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      rosettR: protocol and software for seedling area and growth analysis

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          Abstract

          Background

          Growth is an important parameter to consider when studying the impact of treatments or mutations on plant physiology. Leaf area and growth rates can be estimated efficiently from images of plants, but the experiment setup, image analysis, and statistical evaluation can be laborious, often requiring substantial manual effort and programming skills.

          Results

          Here we present rosettR, a non-destructive and high-throughput phenotyping protocol for the measurement of total rosette area of seedlings grown in plates in sterile conditions. We demonstrate that our protocol can be used to accurately detect growth differences among different genotypes and in response to light regimes and osmotic stress. rosettR is implemented as a package for the statistical computing software R and provides easy to use functions to design an experiment, analyze the images, and generate reports on quality control as well as a final comparison across genotypes and applied treatments. Experiment procedures are included as part of the package documentation.

          Conclusions

          Using rosettR it is straight-forward to perform accurate, reproducible measurements of rosette area and relative growth rate with high-throughput using inexpensive equipment. Suitable applications include screening mutant populations for growth phenotypes visible at early growth stages and profiling different genotypes in a wide variety of treatments.

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          Most cited references26

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          NIH Image to ImageJ: 25 years of image analysis.

          For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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            Future scenarios for plant phenotyping.

            With increasing demand to support and accelerate progress in breeding for novel traits, the plant research community faces the need to accurately measure increasingly large numbers of plants and plant parameters. The goal is to provide quantitative analyses of plant structure and function relevant for traits that help plants better adapt to low-input agriculture and resource-limited environments. We provide an overview of the inherently multidisciplinary research in plant phenotyping, focusing on traits that will assist in selecting genotypes with increased resource use efficiency. We highlight opportunities and challenges for integrating noninvasive or minimally invasive technologies into screening protocols to characterize plant responses to environmental challenges for both controlled and field experimentation. Although technology evolves rapidly, parallel efforts are still required because large-scale phenotyping demands accurate reporting of at least a minimum set of information concerning experimental protocols, data management schemas, and integration with modeling. The journey toward systematic plant phenotyping has only just begun.
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              EBImage—an R package for image processing with applications to cellular phenotypes

              Summary: EBImage provides general purpose functionality for reading, writing, processing and analysis of images. Furthermore, in the context of microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and use of existing tools in the R environment for signal processing, statistical modeling, machine learning and data visualization. Availability: EBImage is free and open source, released under the LGPL license and available from the Bioconductor project (http://www.bioconductor.org/packages/release/bioc/html/EBImage.html). Contact: gregoire.pau@ebi.ac.uk
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                Author and article information

                Contributors
                tome@mpipz.mpg.de
                karel.jansseune@bayer.com
                bernadette.saey@bayer.com
                jack.grundy@warwick.ac.uk
                korneel.vandenbroucke@bayer.com
                matthew.hannah@bayer.com
                henred@biosustain.dtu.dk
                Journal
                Plant Methods
                Plant Methods
                Plant Methods
                BioMed Central (London )
                1746-4811
                15 March 2017
                15 March 2017
                2017
                : 13
                : 13
                Affiliations
                [1 ]GRID grid.423974.f, , Bayer CropScience NV, ; Technologiepark 38, 9052 Ghent, Belgium
                [2 ]ISNI 0000 0001 0660 6765, GRID grid.419498.9, , Max Planck Institute for Plant Breeding Research, ; 50829 Cologne, Germany
                [3 ] Cluster of Excellence on Plant Sciences “From Complex Traits towards Synthetic Modules”, 40225 Düsseldorf, Germany
                [4 ]ISNI 0000 0001 2181 8870, GRID grid.5170.3, , DTU Biosustain, ; Kemitorvet, Building 220, 2800 Kgs. Lyngby, Denmark
                [5 ]ISNI 0000 0000 8809 1613, GRID grid.7372.1, School of Life Sciences, , University of Warwick, ; Coventry, CV4 7AL UK
                Author information
                http://orcid.org/0000-0001-8559-5274
                Article
                163
                10.1186/s13007-017-0163-9
                5353781
                d977d462-5521-4269-b29f-162f323df2fa
                © The Author(s) 2017

                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.

                History
                : 23 May 2016
                : 5 March 2017
                Funding
                Funded by: EU Project MERIT
                Award ID: 264474
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100008791, Bayer CropScience;
                Categories
                Methodology
                Custom metadata
                © The Author(s) 2017

                Plant science & Botany
                phenotyping,r,growth,leaf area,image analysis
                Plant science & Botany
                phenotyping, r, growth, leaf area, image analysis

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