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Seasonal pigment fluctuation in diploid and polyploid Arabidopsis revealed by machine learning-based phenotyping method PlantServation

Long-term field monitoring of leaf pigment content is informative for understanding plant responses to environments distinct from regulated chambers but is impractical by conventional destructive measurements. We developed PlantServation, a method incorporating robust image-acquisition hardware and...

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Autores principales: Akiyama, Reiko, Goto, Takao, Tameshige, Toshiaki, Sugisaka, Jiro, Kuroki, Ken, Sun, Jianqiang, Akita, Junichi, Hatakeyama, Masaomi, Kudoh, Hiroshi, Kenta, Tanaka, Tonouchi, Aya, Shimahara, Yuki, Sese, Jun, Kutsuna, Natsumaro, Shimizu-Inatsugi, Rie, Shimizu, Kentaro K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10517152/
https://www.ncbi.nlm.nih.gov/pubmed/37737204
http://dx.doi.org/10.1038/s41467-023-41260-3
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author Akiyama, Reiko
Goto, Takao
Tameshige, Toshiaki
Sugisaka, Jiro
Kuroki, Ken
Sun, Jianqiang
Akita, Junichi
Hatakeyama, Masaomi
Kudoh, Hiroshi
Kenta, Tanaka
Tonouchi, Aya
Shimahara, Yuki
Sese, Jun
Kutsuna, Natsumaro
Shimizu-Inatsugi, Rie
Shimizu, Kentaro K.
author_facet Akiyama, Reiko
Goto, Takao
Tameshige, Toshiaki
Sugisaka, Jiro
Kuroki, Ken
Sun, Jianqiang
Akita, Junichi
Hatakeyama, Masaomi
Kudoh, Hiroshi
Kenta, Tanaka
Tonouchi, Aya
Shimahara, Yuki
Sese, Jun
Kutsuna, Natsumaro
Shimizu-Inatsugi, Rie
Shimizu, Kentaro K.
author_sort Akiyama, Reiko
collection PubMed
description Long-term field monitoring of leaf pigment content is informative for understanding plant responses to environments distinct from regulated chambers but is impractical by conventional destructive measurements. We developed PlantServation, a method incorporating robust image-acquisition hardware and deep learning-based software that extracts leaf color by detecting plant individuals automatically. As a case study, we applied PlantServation to examine environmental and genotypic effects on the pigment anthocyanin content estimated from leaf color. We processed >4 million images of small individuals of four Arabidopsis species in the field, where the plant shape, color, and background vary over months. Past radiation, coldness, and precipitation significantly affected the anthocyanin content. The synthetic allopolyploid A. kamchatica recapitulated the fluctuations of natural polyploids by integrating diploid responses. The data support a long-standing hypothesis stating that allopolyploids can inherit and combine the traits of progenitors. PlantServation facilitates the study of plant responses to complex environments termed “in natura”.
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spelling pubmed-105171522023-09-24 Seasonal pigment fluctuation in diploid and polyploid Arabidopsis revealed by machine learning-based phenotyping method PlantServation Akiyama, Reiko Goto, Takao Tameshige, Toshiaki Sugisaka, Jiro Kuroki, Ken Sun, Jianqiang Akita, Junichi Hatakeyama, Masaomi Kudoh, Hiroshi Kenta, Tanaka Tonouchi, Aya Shimahara, Yuki Sese, Jun Kutsuna, Natsumaro Shimizu-Inatsugi, Rie Shimizu, Kentaro K. Nat Commun Article Long-term field monitoring of leaf pigment content is informative for understanding plant responses to environments distinct from regulated chambers but is impractical by conventional destructive measurements. We developed PlantServation, a method incorporating robust image-acquisition hardware and deep learning-based software that extracts leaf color by detecting plant individuals automatically. As a case study, we applied PlantServation to examine environmental and genotypic effects on the pigment anthocyanin content estimated from leaf color. We processed >4 million images of small individuals of four Arabidopsis species in the field, where the plant shape, color, and background vary over months. Past radiation, coldness, and precipitation significantly affected the anthocyanin content. The synthetic allopolyploid A. kamchatica recapitulated the fluctuations of natural polyploids by integrating diploid responses. The data support a long-standing hypothesis stating that allopolyploids can inherit and combine the traits of progenitors. PlantServation facilitates the study of plant responses to complex environments termed “in natura”. Nature Publishing Group UK 2023-09-22 /pmc/articles/PMC10517152/ /pubmed/37737204 http://dx.doi.org/10.1038/s41467-023-41260-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Akiyama, Reiko
Goto, Takao
Tameshige, Toshiaki
Sugisaka, Jiro
Kuroki, Ken
Sun, Jianqiang
Akita, Junichi
Hatakeyama, Masaomi
Kudoh, Hiroshi
Kenta, Tanaka
Tonouchi, Aya
Shimahara, Yuki
Sese, Jun
Kutsuna, Natsumaro
Shimizu-Inatsugi, Rie
Shimizu, Kentaro K.
Seasonal pigment fluctuation in diploid and polyploid Arabidopsis revealed by machine learning-based phenotyping method PlantServation
title Seasonal pigment fluctuation in diploid and polyploid Arabidopsis revealed by machine learning-based phenotyping method PlantServation
title_full Seasonal pigment fluctuation in diploid and polyploid Arabidopsis revealed by machine learning-based phenotyping method PlantServation
title_fullStr Seasonal pigment fluctuation in diploid and polyploid Arabidopsis revealed by machine learning-based phenotyping method PlantServation
title_full_unstemmed Seasonal pigment fluctuation in diploid and polyploid Arabidopsis revealed by machine learning-based phenotyping method PlantServation
title_short Seasonal pigment fluctuation in diploid and polyploid Arabidopsis revealed by machine learning-based phenotyping method PlantServation
title_sort seasonal pigment fluctuation in diploid and polyploid arabidopsis revealed by machine learning-based phenotyping method plantservation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10517152/
https://www.ncbi.nlm.nih.gov/pubmed/37737204
http://dx.doi.org/10.1038/s41467-023-41260-3
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