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中国种业科技创新的智能时代——“玉米育种4.0”
引用本文:王向峰,才卓. 中国种业科技创新的智能时代——“玉米育种4.0”[J]. 玉米科学, 2019, 27(1): 1-9
作者姓名:王向峰  才卓
作者单位:中国农业大学/国家玉米改良中心;吉林省农业科学院/国家玉米工程技术研究中心(吉林)
基金项目:国家自然科学基金项目“应用机器学习的策略预测玉米杂种优势”(31871706)
摘    要:二十一世纪是人类社会全面步入人工智能的时代。人工智能与生命科学的结合必将为医疗、健康、农业等行业带来革命性变革,同时也为我国玉米种业的发展提供了千载难逢的历史机遇。回顾育种发展历史,作物育种技术的变迁大致经历了3个阶段,即古近代耕作者基于表型观察开展的农作物驯化与农家品种选育;现代职业育种家借助遗传学与统计学知识,基于经验开展的新品种改良;分子育种工作者应用基因工程和分子标记技术定向改造作物性状。伴随人类社会步入互联网、大数据、人工智能"三位一体"的时代,育种学已步入第4个历史时期。美国科学院院士、玉米遗传育种学家Edwards Buckler教授提出"育种4.0"的理念,未来育种将在基因编辑与合成生物学、基因组与生物信息学、大数据与人工智能等跨学科、多技术支撑下,实现作物新品种的智能、高效、定向培育,最终推动育种学从"艺术"到"科学"到"智能"的革命性转变。

关 键 词:玉米  育种4.0理念  基因组选择  人工智能

Era of Maize Breeding 4.0
WANG Xiang-feng and CAI Zhuo. Era of Maize Breeding 4.0[J]. Journal of Maize Sciences, 2019, 27(1): 1-9
Authors:WANG Xiang-feng and CAI Zhuo
Affiliation:National Maize Improvement Center, Beijing 100193 and National Maize Engineering Research Center(Jilin), Changchun 130033, China
Abstract:In the first twenty years of 21st century, human society has entered the era of artificial intelligences (AI). Combining AI and life sciences has promoted revolutionary changes in medicines, health and agriculture, and also provide unprecedented opportunities for maize seed industry in China. Crop breeding has experienced three historical phases, in which, ancient farmers cultivated landrace varieties by observing crop trait variations, professional breeders applied designed breeding schemes according to genetics and statistics, and modern molecular breeders applied genetic engineering technology to modify desired traits. Professor Edwards Buckler, a renowned maize geneticist raised the concept of "Breeding 4.0" which emphasizes that nowadays crop breeding will be supported by multiple cross-disciplinary technologies including gene editing and synthetic biology, genomics and bioinformatics, Big Data and AI, together to intelligently and efficiently accelerate breeding cycles, and finally promote the revolutionary change of crop breeding from "art", to "sciences", and finally to "intelligence".
Keywords:Maize  Breeding 4.0  Genomic selection  Machine learning
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