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      HTPheno: An image analysis pipeline for high-throughput plant phenotyping

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          Abstract

          Background

          In the last few years high-throughput analysis methods have become state-of-the-art in the life sciences. One of the latest developments is automated greenhouse systems for high-throughput plant phenotyping. Such systems allow the non-destructive screening of plants over a period of time by means of image acquisition techniques. During such screening different images of each plant are recorded and must be analysed by applying sophisticated image analysis algorithms.

          Results

          This paper presents an image analysis pipeline (HTPheno) for high-throughput plant phenotyping. HTPheno is implemented as a plugin for ImageJ, an open source image processing software. It provides the possibility to analyse colour images of plants which are taken in two different views (top view and side view) during a screening. Within the analysis different phenotypical parameters for each plant such as height, width and projected shoot area of the plants are calculated for the duration of the screening. HTPheno is applied to analyse two barley cultivars.

          Conclusions

          HTPheno, an open source image analysis pipeline, supplies a flexible and adaptable ImageJ plugin which can be used for automated image analysis in high-throughput plant phenotyping and therefore to derive new biological insights, such as determination of fitness.

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          Most cited references4

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          PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit.

          The high-throughput phenotypic analysis of Arabidopsis thaliana collections requires methodological progress and automation. Methods to impose stable and reproducible soil water deficits are presented and were used to analyse plant responses to water stress. Several potential complications and methodological difficulties were identified, including the spatial and temporal variability of micrometeorological conditions within a growth chamber, the difference in soil water depletion rates between accessions and the differences in developmental stage of accessions the same time after sowing. Solutions were found. Nine accessions were grown in four experiments in a rigorously controlled growth-chamber equipped with an automated system to control soil water content and take pictures of individual plants. One accession, An1, was unaffected by water deficit in terms of leaf number, leaf area, root growth and transpiration rate per unit leaf area. Methods developed here will help identify quantitative trait loci and genes involved in plant tolerance to water deficit.
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            Visualization of image data from cells to organisms.

            Advances in imaging techniques and high-throughput technologies are providing scientists with unprecedented possibilities to visualize internal structures of cells, organs and organisms and to collect systematic image data characterizing genes and proteins on a large scale. To make the best use of these increasingly complex and large image data resources, the scientific community must be provided with methods to query, analyze and crosslink these resources to give an intuitive visual representation of the data. This review gives an overview of existing methods and tools for this purpose and highlights some of their limitations and challenges.
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              Dynamics of seedling growth acclimation towards altered light conditions can be quantified via GROWSCREEN: a setup and procedure designed for rapid optical phenotyping of different plant species.

              Using a novel setup, we assessed how fast growth of Nicotiana tabacum seedlings responds to alterations in the light regime and investigated whether starch-free mutants of Arabidopsis thaliana show decreased growth potential at an early developmental stage. Leaf area and relative growth rate were measured based on pictures from a camera automatically placed above an array of 120 seedlings. Detection of total seedling leaf area was performed via global segmentation of colour images for preset thresholds of the parameters hue, saturation and value. Dynamic acclimation of relative growth rate towards altered light conditions occurred within 1 d in N. tabacum exposed to high nutrient availability, but not in plants exposed to low nutrient availability. Increased leaf area was correlated with an increase in shoot fresh and dry weight as well as root growth in N. tabacum. Relative growth rate was shown to be a more appropriate parameter than leaf area for detection of dynamic growth acclimation. Clear differences in leaf growth activity were also observed for A. thaliana. As growth responses are generally most flexible in early developmental stages, the procedure described here is an important step towards standardized protocols for rapid detection of the effects of changes in internal (genetic) and external (environmental) parameters regulating plant growth.
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                Author and article information

                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central
                1471-2105
                2011
                12 May 2011
                : 12
                : 148
                Affiliations
                [1 ]Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstrasse 3, 06466 Gatersleben, Germany
                [2 ]Martin Luther University Halle-Wittenberg, Institute of Computer Science, Von-Seckendor-Platz 1, 06120 Halle, Germany
                Article
                1471-2105-12-148
                10.1186/1471-2105-12-148
                3113939
                21569390
                ada09130-9dbc-43e4-abfe-50cd8fe5d9d5
                Copyright ©2011 Hartmann et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 29 October 2010
                : 12 May 2011
                Categories
                Software

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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