[1]周济,Francois Tardieu,Tony Pridmore,等.植物表型组学:发展、现状与挑战[J].南京农业大学学报,2018,41(4):580-588.[doi:10.7685/jnau.201805100]
 ZHOU Ji,Francois Tardieu,Tony Pridmore,et al.Plant phenomics:history,present status and challenges[J].Journal of Nanjing Agricultural University,2018,41(4):580-588.[doi:10.7685/jnau.201805100]
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植物表型组学:发展、现状与挑战()
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《南京农业大学学报》[ISSN:1000-2030/CN:32-1148/S]

卷:
41卷
期数:
2018年4期
页码:
580-588
栏目:
出版日期:
2018-07-09

文章信息/Info

Title:
Plant phenomics:history,present status and challenges
作者:
周济123 Francois Tardieu4 Tony Pridmore5 John Doonan6 Daniel Reynolds2 Neil Hall2 Simon Griffiths7 程涛1 朱艳1 王秀娥1 姜东1 丁艳锋1
1. 南京农业大学植物表型组学研究中心, 江苏 南京 210095;
2. Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ, UK;
3. University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK
Author(s):
ZHOU Ji123 Francois Tardieu4 Tony Pridmore5 John Doonan6 Daniel Reynolds2 Neil Hall2 Simon Griffiths7 CHENG Tao1 ZHU Yan1 WANG Xiu’e1 JIANG Dong1 DING Yanfeng1
1. Plant Phenomics Research Center, Nanjing Agricultural University, Nanjing 210095, China;
2. Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ, UK;
3. University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK
关键词:
表型组学多层次表型遥感成像技术机器人技术物联网人工智能高通量性状分析
Keywords:
phenomicsmulti-scale phenotypingremote sensingimagingroboticsInternet of Things(IoT)artificial intelligencehigh-throughput traits analyses
分类号:
Q94;N39
DOI:
10.7685/jnau.201805100
摘要:
随着遥感、机器人技术、计算机视觉和人工智能的发展,植物表型组学研究已经步入了快速成长阶段。本文首先介绍了植物表型组学的发展简史,包括其理论核心、研究方法、在生物研究中的应用以及国际上最新的研究动向。然后,针对各类表型技术载体平台如手持、人载、车载、田间实时监控、大型室内外自动化平台和航空机载等,分析这些技术手段在室内、外植物研究中的应用情况和实际问题。为了对表型研究中产生的巨量图像和传感器数据进行量化分析,把大数据转化为有实际意义的性状信息和生物学知识,本文着重讨论了后期表型数据解析和相应的研发过程。最后,提出表型组学的应用前景与未来展望,以期为中国的表型研究提供指导和建议。
Abstract:
With the development of remote sensing,robotics,computer vision and artificial intelligence,plant phenomics research has been developing rapidly in recent years. Here,we first introduced a concise history of this research domain,including the theoretical foundation,research methods,biological applications,and the latest progress. Then,we introduced some important indoor and outdoor phenotyping approaches such as handheld devices,ground-based manual and automated vehicles,robotic systems,Internet of Things(IoT)based distributed platforms,automatic deep phenotyping systems,and large-scale aerial phenotyping,together with their advantages and disadvantages during the applications. In order to extract meaningful information from big image-and sensor-based datasets generated by the phenotyping process,we also specified key phenotypic analysis methods and related development procedures. Finally,we discussed the future perspective of plant phenomics,with recommendations of how to apply this research field to breeding,cultivation and agricultural practices in China.

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备注/Memo

备注/Memo:
收稿日期:2018-05-31。
作者简介:周济,教授,研究方向为表型组学、系统开发、机器学习、图像分析、小麦育种,E-mail:ji.zhou@njau.edu.cn,ji.zhou@earlham.ac.uk。
通信作者:周济,教授,研究方向为表型组学、系统开发、机器学习、图像分析、小麦育种,E-mail:ji.zhou@njau.edu.cn,ji.zhou@earlham.ac.uk
更新日期/Last Update: 1900-01-01