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A low-cost and open-source platform for automated imaging

BACKGROUND: Remote monitoring of plants using hyperspectral imaging has become an important tool for the study of plant growth, development, and physiology. Many applications are oriented towards use in field environments to enable non-destructive analysis of crop responses due to factors such as dr...

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Autores principales: Lien, Max R., Barker, Richard J., Ye, Zhiwei, Westphall, Matthew H., Gao, Ruohan, Singh, Aditya, Gilroy, Simon, Townsend, Philip A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6348682/
https://www.ncbi.nlm.nih.gov/pubmed/30705688
http://dx.doi.org/10.1186/s13007-019-0392-1
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author Lien, Max R.
Barker, Richard J.
Ye, Zhiwei
Westphall, Matthew H.
Gao, Ruohan
Singh, Aditya
Gilroy, Simon
Townsend, Philip A.
author_facet Lien, Max R.
Barker, Richard J.
Ye, Zhiwei
Westphall, Matthew H.
Gao, Ruohan
Singh, Aditya
Gilroy, Simon
Townsend, Philip A.
author_sort Lien, Max R.
collection PubMed
description BACKGROUND: Remote monitoring of plants using hyperspectral imaging has become an important tool for the study of plant growth, development, and physiology. Many applications are oriented towards use in field environments to enable non-destructive analysis of crop responses due to factors such as drought, nutrient deficiency, and disease, e.g., using tram, drone, or airplane mounted instruments. The field setting introduces a wide range of uncontrolled environmental variables that make validation and interpretation of spectral responses challenging, and as such lab- and greenhouse-deployed systems for plant studies and phenotyping are of increasing interest. In this study, we have designed and developed an open-source, hyperspectral reflectance-based imaging system for lab-based plant experiments: the HyperScanner. The reliability and accuracy of HyperScanner were validated using drought and salt stress experiments with Arabidopsis thaliana. RESULTS: A robust, scalable, and reliable system was created. The system was built using open-sourced parts, and all custom parts, operational methods, and data have been made publicly available in order to maintain the open-source aim of HyperScanner. The gathered reflectance images showed changes in narrowband red and infrared reflectance spectra for each of the stress tests that was evident prior to other visual physiological responses and exhibited congruence with measurements using full-range contact spectrometers. CONCLUSIONS: HyperScanner offers the potential for reliable and inexpensive laboratory hyperspectral imaging systems. HyperScanner was able to quickly collect accurate reflectance curves on a variety of plant stress experiments. The resulting images showed spectral differences in plants shortly after application of a treatment but before visual manifestation. HyperScanner increases the capacity for spectroscopic and imaging-based analytical tools by providing more access to hyperspectral analyses in the laboratory setting. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13007-019-0392-1) contains supplementary material, which is available to authorized users.
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spelling pubmed-63486822019-01-31 A low-cost and open-source platform for automated imaging Lien, Max R. Barker, Richard J. Ye, Zhiwei Westphall, Matthew H. Gao, Ruohan Singh, Aditya Gilroy, Simon Townsend, Philip A. Plant Methods Methodology BACKGROUND: Remote monitoring of plants using hyperspectral imaging has become an important tool for the study of plant growth, development, and physiology. Many applications are oriented towards use in field environments to enable non-destructive analysis of crop responses due to factors such as drought, nutrient deficiency, and disease, e.g., using tram, drone, or airplane mounted instruments. The field setting introduces a wide range of uncontrolled environmental variables that make validation and interpretation of spectral responses challenging, and as such lab- and greenhouse-deployed systems for plant studies and phenotyping are of increasing interest. In this study, we have designed and developed an open-source, hyperspectral reflectance-based imaging system for lab-based plant experiments: the HyperScanner. The reliability and accuracy of HyperScanner were validated using drought and salt stress experiments with Arabidopsis thaliana. RESULTS: A robust, scalable, and reliable system was created. The system was built using open-sourced parts, and all custom parts, operational methods, and data have been made publicly available in order to maintain the open-source aim of HyperScanner. The gathered reflectance images showed changes in narrowband red and infrared reflectance spectra for each of the stress tests that was evident prior to other visual physiological responses and exhibited congruence with measurements using full-range contact spectrometers. CONCLUSIONS: HyperScanner offers the potential for reliable and inexpensive laboratory hyperspectral imaging systems. HyperScanner was able to quickly collect accurate reflectance curves on a variety of plant stress experiments. The resulting images showed spectral differences in plants shortly after application of a treatment but before visual manifestation. HyperScanner increases the capacity for spectroscopic and imaging-based analytical tools by providing more access to hyperspectral analyses in the laboratory setting. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13007-019-0392-1) contains supplementary material, which is available to authorized users. BioMed Central 2019-01-28 /pmc/articles/PMC6348682/ /pubmed/30705688 http://dx.doi.org/10.1186/s13007-019-0392-1 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology
Lien, Max R.
Barker, Richard J.
Ye, Zhiwei
Westphall, Matthew H.
Gao, Ruohan
Singh, Aditya
Gilroy, Simon
Townsend, Philip A.
A low-cost and open-source platform for automated imaging
title A low-cost and open-source platform for automated imaging
title_full A low-cost and open-source platform for automated imaging
title_fullStr A low-cost and open-source platform for automated imaging
title_full_unstemmed A low-cost and open-source platform for automated imaging
title_short A low-cost and open-source platform for automated imaging
title_sort low-cost and open-source platform for automated imaging
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6348682/
https://www.ncbi.nlm.nih.gov/pubmed/30705688
http://dx.doi.org/10.1186/s13007-019-0392-1
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