machine learning and deep learning, genome-wide association studies for genomic selection, and multi-omics big data analysis, it integrates and edits genetic information while linking phenotypes with genomes. This approach promises revolutionary changes in agricultural breeding, enhancing crop productivity, improving quality, and refining traits. Looking ahead, precision agriculture and personalised breeding represent key developmental trajectories. Strategies focusing on environmental adaptability and climate change mitigation will aid in addressing ecological challenges. The cutting-edge technologies of smart Breeding, significantly enhancing breeding efficiency and precision, will shape the future direction and progress of plant breeding.

Key words: Smart breeding, High-throughput phenotyping, Machine learning, Genome-wide selection, Big data analysis