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231226s2022 xx |||||o 00| ||eng c |
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|a 10.1111/nph.18314
|2 doi
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|a pubmed24n1147.xml
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Sun, Gang
|e verfasserin
|4 aut
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| 245 |
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|a AirMeasurer
|b open-source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice
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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 Completed 21.10.2022
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|a Date Revised 22.03.2023
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|a published: Print-Electronic
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|a CommentIn: New Phytol. 2022 Nov;236(4):1229-1231. - PMID 35962746
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|a Citation Status MEDLINE
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|a © 2022 The Authors. New Phytologist © 2022 New Phytologist Foundation.
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|a Low-altitude aerial imaging, an approach that can collect large-scale plant imagery, has grown in popularity recently. Amongst many phenotyping approaches, unmanned aerial vehicles (UAVs) possess unique advantages as a consequence of their mobility, flexibility and affordability. Nevertheless, how to extract biologically relevant information effectively has remained challenging. Here, we present AirMeasurer, an open-source and expandable platform that combines automated image analysis, machine learning and original algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low-cost UAVs in rice (Oryza sativa) trials. We applied the platform to study hundreds of rice landraces and recombinant inbred lines at two sites, from 2019 to 2021. A range of static and dynamic traits were quantified, including crop height, canopy coverage, vegetative indices and their growth rates. After verifying the reliability of AirMeasurer-derived traits, we identified genetic variants associated with selected growth-related traits using genome-wide association study and quantitative trait loci mapping. We found that the AirMeasurer-derived traits had led to reliable loci, some matched with published work, and others helped us to explore new candidate genes. Hence, we believe that our work demonstrates valuable advances in aerial phenotyping and automated 2D/3D trait analysis, providing high-quality phenotypic information to empower genetic mapping for crop improvement
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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| 650 |
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4 |
|a 2D/3D trait analysis
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| 650 |
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4 |
|a aerial phenotyping
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| 650 |
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|a genetic mapping
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| 650 |
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4 |
|a predictive modelling
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| 650 |
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|a rice
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| 650 |
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4 |
|a static and dynamic traits
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| 700 |
1 |
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|a Lu, Hengyun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhao, Yan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhou, Jie
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Jackson, Robert
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Yongchun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xu, Ling-Xiang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Ahong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Colmer, Joshua
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ober, Eric
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhao, Qiang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Han, Bin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhou, Ji
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t The New phytologist
|d 1979
|g 236(2022), 4 vom: 23. Nov., Seite 1584-1604
|w (DE-627)NLM09818248X
|x 1469-8137
|7 nnns
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| 773 |
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|g volume:236
|g year:2022
|g number:4
|g day:23
|g month:11
|g pages:1584-1604
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| 856 |
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|u http://dx.doi.org/10.1111/nph.18314
|3 Volltext
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