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Raspberry Pi–powered imaging for plant phenotyping

PREMISE OF THE STUDY: Image‐based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost‐prohibitive. To make high‐throughput phenotyping methods more accessible, low‐cost microcomputers and c...

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Detalles Bibliográficos
Autores principales: Tovar, Jose C., Hoyer, J. Steen, Lin, Andy, Tielking, Allison, Callen, Steven T., Elizabeth Castillo, S., Miller, Michael, Tessman, Monica, Fahlgren, Noah, Carrington, James C., Nusinow, Dmitri A., Gehan, Malia A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5895192/
https://www.ncbi.nlm.nih.gov/pubmed/29732261
http://dx.doi.org/10.1002/aps3.1031
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author Tovar, Jose C.
Hoyer, J. Steen
Lin, Andy
Tielking, Allison
Callen, Steven T.
Elizabeth Castillo, S.
Miller, Michael
Tessman, Monica
Fahlgren, Noah
Carrington, James C.
Nusinow, Dmitri A.
Gehan, Malia A.
author_facet Tovar, Jose C.
Hoyer, J. Steen
Lin, Andy
Tielking, Allison
Callen, Steven T.
Elizabeth Castillo, S.
Miller, Michael
Tessman, Monica
Fahlgren, Noah
Carrington, James C.
Nusinow, Dmitri A.
Gehan, Malia A.
author_sort Tovar, Jose C.
collection PubMed
description PREMISE OF THE STUDY: Image‐based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost‐prohibitive. To make high‐throughput phenotyping methods more accessible, low‐cost microcomputers and cameras can be used to acquire plant image data. METHODS AND RESULTS: We used low‐cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi–controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (e.g., shape, area, height, color) en masse using open‐source image processing software such as PlantCV. CONCLUSIONS: This protocol describes three low‐cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open‐source image processing tools, these imaging platforms provide viable low‐cost solutions for incorporating high‐throughput phenomics into a wide range of research programs.
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spelling pubmed-58951922018-05-04 Raspberry Pi–powered imaging for plant phenotyping Tovar, Jose C. Hoyer, J. Steen Lin, Andy Tielking, Allison Callen, Steven T. Elizabeth Castillo, S. Miller, Michael Tessman, Monica Fahlgren, Noah Carrington, James C. Nusinow, Dmitri A. Gehan, Malia A. Appl Plant Sci Protocol Notes PREMISE OF THE STUDY: Image‐based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost‐prohibitive. To make high‐throughput phenotyping methods more accessible, low‐cost microcomputers and cameras can be used to acquire plant image data. METHODS AND RESULTS: We used low‐cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi–controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (e.g., shape, area, height, color) en masse using open‐source image processing software such as PlantCV. CONCLUSIONS: This protocol describes three low‐cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open‐source image processing tools, these imaging platforms provide viable low‐cost solutions for incorporating high‐throughput phenomics into a wide range of research programs. John Wiley and Sons Inc. 2018-03-31 /pmc/articles/PMC5895192/ /pubmed/29732261 http://dx.doi.org/10.1002/aps3.1031 Text en Applications in Plant Sciences is published by Wiley Periodicals, Inc. on behalf of the Botanical Society of America This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Protocol Notes
Tovar, Jose C.
Hoyer, J. Steen
Lin, Andy
Tielking, Allison
Callen, Steven T.
Elizabeth Castillo, S.
Miller, Michael
Tessman, Monica
Fahlgren, Noah
Carrington, James C.
Nusinow, Dmitri A.
Gehan, Malia A.
Raspberry Pi–powered imaging for plant phenotyping
title Raspberry Pi–powered imaging for plant phenotyping
title_full Raspberry Pi–powered imaging for plant phenotyping
title_fullStr Raspberry Pi–powered imaging for plant phenotyping
title_full_unstemmed Raspberry Pi–powered imaging for plant phenotyping
title_short Raspberry Pi–powered imaging for plant phenotyping
title_sort raspberry pi–powered imaging for plant phenotyping
topic Protocol Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5895192/
https://www.ncbi.nlm.nih.gov/pubmed/29732261
http://dx.doi.org/10.1002/aps3.1031
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