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 ca...

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Vydané v:	Applications in plant sciences Ročník 6; číslo 3; s. e1031 - n/a
Hlavní autori:	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.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States John Wiley & Sons, Inc 01.03.2018 John Wiley and Sons Inc
Predmet:	Cameras color computer software Computers Data analysis Documentation Image processing imaging low‐cost phenotyping Metadata morphology phenomics phenotype Phenotyping Plant extracts Plant sciences Protocol Protocol Note Protocol Notes raspberries Raspberry Pi Research programs Shutdowns Software species diversity New York United States > US low‐cost phenotyping Raspberry Pi morphology imaging
ISSN:	2168-0450, 2168-0450
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Abstract	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.
AbstractList	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.PREMISE OF THE STUDYImage-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.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.METHODS AND RESULTSWe 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.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.CONCLUSIONSThis 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. 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. 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. 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. 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. Premise of the StudyImage‐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 ResultsWe 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.ConclusionsThis 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. 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.
Author	Tielking, Allison Elizabeth Castillo, S. Tovar, Jose C. Hoyer, J. Steen Lin, Andy Carrington, James C. Gehan, Malia A. Fahlgren, Noah Nusinow, Dmitri A. Tessman, Monica Miller, Michael Callen, Steven T.
AuthorAffiliation	1 Donald Danforth Plant Science Center 975 North Warson Road St. Louis Missouri 63132 USA 2 Computational and Systems Biology Program Washington University in St. Louis One Brookings Drive St. Louis Missouri 63130 USA
AuthorAffiliation_xml	– name: 2 Computational and Systems Biology Program Washington University in St. Louis One Brookings Drive St. Louis Missouri 63130 USA – name: 1 Donald Danforth Plant Science Center 975 North Warson Road St. Louis Missouri 63132 USA
Author_xml	– sequence: 1 givenname: Jose C. surname: Tovar fullname: Tovar, Jose C. organization: Donald Danforth Plant Science Center – sequence: 2 givenname: J. Steen surname: Hoyer fullname: Hoyer, J. Steen organization: Washington University in St. Louis – sequence: 3 givenname: Andy surname: Lin fullname: Lin, Andy organization: Donald Danforth Plant Science Center – sequence: 4 givenname: Allison surname: Tielking fullname: Tielking, Allison organization: Donald Danforth Plant Science Center – sequence: 5 givenname: Steven T. surname: Callen fullname: Callen, Steven T. organization: Donald Danforth Plant Science Center – sequence: 6 givenname: S. surname: Elizabeth Castillo fullname: Elizabeth Castillo, S. organization: Donald Danforth Plant Science Center – sequence: 7 givenname: Michael surname: Miller fullname: Miller, Michael organization: Donald Danforth Plant Science Center – sequence: 8 givenname: Monica surname: Tessman fullname: Tessman, Monica organization: Donald Danforth Plant Science Center – sequence: 9 givenname: Noah surname: Fahlgren fullname: Fahlgren, Noah organization: Donald Danforth Plant Science Center – sequence: 10 givenname: James C. surname: Carrington fullname: Carrington, James C. organization: Donald Danforth Plant Science Center – sequence: 11 givenname: Dmitri A. surname: Nusinow fullname: Nusinow, Dmitri A. organization: Donald Danforth Plant Science Center – sequence: 12 givenname: Malia A. orcidid: 0000-0002-3238-2627 surname: Gehan fullname: Gehan, Malia A. email: mgehan@danforthcenter.org organization: Donald Danforth Plant Science Center
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Snippet	Premise of the Study Image‐based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent... Image-based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy... Premise of the StudyImage‐based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent... PREMISE OF THE STUDY: Image‐based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent...
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SubjectTerms	Cameras color computer software Computers Data analysis Documentation Image processing imaging low‐cost phenotyping Metadata morphology phenomics phenotype Phenotyping Plant extracts Plant sciences Protocol Protocol Note Protocol Notes raspberries Raspberry Pi Research programs Shutdowns Software species diversity
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