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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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2018
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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. |
format | Online Article Text |
id | pubmed-5895192 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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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