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Automatic cell identification and counting of leaf epidermis for plant phenotyping

The counting of leaf epidermal cells is useful to study the plant developmental changes produced by environmental or genetic factors. The scanning electron microscopy can be used, but it is expensive and time-consuming. Methods using optical microscopy are also available, but they still require leav...

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Autores principales: Mele, Giacomo, Gargiulo, Laura
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7132086/
https://www.ncbi.nlm.nih.gov/pubmed/32274336
http://dx.doi.org/10.1016/j.mex.2020.100860
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author Mele, Giacomo
Gargiulo, Laura
author_facet Mele, Giacomo
Gargiulo, Laura
author_sort Mele, Giacomo
collection PubMed
description The counting of leaf epidermal cells is useful to study the plant developmental changes produced by environmental or genetic factors. The scanning electron microscopy can be used, but it is expensive and time-consuming. Methods using optical microscopy are also available, but they still require leaves pre-treatment and manual cell identification. We propose a quick and simple method for counting leaf epidermal cells without leaf treatments and based on automated cell identification and marking. It allows to highly improve the representativeness of leaf epidermis screening, aiming at a high-throughput plant phenotyping approach. • The leaves are pressed between two glass slides without any pre-treatment and digital micrographs are acquired under incident light. • Epidermal cells are automatically identified and counted by means of a “macro" of ImageJ; • The cell count obtained applying the procedure of image processing is very close to that obtainable by manual cell identification.
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spelling pubmed-71320862020-04-09 Automatic cell identification and counting of leaf epidermis for plant phenotyping Mele, Giacomo Gargiulo, Laura MethodsX Agricultural and Biological Science The counting of leaf epidermal cells is useful to study the plant developmental changes produced by environmental or genetic factors. The scanning electron microscopy can be used, but it is expensive and time-consuming. Methods using optical microscopy are also available, but they still require leaves pre-treatment and manual cell identification. We propose a quick and simple method for counting leaf epidermal cells without leaf treatments and based on automated cell identification and marking. It allows to highly improve the representativeness of leaf epidermis screening, aiming at a high-throughput plant phenotyping approach. • The leaves are pressed between two glass slides without any pre-treatment and digital micrographs are acquired under incident light. • Epidermal cells are automatically identified and counted by means of a “macro" of ImageJ; • The cell count obtained applying the procedure of image processing is very close to that obtainable by manual cell identification. Elsevier 2020-03-14 /pmc/articles/PMC7132086/ /pubmed/32274336 http://dx.doi.org/10.1016/j.mex.2020.100860 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Agricultural and Biological Science
Mele, Giacomo
Gargiulo, Laura
Automatic cell identification and counting of leaf epidermis for plant phenotyping
title Automatic cell identification and counting of leaf epidermis for plant phenotyping
title_full Automatic cell identification and counting of leaf epidermis for plant phenotyping
title_fullStr Automatic cell identification and counting of leaf epidermis for plant phenotyping
title_full_unstemmed Automatic cell identification and counting of leaf epidermis for plant phenotyping
title_short Automatic cell identification and counting of leaf epidermis for plant phenotyping
title_sort automatic cell identification and counting of leaf epidermis for plant phenotyping
topic Agricultural and Biological Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7132086/
https://www.ncbi.nlm.nih.gov/pubmed/32274336
http://dx.doi.org/10.1016/j.mex.2020.100860
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