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      Dealing with multi‐source and multi‐scale information in plant phenomics: the ontology‐driven Phenotyping Hybrid Information System

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Summary

          • Phenomic datasets need to be accessible to the scientific community. Their reanalysis requires tracing relevant information on thousands of plants, sensors and events.

          • The open‐source Phenotyping Hybrid Information System ( PHIS) is proposed for plant phenotyping experiments in various categories of installations (field, glasshouse). It unambiguously identifies all objects and traits in an experiment and establishes their relations via ontologies and semantics that apply to both field and controlled conditions. For instance, the genotype is declared for a plant or plot and is associated with all objects related to it. Events such as successive plant positions, anomalies and annotations are associated with objects so they can be easily retrieved.

          • Its ontology‐driven architecture is a powerful tool for integrating and managing data from multiple experiments and platforms, for creating relationships between objects and enriching datasets with knowledge and metadata. It interoperates with external resources via web services, thereby allowing data integration into other systems; for example, modelling platforms or external databases.

          • It has the potential for rapid diffusion because of its ability to integrate, manage and visualize multi‐source and multi‐scale data, but also because it is based on 10 yr of trial and error in our groups.

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          Most cited references 53

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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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            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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              OWL 2: The next step for OWL

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                Author and article information

                Contributors
                llorenc.cabrera-bosquet@inra.fr
                Journal
                New Phytol
                New Phytol
                10.1111/(ISSN)1469-8137
                NPH
                The New Phytologist
                John Wiley and Sons Inc. (Hoboken )
                0028-646X
                1469-8137
                28 August 2018
                January 2019
                : 221
                : 1 ( doiID: 10.1111/nph.2019.221.issue-1 )
                : 588-601
                Affiliations
                [ 1 ] MISTEA, INRA, Montpellier SupAgro, Université de Montpellier Montpellier 34060 France
                [ 2 ] LEPSE, INRA, Montpellier SupAgro, Université de Montpellier Montpellier 34060 France
                [ 3 ] UE DIASCOPE, INRA, Montpellier SupAgro, Université de Montpellier Montpellier 34060 France
                [ 4 ] INRA, UR1164 URGI – Research Unit in Genomics‐Info INRA de Versailles‐Grignon Route de Saint‐Cyr Versailles 78026 France
                Author notes
                [* ] Author for correspondence:

                Llorenç Cabrera‐Bosquet

                Tel: +33 499 612 956

                Email: llorenc.cabrera-bosquet@ 123456inra.fr

                Article
                NPH15385 2018-27247
                10.1111/nph.15385
                6585972
                30152011
                © 2018 INRA New Phytologist © 2018 New Phytologist Trust

                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.

                Page count
                Figures: 9, Tables: 0, Pages: 14, Words: 9742
                Product
                Funding
                Funded by: Agence Nationale de la Recherche
                Award ID: ANR‐11‐INBS‐0012
                Categories
                Method
                Research
                Methods
                Custom metadata
                2.0
                nph15385
                January 2019
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.6.4 mode:remove_FC converted:20.06.2019

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