室内植物表型平台及性状鉴定研究进展和展望

doi:10.12133/j.smartag.2020.2.1.202003-SA002

Smart Agriculture ›› 2020, Vol. 2 ›› Issue (1): 23-42.doi: 10.12133/j.smartag.2020.2.1.202003-SA002

• 专题--农业遥感与表型信息获取分析 • 上一篇下一篇

室内植物表型平台及性状鉴定研究进展和展望

徐凌翔¹, 陈佳玮¹, 丁国辉¹, 卢伟², 丁艳锋¹, 朱艳³, 周济^1,⁴()

^1.南京农业大学作物表型组学交叉研究中心/中英植物表型组学联合研究中心/江苏省现代作物生产协同创新中心/现代作物生产省部共建协同创新中心，江苏南京 210095
^2.南京农业大学工学院/江苏省现代设施农业技术与装备工程实验室，江苏南京 210095
^3.国家信息农业工程技术中心/农业农村部农作物系统分析及决策重点实验室/智慧农业教育部工程研究中心/江苏省信息农业高技术研究重点实验室，江苏南京 210095
^4.数字科学研发部，英国国立农业植物研究所/剑桥作物研究中心，剑桥 CB3 0LE，英国

收稿日期:2020-03-02 修回日期:2020-03-26 出版日期:2020-03-30
基金资助:
中央高校基本科研专项资金(JCQY201902);江苏省基础研究计划面上项目(BK20191311)
作者简介:徐凌翔（1984－），男，博士，研究方向：种子活力表型与分析，midway2005@163.com|陈佳玮（1994－），男，硕士，研究方向：表型分析与深度学习，chenjiawei@njau.edu.cn 徐凌翔、陈佳玮对本文有同等贡献，并列第一作者
通信作者:

Indoor phenotyping platforms and associated trait measurement: Progress and prospects

Xu Lingxiang¹, Chen Jiawei¹, Ding Guohui¹, Lu Wei², Ding Yanfeng¹, Zhu Yan³, Zhou Ji^1,⁴()

^1.Plant Phenomics Research Center/China-UK Plant Phenomics Research Center/Jiangsu Collaborative Innovation Center for Modern Crop Production/Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
^2.Electrical Engineering, College of Engineering, Jiangsu Key Laboratory of Modern Facility Agricultural Technology and Equipment Engineering, Nanjing Agricultural University, Nanjing 210095, China
^3.National Engineering and Technology Center for Information Agriculture/Ministry of Agriculture and Rural Affairs (MARA ) Key Laboratory for Crop System Analysis and Decision Making/Engineering Research Center for Smart Agriculture (Ministry of Education)/Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
^4.Data Sciences, National Institute of Agricultural Botany, Cambridge Crop Research, Cambridge CB3 0LE, Cambridge shire, UK

Received:2020-03-02 Revised:2020-03-26 Online:2020-03-30

摘要/Abstract

摘要：

植物表型组学研究正逐渐向综合化、规模化、多尺度和高通量的方向快速发展。本文首先介绍了植物表型研究的最新动向。然后针对室内表型监测平台的特点和各类室内表型针对的表型性状进行了系统介绍，包括产量、品质、胁迫抗性（包括干旱、抗冷热、盐胁迫、重金属和病虫害）等。在此基础上，本文还根据通量、传感器集成度和平台大小等把一些国内外流行的室内植物表型平台进行了分类，并介绍了这些室内表型平台在植物研究中的应用情况。同时，本文还介绍了室内表型数据的管理和解析方法。最后，本文着重讨论了室内表型平台的发展方向，并结合中国植物研究的实际情况对表型组学在中国的发展提出了展望，以期为中国植物表型研究提供指导和建议。

关键词: 植物表型组学, 室内表型监测, 产量性状, 品质性状, 抗性表型, 表型数据管理和解析分类

Abstract:

Plant phenomics is under rapid development in recent years, a research field that is progressing towards integration, scalability, multi-perspectivity and high-throughput analysis. Through combining remote sensing, Internet of Things (IoT), robotics, computer vision, and artificial intelligence techniques such as machine learning and deep learning, relevant research methodologies, biological applications and theoretical foundation of this research domain have been advancing speedily in recent years. This article first introduces the current trends of plant phenomics and its related progress in China and worldwide. Then, it focuses on discussing the characteristics of indoor phenotyping and phenotypic traits that are suitable for indoor experiments, including yield, quality, and stress related traits such as drought, cold and heat resistance, salt stress, heavy metals, and pests. By connecting key phenotypic traits with important biological questions in yield production, crop quality and Stress-related tolerance, we associated indoor phenotyping hardware with relevant biological applications and their plant model systems, for which a range of indoor phenotyping devices and platforms are listed and categorized according to their throughput, sensor integration, platform size, and applications. Additionally, this article introduces existing data management solutions and analysis software packages that are representative for phenotypic analysis. For example, ISA-Tab and MIAPPE ontology standards for capturing metadata in plant phenotyping experiments, PHIS and CropSight for managing complicated datasets, and Python or MATLAB programming languages for automated image analysis based on libraries such as OpenCV, Scikit-Image, MATLAB Image Processing Toolbox. Finally, due to the importance of extracting meaningful information from big phenotyping datasets, this article pays extra attention to the future development of plant phenomics in China, with suggestions and recommendations for the integration of multi-scale phenotyping data to increase confidence in research outcomes, the cultivation of cross-disciplinary researchers to lead the next-generation plant research, as well as the collaboration between academia and industry to enable world-leading research activities in the near future.

Key words: plant phenomics, indoor phenotyping platform, yield-related traits, quality-related traits, resistance-related phenotypes, phenotyping data management and phenotypic analysis

中图分类号:

徐凌翔, 陈佳玮, 丁国辉, 卢伟, 丁艳锋, 朱艳, 周济. 室内植物表型平台及性状鉴定研究进展和展望[J]. 智慧农业（中英文）, 2020, 2(1): 23-42.

Xu Lingxiang, Chen Jiawei, Ding Guohui, Lu Wei, Ding Yanfeng, Zhu Yan, Zhou Ji. Indoor phenotyping platforms and associated trait measurement: Progress and prospects[J]. Smart Agriculture, 2020, 2(1): 23-42.

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室内植物表型平台及性状鉴定研究进展和展望

Indoor phenotyping platforms and associated trait measurement: Progress and prospects

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