Unlocking the potential of plant phenotyping data through integration and data-driven approaches.
Unlocking the potential of plant phenotyping data through integration and data-driven approaches.
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DOI：
10.1016/j.coisb.2017.07.002
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发表时间：
2017-08-01
影响因子：
3.7
通讯作者：
Dhondt, Stijn
中科院分区：
文献类型：
--
作者：
Coppens, Frederik;Wuyts, Nathalie;Dhondt, Stijn
植物表型分析已成为一个综合性的研究领域，这是成像传感器在高通量数据采集应用方面取得重大进展的结果。不利的一面是，存在被自动化表型分析系统产生的大量数据淹没的风险。目前，主要的挑战在于数据管理，包括数据标注和恰当的元数据收集层面，以及在数据采集和分析之间实现协同方面。数据分析的进展包括努力整合表型和组学数据资源，以弥合表型 - 基因型差距，并深入了解植物的基本过程。
Plant phenotyping has emerged as a comprehensive field of research as the result of significant advancements in the application of imaging sensors for high-throughput data collection. The flip side is the risk of drowning in the massive amounts of data generated by automated phenotyping systems. Currently, the major challenge lies in data management, on the level of data annotation and proper metadata collection, and in progressing towards synergism across data collection and analyses. Progress in data analyses includes efforts towards the integration of phenotypic and -omics data resources for bridging the phenotype-genotype gap and obtaining in-depth insights into fundamental plant processes.