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综述:新一代表型组学的新型传感器和数据驱动方法。

Review: New sensors and data-driven approaches-A path to next generation phenomics.

机构信息

Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark; Department of Adaptive Biotechnologies, Global Change Research Institute, CAS, Brno, Czech Republic.

LEPSE, INRA, Montpellier SupAgro, Univ Montpellier, Montpellier, France.

出版信息

Plant Sci. 2019 May;282:2-10. doi: 10.1016/j.plantsci.2019.01.011. Epub 2019 Jan 12.

DOI:10.1016/j.plantsci.2019.01.011
PMID:31003608
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6483971/
Abstract

At the 4th International Plant Phenotyping Symposium meeting of the International Plant Phenotyping Network (IPPN) in 2016 at CIMMYT in Mexico, a workshop was convened to consider ways forward with sensors for phenotyping. The increasing number of field applications provides new challenges and requires specialised solutions. There are many traits vital to plant growth and development that demand phenotyping approaches that are still at early stages of development or elude current capabilities. Further, there is growing interest in low-cost sensor solutions, and mobile platforms that can be transported to the experiments, rather than the experiment coming to the platform. Various types of sensors are required to address diverse needs with respect to targets, precision and ease of operation and readout. Converting data into knowledge, and ensuring that those data (and the appropriate metadata) are stored in such a way that they will be sensible and available to others now and for future analysis is also vital. Here we are proposing mechanisms for "next generation phenomics" based on our learning in the past decade, current practice and discussions at the IPPN Symposium, to encourage further thinking and collaboration by plant scientists, physicists and engineering experts.

摘要

在 2016 年国际植物表型组学网络(IPPN)于墨西哥 CIMMYT 举行的第四届国际植物表型组学研讨会会议上,召开了一次研讨会,以探讨传感器在表型分析方面的未来发展方向。越来越多的田间应用带来了新的挑战,需要专门的解决方案。有许多对植物生长和发育至关重要的特性,需要仍处于开发早期或当前能力无法实现的表型分析方法。此外,人们对低成本传感器解决方案和可运输到实验现场的移动平台越来越感兴趣,而不是让实验来到平台。需要各种类型的传感器来满足针对目标、精度和操作简便性及读取的多样化需求。将数据转化为知识,并确保这些数据(和适当的元数据)以一种能够在现在和未来的分析中合理和可用的方式进行存储,这也至关重要。在这里,我们基于过去十年的经验、当前的实践和 IPPN 研讨会的讨论,提出了“下一代表型组学”的机制,以鼓励植物科学家、物理学家和工程专家进一步思考和合作。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc2d/6483971/d7843369a537/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc2d/6483971/d7843369a537/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc2d/6483971/d7843369a537/gr1.jpg

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