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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...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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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”. |
format | Online Article Text |
id | pubmed-10517152 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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