A. F. Adam-blondon, M. Alaux, C. Pommier, D. Cantu, Z. M. Cheng et al., Towards an open grapevine information system, Horticulture Research, vol.3, p.16056, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01404714

A. Alercia, S. Diulgheroff, and M. Mackay, FAO/bioversity multi-crop passport descriptors V. 2.1 [MCPD V. 2.1, 2015.

, New Phytologist Trust www.newphytologist.com, 2018.

A. Prado, S. Cabrera-bosquet, L. Grau, A. Coupel-ledru, A. Millet et al., Phenomics allows identification of genomic regions affecting maize stomatal conductance with conditional effects of water deficit and evaporative demand, Plant, Cell & Environment, vol.41, pp.314-326, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02621284

D. Arend, A. Junker, U. Scholz, D. Schuler, J. Wylie et al., PGP repository: a plant phenomics and genomics data publication infrastructure, Database: The Journal of Biological Databases and Curation, vol.2016, pp.1-10, 2016.

D. Arend, M. Lange, J. Chen, C. Colmsee, S. Flemming et al., e!DAL -a framework to store, share and publish research data, BMC Bioinformatics, vol.15, p.214, 2014.

A. Bandrowski, R. Brinkman, M. Brochhausen, M. H. Brush, B. Bug et al., The ontology for biomedical investigations, PLoS ONE, vol.11, p.154556, 2016.

A. Bernal-vasquez, H. Utz, and H. Piepho, Outlier detection methods for generalized lattices: a case study on the transition from ANOVA to REML, Theoretical and Applied Genetics, vol.129, pp.787-804, 2016.

T. Berners-lee, J. Hendler, and O. Lassila, The semantic web, Scientific American, vol.284, pp.34-43, 2001.

N. Brichet, C. Fournier, O. Turc, O. Strauss, S. Artzet et al., A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform, Plant Methods, vol.13, p.96, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01631186

D. Bustos-korts, M. Malosetti, S. Chapman, and F. Van-eeuwijk, Modelling of genotype by environment interaction and prediction of complex traits across multiple environments as a synthesis of crop growth modelling, genetics and statistics, Crop systems biology: narrowing the gaps between crop modelling and genetics, pp.55-82, 2016.

L. Cabrera-bosquet, J. Crossa, J. Von-zitzewitz, M. D. Serret, and J. L. Araus, High-throughput phenotyping and genomic selection: the frontiers of crop breeding converge, Journal of Integrative Plant Biology, vol.54, pp.312-320, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01001607

L. Cabrera-bosquet, C. Fournier, N. Brichet, C. Welcker, B. Suard et al., High-throughput estimation of incident light, light interception and radiation-use efficiency of thousands of plants in a phenotyping platform, New Phytologist, vol.212, pp.269-281, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01229560

C. Caracciolo, A. Stellato, A. Morshed, G. Johannsen, S. Rajbhandari et al., The AGROVOC linked dataset. Semantic Web, vol.4, pp.341-348, 2013.

L. Cooper, A. Meier, M. A. Laporte, J. L. Elser, C. Mungall et al., The Planteome database: an integrated resource for reference ontologies, plant genomics and phenomics, Nucleic Acids Research, vol.46, pp.1168-1180, 2018.

L. Cooper, R. L. Walls, J. Elser, M. A. Gandolfo, D. W. Stevenson et al., The plant ontology as a tool for comparative plant anatomy and genomic analyses, Plant and Cell Physiology, vol.54, p.1, 2013.

F. Coppens, N. Wuyts, D. Inz-e, and S. Dhondt, Unlocking the potential of plant phenotyping data through integration and data-driven approaches, Current Opinion in Systems Biology, vol.4, pp.58-63, 2017.

A. Coupel-ledru, E. Lebon, C. A. Gallo, A. Gago, P. Pantin et al., Reduced nighttime transpiration is a relevant breeding target for high water-use efficiency in grapevine, Proceedings of the National Academy of Sciences, vol.113, pp.8963-8968, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01350492

H. Cwiek-kupczy-nska, T. Altmann, D. Arend, E. Arnaud, D. Chen et al., Measures for interoperability of phenotypic data: minimum information requirements and formatting, Plant Methods, vol.12, p.44, 2016.

F. A. Van-eeuwijk, M. Bink, K. Chenu, and S. C. Chapman, Detection and use of QTL for complex traits in multiple environments, Current Opinion in Plant Biology, vol.13, pp.193-205, 2010.

J. Fabre, M. Dauzat, V. Negre, N. Wuyts, A. Tireau et al., PHENOPSIS DB: an information system for Arabidopsis thaliana phenotypic data in an environmental context, BMC Plant Biology, vol.11, p.77, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01018993

F. Fiorani and U. Schurr, Future scenarios for plant phenotyping, Annual Review of Plant Biology, vol.64, pp.267-291, 2013.

R. T. Furbank and M. Tester, Phenomics -technologies to relieve the phenotyping bottleneck, Trends in Plant Science, vol.16, pp.635-644, 2011.

G. V. Gkoutos, P. N. Schofield, and R. Hoehndorf, The anatomy of phenotype ontologies: principles, properties and applications, Briefings in Bioinformatics, p.35, 2017.

C. Granier, L. Aguirrezabal, K. Chenu, S. J. Cookson, M. Dauzat et al., 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, New Phytologist, vol.169, pp.623-635, 2006.

B. C. Grau, I. Horrocks, B. Motik, B. Parsia, P. Patel-schneider et al., OWL 2: the next step for OWL, Journal of Web Semantics, vol.6, pp.309-322, 2008.

D. K. Großkinsky, S. J. Syaifullah, and T. Roitsch, Integration of multi-omics techniques and physiological phenotyping within a holistic phenomics approach to study senescence in model and crop plants, Journal of Experimental Botany, vol.69, pp.825-844, 2017.

M. Halewood, T. Chiurugwi, S. Hamilton, R. Kurtz, B. Marden et al., Plant genetic resources for food and agriculture: opportunities and challenges emerging from the science and information technology revolution, New Phytologist, vol.217, pp.1407-1419, 2018.

J. Hannemann, H. Poorter, B. Usadel, O. E. Blaesing, A. Finck et al., Xeml Lab: a tool that supports the design of experiments at a graphical interface and generates computer-readable metadata files, which capture information about genotypes, growth conditions, environmental perturbations and sampling strategy, Plant, Cell & Environment, vol.32, pp.1185-1200, 2009.

K. Ilic, E. A. Kellogg, P. Jaiswal, F. Zapata, P. F. Stevens et al., The plant structure ontology, a unified vocabulary of anatomy and morphology of a flowering plant, Plant Physiology, vol.143, pp.587-599, 2007.

A. R. Jones, M. Miller, R. Aebersold, R. Apweiler, C. A. Ball et al., The Functional Genomics Experiment model (FuGE): an extensible framework for standards in functional genomics, Nature Biotechnology, vol.25, pp.1127-1133, 2007.

C. Jonquet, A. Toulet, E. Arnaud, S. Aubin, E. Dzal-e-yeumo et al., AgroPortal: a vocabulary and ontology repository for agronomy, Computers and Electronics in Agriculture, vol.144, pp.126-143, 2018.
URL : https://hal.archives-ouvertes.fr/lirmm-01679502

A. Junker, M. M. Muraya, K. Weigelt-fischer, F. Arana-ceballos, C. Klukas et al., Optimizing experimental procedures for quantitative evaluation of crop plant performance in high throughput phenotyping systems, Frontiers in Plant Science, vol.5, p.770, 2014.

D. I. Kalogiros, M. O. Adu, P. J. White, M. R. Broadley, X. Draye et al., Analysis of root growth from a phenotyping data set using a density-based model, Journal of Experimental Botany, vol.67, pp.1045-1058, 2016.

C. Klukas, D. Chen, and J. Pape, Integrated analysis platform: an open-source information system for high-throughput plant phenotyping, Plant Physiology, vol.165, pp.506-518, 2014.

P. Krajewski, D. Chen, H. Cwiek, A. Van-dijk, F. Fiorani et al., Towards recommendations for metadata and data handling in plant phenotyping, Journal of Experimental Botany, vol.66, pp.5417-5427, 2015.
URL : https://hal.archives-ouvertes.fr/hal-02636470

S. Lacube, C. Fournier, C. Palaffre, E. J. Millet, F. Tardieu et al., Distinct controls of leaf widening and elongation by light and evaporative demand in maize, Plant, Cell & Environment, vol.40, pp.2017-2028, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01548684

L. Ngoc, L. Tireau, A. Venkatesan, A. Neveu, P. Larmande et al., Development of a knowledge system for Big Data: case study to plant phenotyping data, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01411565

, WIMS'16 Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics

S. Leonelli, R. P. Davey, E. Arnaud, G. Parry, and R. Bastow, Data management and best practice for plant science, Nature Plants, vol.3, p.17086, 2017.

Y. Li, G. Kennedy, F. Ngoran, P. Wu, and J. Hunter, An ontology-centric architecture for extensible scientific data management systems, Future Generation Computer Systems, vol.29, pp.641-653, 2013.

, New Phytologist Trust New Phytologist, 2018.

S. Mairhofer, S. Zappala, S. Tracy, C. Sturrock, M. J. Bennett et al., Recovering complete plant root system architectures from soil via X-ray l-computed tomography, Plant Methods, vol.9, p.8, 2013.

M. Malosetti, J. Ribaut, and F. A. Van-eeuwijk, The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis, Frontiers in Physiology, vol.4, p.44, 2013.

C. Massonnet, D. Vile, J. Fabre, M. A. Hannah, C. Caldana et al., Probing the reproducibility of leaf growth and molecular phenotypes: a comparison of three Arabidopsis accessions cultivated in ten laboratories, Plant Physiology, vol.152, pp.2142-2157, 2010.
URL : https://hal.archives-ouvertes.fr/hal-01203861

A. Miles and S. Bechhofer, SKOS simple knowledge organization system reference. W3C recommendation, vol.18, p.3, 2009.

K. A. Nagel, A. Putz, F. Gilmer, K. Heinz, A. Fischbach et al., GROWSCREEN-Rhizo is a novel phenotyping robot enabling simultaneous measurements of root and shoot growth for plants grown in soil-filled rhizotrons, Functional Plant Biology, vol.39, p.891, 2012.

B. Negin and M. Moshelion, The advantages of functional phenotyping in prefield screening for drought-tolerant crops, Functional Plant Biology, vol.44, pp.107-118, 2016.

H. Poorter, F. Fiorani, R. Pieruschka, T. Wojciechowski, W. H. Van-der-putten et al., Pampered inside, pestered outside? Differences and similarities between plants growing in controlled conditions and in the field, New Phytologist, vol.212, pp.838-855, 2016.

C. Pradal, S. Artzet, J. Chopard, D. Dupuis, C. Fournier et al., InfraPhenoGrid: a scientific workflow infrastructure for plant phenomics on the Grid, Future Generation Computer Systems, vol.67, pp.341-353, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01336655

C. Pradal, S. Dufour-kowalski, F. Boudon, C. Fournier, and C. Godin, OpenAlea: a visual programming and component-based software platform for plant modelling, Functional Plant Biology, vol.35, pp.751-760, 2008.

C. Pradal, C. Fournier, P. Valduriez, and S. Cohen-boulakia, OpenAlea: scientific workflows combining data analysis and simulation, 27th International Conference on Scientific and Statistical Database Management (SSDBM 2015), 2015.
URL : https://hal.archives-ouvertes.fr/hal-01166298

. R-core-team, R: a language and environment for statistical computing, R Foundation for Statistical Computing, 2015.

A. Rajasekar, R. Moore, C. Hou, C. A. Lee, R. Marciano et al., iRODS Primer: integrated ruleoriented data system, Synthesis Lectures on Information Concepts, Retrieval, and Services, vol.2, pp.1-143, 2010.

G. J. Rebetzke, J. A. Jimenez-berni, W. D. Bovill, D. M. Deery, and R. A. James, Highthroughput phenotyping technologies allow accurate selection of stay-green, Journal of Experimental Botany, vol.67, pp.4919-4924, 2016.

M. Reymond, B. Muller, A. Leonardi, A. Charcosset, and F. Tardieu, Combining quantitative trait loci analysis and an ecophysiological model to analyze the genetic variability of the responses of maize leaf growth to temperature and water deficit, Plant Physiology, vol.131, pp.664-675, 2003.

W. Sadok, P. Naudin, P. Hamard, C. Welcker, B. Muller et al., A phenotyping set up for the analysis of the genetic variablity of the response of leaf growth to water deficit, Comparative Biochemistry and Physiology -Part A: Molecular & Integrative Physiology, vol.141, p.313, 2005.

A. Salehi, J. Jimenez-berni, D. M. Deery, D. Palmer, E. Holland et al., SensorDB: a virtual laboratory for the integration, visualization and analysis of varied biological sensor data, Plant Methods, vol.11, p.53, 2015.

R. Shrestha, L. Matteis, M. Skofic, A. Portugal, G. Mclaren et al., Bridging the phenotypic and genetic data useful for integrated breeding through a data annotation using the Crop Ontology developed by the crop communities of practice, Frontiers in Physiology, vol.3, p.326, 2012.

B. Smith, W. Ceusters, B. Klagges, J. K?-ohler, A. Kumar et al., Relations in biomedical ontologies, Genome Biology, vol.6, p.46, 2005.

D. Steinbach, M. Alaux, J. Amselem, N. Choisne, S. Durand et al., GnpIS: an information system to integrate genetic and genomic data from plants and fungi, Database: The Journal of Biological Databases and Curation, p.58, 2013.

F. Tardieu, L. Cabrera-bosquet, T. Pridmore, and M. Bennett, Plant phenomics, from sensors to knowledge, Current Biology, vol.27, pp.770-783, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01608414

F. Tardieu, T. Simonneau, and B. Muller, The physiological basis of drought tolerance in crop plants: a scenario-dependent probabilistic approach, Annual Review of Plant Biology, vol.69, pp.733-759, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02626143

R. L. Walls, B. Athreya, L. Cooper, J. Elser, M. A. Gandolfo et al., Ontologies as integrative tools for plant science, American Journal of Botany, vol.99, pp.1263-1275, 2012.

M. D. Wilkinson, M. Dumontier, I. J. Aalbersberg, G. Appleton, M. Axton et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, vol.3, 2016.

E. D. Yeumo, M. Alaux, E. Arnaud, S. Aubin, U. Baumann et al., Developing data interoperability using standards: a wheat community use case, vol.6, p.1843, 1000.
URL : https://hal.archives-ouvertes.fr/lirmm-01652022