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通过数字化和自动化提高植物根系表型分析的效率。

Improving the efficiency of plant root system phenotyping through digitization and automation.

作者信息

Teramoto Shota, Uga Yusaku

机构信息

Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki 305-8518, Japan.

出版信息

Breed Sci. 2022 Mar;72(1):48-55. doi: 10.1270/jsbbs.21053. Epub 2022 Feb 9.

Abstract

Root system architecture (RSA) determines unevenly distributed water and nutrient availability in soil. Genetic improvement of RSA, therefore, is related to crop production. However, RSA phenotyping has been carried out less frequently than above-ground phenotyping because measuring roots in the soil is difficult and labor intensive. Recent advancements have led to the digitalization of plant measurements; this digital phenotyping has been widely used for measurements of both above-ground and RSA traits. Digital phenotyping for RSA is slower and more difficult than for above-ground traits because the roots are hidden underground. In this review, we summarized recent trends in digital phenotyping for RSA traits. We classified the sample types into three categories: soil block containing roots, section of soil block, and root sample. Examples of the use of digital phenotyping are presented for each category. We also discussed room for improvement in digital phenotyping in each category.

摘要

根系结构(RSA)决定了土壤中水分和养分的分布不均。因此,RSA的遗传改良与作物产量相关。然而,RSA表型分析的开展频率低于地上部分的表型分析,因为在土壤中测量根系既困难又耗费人力。最近的进展已使植物测量数字化;这种数字表型分析已广泛用于地上部分和RSA性状的测量。由于根系隐藏在地下,RSA的数字表型分析比地上部分性状的分析更慢且更困难。在本综述中,我们总结了RSA性状数字表型分析的最新趋势。我们将样本类型分为三类:含根土块、土块切片和根样本。针对每一类给出了数字表型分析的应用实例。我们还讨论了每一类数字表型分析的改进空间。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b371/8987843/26524ea83398/72_048-g001.jpg

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