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Image Analysis: The New Bottleneck in Plant Phenotyping [Applications Corner]\ud

机译:图像分析:植物表型分析的新瓶颈[应用范围] \ ud

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摘要

Plant phenotyping is the identification of effects on the phenotype (i.e., the plant appearance and performance) as a result of genotype differences (i.e., differences in the genetic code) and the environmental conditions to which a plant has been exposed [1]?[3]. According to the Food and Agriculture Organization of the United Nations, large-scale experiments in plant phenotyping are a key factor in meeting the agricultural needs of the future to feed the world and provide biomass for energy, while using less water, land, and fertilizer under a constantly evolving environment due to climate change. Working on model plants (such as Arabidopsis), combined with remarkable advances in genotyping, has revolutionized our understanding of biology but has accelerated the need for precision and automation in phenotyping, favoring approaches that provide quantifiable phenotypic information that could be better used to link and find associations in the genotype [4]. While early on, the collection of phenotypes was manual, currently noninvasive, imaging-based methods are increasingly being utilized [5], [6]. However, the rate at which phenotypes are extracted in the field or in the lab is not matching the speed of genotyping and is creating a bottleneck.
机译:植物表型是识别由于基因型差异(即遗传密码的差异)和植物所处环境条件而对表型(即植物外观和性能)的影响[1]?[ 3]。据联合国粮食及农业组织称,植物表型的大规模试验是满足未来农业需求的关键因素,以满足世界粮食需求并提供生物量能源,同时减少水,土地和肥料的使用在由于气候变化而不断变化的环境中。在模型植物(如拟南芥)上进行的工作,再加上基因分型的显着进步,彻底改变了我们对生物学的理解,但加速了对表型进行精确性和自动化的需求,偏向于提供可量化表型信息的方法,这些方法可以更好地用于关联和分析。在基因型中找到关联[4]。在早期,表型的收集是手动的,目前越来越多地使用基于成像的非侵入性方法[5],[6]。但是,在现场或实验室中提取表型的速度与基因分型的速度不匹配,并且正在造成瓶颈。

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