Neelam Yadav课题组评述高通量表型分析平台加速农作物改良

来源:上海国际人类表型组研究院 发布时间:2021-05-13

2021年5月11日,印度哈里亚纳邦农业大学(Haryana Agricultural University)Neelam Yadav课题组在《表型组学》(Phenomics)期刊在线发表了题为High-Throughput Phenotyping: A Platform for Accelerating Crop Improvement的综述文章。该文章阐述了表型组学在作物改良中的重要性,讨论了多种适用于作物高通量表型分析技术,强调了高通量表型组学平台在田间试验的作物改良应用和新兴技术的未来前景。

提高粮食作物产量是全世界长期以来的关注焦点。为满足全球气候条件下的粮食需求,需在不同种植条件下筛选不同特征的农作物。许多粮食作物的基因组信息在公众平台上是开放的,同时与其对应的表型信息却因受制于环境因素等而非常欠缺。近年来,高通量表型分析技术和自动化植物表型分析平台发展迅速,提高了自动化传感、数据采集和分析能力,有利于揭示复杂特征的遗传基础与植物生长发育和特定表型之间的关系以及加速品种的筛选过程,将为作物育种带来了革命性变革。

开放的田间农作物测量平台主要涉及地下和地上筛选。其中,成像技术对于植物高通量表型分析具有重要作用,遥感技术、机器人技术、航空计算技术的进步也促进了基于田间环境的表型测量平台发展。科学家们和企业研发了不同的田间作物表型测量系统和技术,如高通量根系表型分析平台Shovelomics和Rhizoslides、自动化大规模表型筛选系统Phenoscope、多传感器的田间高通量表型分析平台BreedVision等。此外,作者讨论了欧洲植物分析网络、澳大利亚植物表型组学设施、国际植物表型网络等自动化表型测量平台或设施,以及印度高通量表型分析设施现状。

这些高通量表型分析技术和平台的应用对于作物的干旱胁迫、病虫害、盐胁迫、种质、营养等关键表型分析和筛选改良至关重要。

High-Throughput Phenotyping: A Platform to Accelerate Crop Improvement

Abstract:Development of high-throughput phenotyping technologies has progressed considerably in the last 10 years. These technologies provide precise measurements of desired traits among thousands of field-grown plants under diversified environments; this is a critical step towards selection of better performing lines as to yield, disease resistance, and stress tolerance to accelerate crop improvement programs. High-throughput phenotyping techniques and platforms help unraveling the genetic basis of complex traits associated with plant growth and development and targeted traits. This review focuses on the advancements in technologies involved in high-throughput, field-based, aerial, and unmanned platforms. Development of user-friendly data management tools and softwares to better understand phenotyping will increase the use of field-based high-throughput techniques, which have potential to revolutionize breeding strategies and meet the future needs of stakeholders.

论文DOI链接:

https://link.springer.com/article/10.1007/s43657-020-00007-6

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