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231226s2022 xx |||||o 00| ||eng c |
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|a 10.1270/jsbbs.21053
|2 doi
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|a pubmed24n1152.xml
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|a (DE-627)NLM345632273
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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|a Teramoto, Shota
|e verfasserin
|4 aut
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|a Improving the efficiency of plant root system phenotyping through digitization and automation
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|c 2022
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Revised 07.09.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Copyright © 2022 by JAPANESE SOCIETY OF BREEDING.
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|a 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
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|a Journal Article
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|a high-throughput
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|a image analysis
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|a root traits
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|a semantic segmentation
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|a vectorization
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700 |
1 |
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|a Uga, Yusaku
|e verfasserin
|4 aut
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773 |
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|i Enthalten in
|t Breeding science
|d 1998
|g 72(2022), 1 vom: 01. März, Seite 48-55
|w (DE-627)NLM098238299
|x 1344-7610
|7 nnns
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773 |
1 |
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|g volume:72
|g year:2022
|g number:1
|g day:01
|g month:03
|g pages:48-55
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|u http://dx.doi.org/10.1270/jsbbs.21053
|3 Volltext
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|d 72
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|e 1
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|h 48-55
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