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Dernière mise à jour : Mai 2018

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Phenome-french plant phenotyping network

Publications

Liste complète des publications du projet par an

2021

  1. Affortit et al. High-throughput phenotyping reveals a link between transpiration efficiency and transpiration restriction under high evaporative demand and new loci controlling water use-related traits in African rice, Oryza glaberrima Steud. BioArXiv https://doi.org/10.1101/2021.11.28.470237
  2. Ancín M, Larraya L, Florez-Sarasa I, Bénard C, Fernández-San Millán A, Veramendi J, Gibon Y, Fernie AR, Aranjuelo I, Farran I (2021) Overexpression of thioredoxin m in chloroplasts alters carbon and nitrogen partitioning in tobacco. J. Exp. Bot 72: 4949-4964. doi:10.1093/jxb/erab193
  3. Balliau T, Durufle H, Blanchet N, Blein-Nicolas M, Langlade NB, Zivy M: Proteomic data from leaves of twenty-four sunflower genotypes under water deficit. Ocl-Oilseeds and Fats Crops and Lipids 2021, 28.
  4. Bengoa Luoni SA, Cenci A, Moschen S, Nicosia S, Radonic LM, Sabio y Garcia J, Carrère S, Langlade NB, Vile D, Vazquez Rovere C and Fernandez P. 2021. Genome-Wide analysis of NAC Transcription Factors in Sunflower (Helianthus annuus), their comparative phylogenetic analysis and association with leaf senescence. BMC Genomics 22:893 https://doi.org/10.1186/s12864-021-08199-5
  5. Bergès SE, M Yvon, D Masclef, M Dauzat, D Vile, M van Munster. Water deficit changes the relationships between epidemiological traits of the Cauliflower mosaic virus across diverse Arabidopsis thaliana accessions. 2021. Scientific Reports 11:24103. https://www.nature.com/articles/s41598-021-03462-x
  6. Berton T, Bernillon S, Fernandez O, Duruflé H, Flandin A, Cassan C, Jacob D, Langlade NB, Gibon Y, Moing A (2021) Leaf metabolomic data of eight sunflower lines and their sixteen hybrids under water deficit. OCL 28: #42. Doi: 10.1051/ocl/2021029
  7. Chen J, Beauvoit B, Génard M, Colombie S, Moing A, Vercambre G, Gomes E, Gibon Y, Dai Z (2021) Modelling predicts tomatoes can be bigger and sweeter if biophysical factors and transmembrane transports are fine‐tuned during fruit development. New Phytologist 230: 1489-1502. doi: 10.1111/nph.17260
  8. David, E., Serouart, M., Smith, D., Madec, S., Velumani, K., Liu, S., Wang, X., Pinto, F., Shafiee, S., Tahir, I.S.A., Tsujimoto, H., Nasuda, S., Zheng, B., Kirchgessner, N., Aasen, H., Hund, A., Sadhegi-Tehran, P., Nagasawa, K., Ishikawa, G., Dandrifosse, S., Carlier, A., Dumont, B., Mercatoris, B., Evers, B., Kuroki, K., Wang, H., Ishii, M., Badhon, M.A., Pozniak, C., LeBauer, D.S., Lillemo, M., Poland, J., Chapman, S., de Solan, B., Baret, F., Stavness, I., Guo, W., 2021. Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods. Plant Phenomics 2021, 1–9. https://doi.org/10.34133/2021/9846158
  9. Debaeke P, Casadebaig P, Langlade NB: New challenges for sunflower ideotyping in changing environments and more ecological cropping systems. Ocl-Oilseeds and Fats Crops and Lipids 2021, 28.
  10. Destailleur A, Poucet T, Cabasson C, Alonso AP, Cocuron J-C, Larbat R, Vercambre G, Colombié S, Petriacq P, Andrieu M-H, Beauvoit B, Gibon Y, Dieuaide-Noubhani M (2021) The evolution of leaf function during development is reflected in profound changes in the metabolic composition of the vacuole. Metabolites 11: #848. doi: 10.3390/metabo11120848
  11. Eyland D, Breton C, Sardos J, Kallow S, Panis B, Swennen R, Paofa J, Tardieu F, Welcker C, Janssens SB, Carpentier SC. 2021. Filling the gaps in gene banks: Collecting, characterizing, and phenotyping wild banana relatives of Papua New Guinea. Crop Science 61, 137-149.
  12. Eyland et al. High-throughput phenotyping reveals differential transpiration behavior within the banana wild relatives highlighting diversity in drought tolerance. Authorea. August 02, 2021. https://doi.org/10.22541/au.162788270.08316844/v1
  13. Fagny, M; Kuijjer, ML; Stam, M; Joets, J; Turc, O; Roziere, J; Pateyron, S; Venon, A; Vitte, C (2021) Identification of Key Tissue-Specific, Biological Processes by Integrating Enhancer Information in Maize Gene Regulatory Networks. Frontiers in Genetics DOI 10.3389/fgene.2020.606285
  14. G. ElMasry, N. Mandour, Y. Ejeez, D. Demilly, S. Al-Rejaie, J. Verdier, E. Belin & D. Rousseau. Multichannel imaging for monitoring chemical composition and germination capacity of cowpea (Vigna unguiculata) seeds during development and maturation. The Crop Journal, 2021.
  15. Garbouge, H., Rasti, P., & Rousseau, D. (2021). Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep  Learning. Sensors, 21(24), 8425.
  16. Grégoire Bianchetti, Cécile Baron, Aurélien Carrillo, Solenne Berardocco, Nathalie Marnet, Marie-Hélène Wagner, Didier Demilly, Sylvie Ducournau, Maria Manzanares-Dauleux, Françoise Le Cahérec, Julia Buitink, Nathalie Nesi, 2021. Dataset for the metabolic and physiological characterization of seeds from oilseed rape ( Brassica napus L.) plants grown under single or combined effects of drought and clubroot pathogen Plasmodiophora brassicae. Data in Brief, Volume 38, 2021 https://doi.org/10.1016/j.dib.2021.107247
  17. Jacques C., Forest M., Durey V., Salon C., Ourry A., Prudent M. (2021). Transient nutrient deficiencies in pea: consequences on nutrient uptake, remobilization and seed quality. Front. Plant Sci. 12:art.785221 (14p.).
  18. Jacques C., Salon C., Barnard R.L., Vernoud V., Prudent M. (2021). Drought stress memory at the plant cycle level: a review. Plants. 10:art.1873 (13.). 10.3390/plants10091873
  19. Jiang, J.Y., Comar, A., Weiss, M. and Baret, F., 2021. FASPECT: A model of leaf optical properties accounting for the differences between upper and lower faces. Remote Sensing of Environment, 253.
  20. Jin, S.C., Su, Y.J., Zhang, Y.G., Song, S.L., Li, Q., Liu, Z.H., Ma, Q., Ge, Y., Liu, L.L., Ding, Y.F., Baret, F. and Guo, Q.H., 2021. Exploring Seasonal and Circadian Rhythms in Structural Traits of Field Maize from LiDAR Time Series. Plant Phenomics, 2021.
  21. Jin, S.C., Sun, X.L., Wu, F.F., Su, Y.J., Li, Y.M., Song, S.L., Xu, K.X., Ma, Q., Baret, F., Jiang, D., Ding, Y.F. and Guo, Q.H., 2021. Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects. ISPRS Journal of Photogrammetry and Remote Sensing, 171: 202-223.
  22. Krzyzaniak Y., Cointault F., Loupiac C., Bernaud E., Ott F., Salon C., Laybros A., Han S., Héloir M.-C., Adrian M., Trouvelot S. (2021). In situ phenotyping of grapevine root system architecture by 2D or 3D imaging: advantages and limits of three cultivation methods. Front. Plant Sci. 12:art.638688 (15p.).
  23. Laborde, A., Puig-Castellví, F., Jouan-Rimbaud Bouveresse, D., Eveleigh, L., Cordella, C. & Jaillais, B. (2021) Detection of chocolate powder adulteration with peanut using near-infrared hyperspectral imaging and Multivariate Curve Resolution. Food Control, 119, 107454
  24. Laue, C. Stevens. Y, van Erp, M., Papazova, E., Soeth, E., Pannenbeckers, A., Stolte, E., Böhm, R., Le Gall, S., Falourd, X., Ballance, S., Knutsen, S.H., Pinheiro, I., Possemiers, S., Ryan, P.M., Ross,R.P., Stanton, C., Wells, J.M., van der Werf, S., Mes,J.J., Schrezenmeir, J. Adjuvant Effect of Orally Applied Preparations Containing Non-Digestible Polysaccharides on Influenza Vaccination in the Healthy Elderly: A Double-Blind, Randomised, Controlled Pilot Trial. Nutrients 2021, 13, 2683. https://doi.org/10.3390/nu13082683
  25. Le Gall, S., Sole-Jamault, V., Nars-Chasseray, M., Le Goff, A., Le Bot, L., Guinet, T., Renaud, C., Gervais, J., Bansard, S., Ohleyer, L., Jeandroz, S. Data on agronomic traits, biochemical composition of lipids, proteins and polysaccharides and rheological measurement in a brown mustard seed collection. Data In Brief. 2021. https://doi.org/10.1016/j.dib.2021.107417
  26. Li, W., A. Comar, M. Weiss, S. Jay, G. Colombeau, R. Lopez-Lozano, S. Madec and F. Baret (2021). "A Double Swath Configuration for Improving Throughput and Accuracy of Trait Estimate from UAV Images." Plant Phenomics 2021.
  27. Li, W., J. Jiang, M. Weiss, S. Madec, F. Tison, B. Philippe, A. Comar and F. Baret (2021). "Impact of the reproductive organs on crop BRDF as observed from a UAV." Remote Sensing of Environment 259: 112433.
  28. Li, W.J., Fang, H.L., Wei, S.S., Weiss, M. and Baret, F., 2021. Critical analysis of methods to estimate the fraction of absorbed or intercepted photosynthetically active radiation from ground measurements: Application to rice crops. Agricultural and Forest Meteorology, 297.
  29. Liu, S., F. Baret, M. Abichou, L. Manceau, B. Andrieu, M. Weiss and P. Martre (2021). "Importance of the description of light interception in crop growth models." Plant Physiology 186(2): 977-997.
  30. Luoni SAB, Cenci A, Moschen S, Nicosia S, Radonic LM, Sabio GJ, Langlade NB, Vile D, Rovere CV, Fernandez P: Genome-wide and comparative phylogenetic analysis of senescence-associated NAC transcription factors in sunflower (Helianthus annuus). Bmc Genomics 2021, 22.
  31. Machwitz, M., R. Pieruschka, K. Berger, M. Schlerf, H. Aasen, S. Fahrner, J. Jiménez-Berni, F. Baret and U. Rascher (2021). "Bridging the Gap Between Remote Sensing and Plant Phenotyping—Challenges and Opportunities for the Next Generation of Sustainable Agriculture." Frontiers in Plant Science 12(2334).
  32. Maslard C., Arkoun M., Salon C., Prudent M. (2021). Root architecture characterization in relation to biomass allocation and biological nitrogen fixation in a collection of european soybean genotypes. OCL-Ol. Corps Gras Lipides. 28:art. 48 (12p.).
  33. Nikolić Chenais J, Marion L, Larocque R, Jam M, Jouanneau D, Cladiere L, Le Gall S, Fanuel M, Desban N, Rogniaux H, Ropartz D, Ficko-Blean E, Michel G. Systematic comparison of eight methods for preparation of high purity sulfated fucans extracted from the brown alga Pelvetia canaliculata. Int J Biol Macromol. 2021 Dec 27:S0141-8130(21)02752-5. https://doi: 10.1016/j.ijbiomac.2021.12.122
  34. Perez-Valencia et al. A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data. BioRXiv https://doi.org/10.1101/2021.08.10.455613
  35. Poucet T, González-Moro MB, Cabasson C, Beauvoit B, Gibon Y, Dieuaide-Noubhani M, Marino D (2021) Ammonium supply induces differential metabolic adaptive responses in tomato according to leaf phenological stage. J. Exp. Bot. 72: 3185–3199. doi:10.1093/jxb/erab057
  36. Rubio B, Fernandez O, Cosson P, Berton T, Caballero M, Lion R, Roux F, Bergelson J, Gibon Y, Schurdi-Levraud V (2021) Metabolic Profile Discriminates and Predicts Arabidopsis Susceptibility to Virus under Field Conditions. Metabolites 11: #230. doi: 10.3390/metabo11040230
  37. Shichao, J., S. Xiliang, F. Wu, Y. Su, Y. Li, S. Song, K. Xu, Q. Ma, F. Baret, D. Jiang, Y. Ding and Q. Guo (2021). "Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects." ISPRS Journal of Photogrammetry and Remote Sensing Accepted (Novembre 2020).
  38. Shinohara T., Ducournau S., Matthews S., Wagner M.-H., and Powell A.A., 2021. Early counts of radicle emergence, counted manually and by image analysis, can reveal differences in the production of normal seedlings and the vigour of seed lots of cauliflower. Seed Science and Technology 49, 3, 219-235. https://doi.org/10.15258/sst.2021.49.3.04
  39. Sichert, A.; Le Gall, S.; Klau, L. J.; Laillet, B.; Rogniaux, H.; Aachmann, F. L.; Hehemann, J.-H. Ion-Exchange Purification and Structural Characterization of Five Sulfated Fucoidans from Brown Algae. Glycobiology 2021, 31 (4), 352–357. https://doi.org/10.1093/glycob/cwaa064
  40. Tardieu F (2021) Different avenues for progress apply to drought tolerance, water use efficiency and yield in dry areas. Current opinion in biotechnology; https://doi.org/10.1016/j.copbio.2021.07.019
  41. Tristan Lurthy, Cécile Cantat, Christian Jeudy, Philippe Declerck, Karine Gallardo, et al.. Impact of Bacterial Siderophores on Iron Status and Ionome in Pea. Frontiers in Plant Science, Frontiers, 2020, 11.
  42. Urrutia M, Blein-Nicolas M, Prigent S, Bernillon S, Deborde C, Balliau T, Maucourt M, Jacob D, Ballias P, Bénard C, Sellier H, Gibon Y, Giauffret C, Zivy M, Moing A (2021) Maize metabolome and proteome responses to controlled cold stress partly mimic early-sowing effects in the field and differ from those of Arabidopsis. Plant Cell Environ. 44:1504-1521. doi: 10.1111/pce.13993
  43. Vargas-Rojas, F. (2021). Ontological Formalisation of Mathematical Equations for Phenomic Data Exploitation.  European Semantic Web Conference. https://doi.org/10.1007/978-3-030-80418-3_30
  44. Velumani, K., Lopez-Lozano, R., Madec, S., Guo, W., Gillet, J., Comar, A. and Baret, F., 2021. Estimates of Maize Plant Density from UAV RGB Images Using Faster-RCNN Detection Model: Impact of the Spatial Resolution. Plant Phenomics, 2021.
  45. Zhijuan Chen, Joseph Ly Vu, Benoit Ly Vu, Julia Buitink, Olivier Leprince, et al.. Genome-Wide Association Studies of Seed Performance Traits in Response to Heat Stress in Medicago truncatula Uncover MIEL1 as a Regulator of Seed Germination Plasticity. Frontiers in Plant Science, Frontiers, 2021, 12, pp.673072. ⟨10.3389/fpls.2021.673072⟩

 

Autres articles de vulgarisation scientifique

  1. Perspectives Agricoles (projet DUROSTRESS) Stratégies d’adaptation du blé dur au nouveau climat Delphine Hourcade, Paloma Cabeza-Orcel, Décembre 2021 – N°494 Projet accueilli sur la plateforme DiaPhen mais oubli de citation dans l’article
  2. Joram P, Dauzat M, Bédiée A, Vile D. 2022. Relamping PHENOPSIS – a high throughput phenotyping platform – with LEDs. Acta Horticulturae.
  3. Leger J-B, Kuhn E, Parent B, Tardieu F, Welcker C (2021) Estimation des paramètres d'un modèle de culture à partir de données de plein champ et de données de plateforme de phénotypage. 52èmes Journées de Statistique de la Société Française de Statistique, Nice, France (Actes de congrès)
  4.  « Enjeux et outils du phénotypage : UNE STRATÉGIE NUMÉRIQUE sur le long terme » Katia BEAUCHENE Katia Beauchêne, Benoît de Solan, Stéphane Jezequel, Jean Pierre Cohan et Xavier Pinochet, Publication Agricole 485-31-36- de Février 2021.
  5. Redon M. 2021. Imagerie pour le phénotypage du végétal. Application au robot Phenobean. Rapport de projet M2 PSI, Université d’Angers. 09/02/2021.
  6. Redon M. 2021. Imagerie pour le phénotypage du végétal. Application au robot Phenobean. Rapport d’alternance M2 PSI, Université d’Angers. 26/08/2021.

2020

  1. Artzet S, Chen TW, Chopard J, Brichet N, Mielewczik M, Cohen-Boulakia S, Cabrera Bosquet L, Tardieu F, Fournier C, Pradal C (2020) Phenomenal: An automatic open source library for 3D shoot architecture reconstruction and analysis for image-based plant phenotyping. https://hal.inrae.fr/hal-02788579
  2. Béral A, Le Gouis J, Rincent R, Girousse C, Allard V (2020) Wheat individual grain-size variance originates from crop development and from specific genetic determinism. PLos One 15:e0230689
  3. Bergès SE, Vasseur F, Bediée A, Rolland G, Masclef D, Dauzat M, van Munster M, Vile D (2020) Natural variation of Arabidopsis thaliana responses to Cauliflower mosaic virus infection upon water deficit. PLOS Pathogens 16: e1008557. doi:10.1371/journal.ppat.1008557.
  4. Bernard, A., Hamdy, S., Le Corre, L. et al. 3D characterization of walnut morphological traits using X-ray computed tomography. Plant Methods 16, 115 (2020). https://doi.org/10.1186/s13007-020-00657-7
  5. Blein-Nicolas M, Negro SS, Balliau T, Welcker C, Cabrera-Bosquet L, Nicolas SD, Charcosset A, Zivy M (2020) A systems genetics approach reveals environment-dependent associations between SNPs, protein coexpression, and drought-related traits in maize. Genome research 30: 1593-1604. doi:10.1101/gr.255224.119.
  6. Cakpo CB, Vercambre G, Baldazzi V, Roch L, Valsesia P, Memah M-M, Colombié S, Moing A, Gibon Y, Génard M (2020) Model-assisted comparison of sugar accumulation patterns in ten fleshy fruits highlights differences between annual and perennial species. Annals of Botany, in press. DOI: 10.1093/aob/mcaa082
  7. Castelletti S, Coupel-Ledru A, Granato I, Palaffre C, Cabrera-Bosquet L, Tonelli C, Nicolas SD, Tardieu F, Welcker C, Conti L (2020) Maize adaptation across temperate climates was obtained via expression of two florigen genes. Plos Genetics 16: e1008882. doi:10.1371/journal.pgen.1008882.
  8. Couchoud M., Salon C., Girodet S., Jeudy C., Vernoud V., Marion Prudent (2020). Pea efficiency of post drought recovery relies on the strategy to fine-tune nitrogen nutrition. Frontiers in Plant Science, 11, 204. https://doi.org/10.3389/fpls.2020.00204
  9. David, E., S. Madec, P. Sadeghi-Tehran, H. Aasen, B. Zheng, S. Liu, N. Kirchgessner, G. Ishikawa, K. Nagasawa, M. A. Badhon, C. Pozniak, B. de Solan, A. Hund, S. C. Chapman, F. Baret, I. Stavness and W. Guo (2020). "Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods." Plant Phenomics 2020: 3521852.
  10. Ding L, Milhiet T, Couvreur V, Nelissen H, Meziane A, Parent B, Aesaert S, Van Lijsebettens M, Inzé D, Tardieu F, Draye X, Chaumont F (2020) Modification of the expression of the aquaporin ZmPIP2;5 affects water relations and plant growth. Plant Physiology 182: 2154-2165. doi:10.1104/pp.19.01183.
  11. Dingkuhn M, Luquet D, Fabre D, Muller B, Yin X, Paul M (2020) The case for improving crop carbon sink strength or plasticity for a CO2-rich future. Current Opinion in Plant Biology 56: 259-272. doi:10.1016/j.pbi.2020.05.012.
  12. Ducournau, S., Charrier, A., Demilly, D., Wagner, M. H., Trigui, G., Dupont, A., ... & Dürr, C. (2020). High throughput phenotyping dataset related to seed and seedling traits of sugar beet genotypes. Data in brief, 29, 105201.
  13. Duran Garzon C, Lequart M, Rautengarten C, Bassard S, Sellier-Richard H, Baldet P, Heazlewood JL, Gibon Y, Domon J-M, Giauffret C, Rayon C (2020) Regulation of carbon metabolism in two maize sister lines contrasted for chilling tolerance. Journal of Experimental Botany 71 : 356–369. doi:10.1093/jxb/erz421
  14. ElMasry, G., ElGamal, R., Mandour, N., Gou, P., Al-Rejaie, S., Belin, E., & Rousseau, D. (2020). Emerging thermal imaging techniques for seed quality evaluation: Principles and applications. Food Research International, 131, 109025.
  15. Garbez, M., Belin, E., Chéné, Y. et al. A new approach to predict the visual appearance of rose bush from image analysis of 3D videos. Eur. J. Hortic. Sci, 2020, vol. 85, p. 182-190.
  16. Gody, L., Duruflé, H., Blanchet, N., Carré, C., Legrand, L., Mayjonade, B., Muños, S., Pomiès, L., de Givry, S., Langlade, N.B., others, 2020. Transcriptomic data of leaves from eight sunflower lines and their sixteen hybrids under water deficit. OCL 27, 48.
  17. Heidsieck G, De Oliveira D, Pacitti E, Pradal C, Tardieu F, Valduriez P (2020) Efficient Execution of Scientific Workflows in the Cloud Through Adaptive Caching. In A Hameurlain, AM Tjoa, P Lamarre, K Zeitouni, eds, Transactions on Large-Scale Data-and Knowledge-Centered Systems XLIV, Vol 12380. Springer, Berlin, Germany, pp 41-66.
  18. Hennet L., Berger A., Trabanco N., Ricciuti E., Dufayard J-F., Bocs S., Bastianelli D., Bonnal L., Roques S., Rossini L., Luquet D., Terrier N., Pot D. 2020 Transcriptional regulation of sorghum stem composition: key players identified through co-expression gene network and comparative genomics analyses. Frontiers in Plant Science. Frontiers Media S.A., 11, p. 224. doi: 10.3389/fpls.2020.00224.
  19. Jacob D, David R, Aubin S, Gibon Y (2020). Making experimental data tables in the life sciences more FAIR: a pragmatic approach. GigaScience 9 : giaa144. Doi : 10.1093/gigascience/giaa144
  20. Jay, S., Comar, A., Benicio, R., Beauvois, J., Dutartre, D., Daubige, G., ... & Baret, F. (2020). Scoring cercospora leaf spot on sugar beet: comparison of UGV and UAV phenotyping systems. Plant Phenomics, 2020.
  21. Jiang, J., A. Comar, M. Weiss and F. Baret (2020). "FASPECT: a model of leaf optical properties accounting for the differences between upper and lower faces." Remote Sensing of Environment Accepté (Novembre 2020).
  22. Laborde, A., Jaillais, B., Roger, JM., Metz, M., Jouan-Rimbaud Bouveresse, D., Eveleigh, L., Cordella, C. (2020) Subpixel detection of peanut in wheat flour using a matched subspace detector algorithm and near-infrared hyperspectral imaging. Talanta, 337.
  23. Lacube S, Manceau L, Welcker C, Millet E, Gouesnard B, Palaffre C, Ribaut JM, Hammer G, Parent B, Tardieu F (2020) Simulating the effect of flowering time on maize individual leaf area in contrasting environmental scenarios. Journal of Experimental Botany 71: 5577-5588. doi:10.1093/jxb/eraa278.
  24. Lahaye M., Falourd, X., Laillet, B., Le Gall., S. (2020) Cellulose, pectin and water in cell walls determine apple flesh viscoelastic mechanical properties. Carbohydrate Polymers, 232, 115768.
  25. Lancelot, E., Courcoux, P., Chevallier, S., Le-Bail, A. & Jaillais, B. (2020) Prediction of water content in biscuit using Near-Infrared hyperspectral imaging spectroscopy and chemometric. Journal of Near Infrared Spectroscopy, 28(3), 140-147.
  26. Luna E, Flandin A, Cassan C, Prigent S, Chevanne C, Kadiri CF, Gibon Y, Pétriacq P (2020) Metabolomics to exploit the primed immune system of tomato fruit. Metabolites 11: 146. doi: 10.3390/metabo10030096.
  27. Méline V, Brin C, Lebreton G, Ledroit L, Sochard D, Hunault G, Boureau T, Belin E. « A Computation Method Based on the Combination of Chlorophyll Fluorescence Parameters to Improve the Discrimination of Visually Similar Phenotypes Induced by Bacterial Virulence Factors. ». Frontiers in Plant Science. 2020 Vol 26;11:213. doi: 10.3389/fpls.2020.00213. eCollection 2020.
  28. Montazeaud G, Violle C, Roumet P, Rocher A, Ecarnot M, Compan F, Maillet G, Fort F, Fréville H. 2020. Multifaceted functional diversity for multifaceted crop yield: towards ecological assembly rules for varietal mixtures. Journal of Applied Ecology. doi:10.1111/1365-2664.13735
  29. Papoutsoglou EA, Faria D, Arend D, Arnaud E, Athanasiadis IN, Chaves I, Coppens F, Cornut G, Costa BV, Cwiek-Kupczynska H, Droesbeke B, Finkers R, Gruden K, Junker A, King GJ, Krajewski P, Lange M, Laporte M-A, Michotey C, Oppermann M, Ostler R, Poorter H, Rami Rez-Gonzalez R, Ramsak Z, Reif JC, Rocca-Serra P, Sansone S-A, Scholz U, Tardieu F, Uauy C, Usadel B, Visser RGF, Weise S, Kersey PJ, Miguel CM, Adam-Blondon A-F, Pommier C (2020) Enabling reusability of plant phenomic datasets with MIAPPE 1.1. New Phytologist 227: 260-273. doi:10.1111/nph.16544
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  31. Penouilh-Suzette, C., Pomiès, L., Duruflé, H., Blanchet, N., Bonnafous, F., Dinis, R., Brouard, C., Gody, L., Grassa, C., Heudelot, X., others, 2020. RNA expression dataset of 384 sunflower hybrids in field condition. OCL 27, 36.
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  37. Sichert, A., Le Gall, S., Klau, L-J., Laillet, B., Rogniaux, H., Aachmann, F-L., Hehemann, J-H. (2020) Ion-exchange purification and structural characterization of five sulfated fucoidans from brown algae. Glycobiology, cwaa064, https://doi.org/10.1093/glycob/cwaa064
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2019

  1. Artzet S, Chen T-W, Chopard J, Brichet N, Mielewczik M, Cohen-Boulakia S, Cabrera-Bosquet L, Tardieu F, Fournier C, Pradal C. 2019. “Phenomenal”: An automatic open source library for 3D shoot architecture reconstruction and analysis for image-based plant phenotyping. bioRxiv, 805739. https://doi.org/10.1101/805739
  2. Blein-Nicolas M, Negro SS, Balliau T, Welcker C, Bosquet LC, Nicolas SD, Charcosset A, Zivy M. 2019. A proteomics-based systems genetics approach reveals environment-specific loci modulating protein co-expression and drought-related traits in maize. bioRxiv, 636514. https://doi.org/10.1101/636514
  3. Decros, G., Baldet, P., Beauvoit, B., Stevens, R., Flandin, A., Colombié, S., Gibon, Y., Pétriacq, P. (2019). Get the Balance Right: ROS Homeostasis and Redox Signalling in Fruit. Frontiers in Plant Science, 10, 1-16. DOI: 10.3389/fpls.2019.01091
  4. Decros, G., Beauvoit, B., Colombié, S., Cabasson, C., Bernillon, S., Arrivault, S., Guenther, M., Belouah, I., Prigent, S., Baldet, P., Gibon, Y., Pétriacq, P. (2019). Regulation of Pyridine Nucleotide Metabolism During Tomato Fruit Development Through Transcript and Protein Profiling. Frontiers in Plant Science, 10, 1201. DOI: 10.3389/fpls.2019.01201
  5. van Eeuwijk F, Bustos-Korts D, Millet EJ, Boer M, Kruijer W, Thompson A, Malosetti M, Iwata H, Quiroz R, Kuppe C, Muller O, Blazakis KN, Yu K, Tardieu F, Chapman S (2019) Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding. Plant Science doi:10.1016/j.plantsci.2018.06.018
  6. Henriet C., Aimé D., Terezol M., Kilandamoko A., Rossin N., Combes-Soia L., Labas V., Serre R.-F., Prudent M., Kreplak J., Vernoud V., Gallardo K. (2019). Water stress combined with Sulfur deficiency in pea affects yield components but mitigates the effect of deficiency on seed globulin composition. J. Exp. Bot. 70:4287-4303.
  7. Jaafar, Z., Mazeau, K., Boissière, A., Le Gall S., Villares A., Vigouroux J., Beury N., Moreau C., Lahaye M., Cathala B. (2019) Meaning of xylan acetylation on xylan-cellulose interactions: a quartz crystal microbalance with dissipation (QCM-D) and molecular dynamic study. Carbohydrate Polymers 226, 115315.
  8. Jay, S., F. Baret, D. Dutartre, G. Malatesta, S. Héno, A. Comar, M. Weiss and F. Maupas (2019). Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops. Remote Sensing of Environment 231: 110898.
  9. Jin, X., S. Madec, D. Dutartre, B. de Solan, A. Comar and F. Baret (2019). High-throughput measurements of stem characteristics to estimate ear density and above-ground biomass. Plant Phenomics 2019: 4820305.
  10. Koch G, Rolland G, Dauzat M, Bediee A, Baldazzi V, Bertin N, Guédon Y, Granier C (2019) Leaf Production and Expansion: A Generalized Response to Drought Stresses from Cells to Whole Leaf Biomass—A Case Study in the Tomato Compound Leaf. Plants 8: 409. doi:10.3390/plants8100409.
  11. Larue F., Fumey D., Rouan L., Soulié J-C, Roques S., Beurier G., Luquet D. 2019. Modelling tiller growth and mortality as a sink-driven process using Ecomeristem: implications for biomass sorghum ideotyping. Annals of Botany 124: 675-690, doi: 10.1093/aob/mcz038.
  12. Liu, S., P. Martre, S. Buis, M. Abichou, B. Andrieu and F. Baret (2019). "Estimation of Plant and Canopy Architectural Traits Using the Digital Plant Phenotyping Platform." Plant physiology 181(3): 881-890.
  13. Madec, S., X. Jin, H. Lu, B. De Solan, S. Liu, F. Duyme, E. Heritier and F. Baret (2019). "Ear density estimation from high resolution RGB imagery using deep learning technique." Agricultural and Forest Meteorology 264: 225-234
  14. Mori, K., Beauvoit, B., Biais, B., Chabane, M., Allwood, J. W., Deborde, C., Maucourt, M., Goodacre, R., Cabasson, C., Moing, A., Rolin, D., Gibon, Y. (2019). Central Metabolism Is Tuned to the Availability of Oxygen in Developing Melon Fruit. Frontiers in Plant Science, 10, 594. DOI: 10.3389/fpls.2019.00594
  15. Parent B, Millet EJ, Tardieu F (2019) The use of thermal time in plant studies has a sound theoretical basis provided that confounding effects are avoided. Journal of Experimental Botany 70: 2359-2370. doi:10.1093/jxb/ery402.
  16. Perez RPA, Fournier C, Cabrera-Bosquet L, Artzet S, Pradal C, Brichet N, Chen TW, Chapuis R, Welcker C, Tardieu F. 2019. Changes in the vertical distribution of leaf area enhanced light interception efficiency in maize over generations of selection. Plant, Cell & Environment. https://doi.org/10.1111/pce.13539
  17. Pommier, C., Michotey, C., Cornut, G., Roumet, P., Duchêne, E., Flores, R., Lebreton, A., Alaux, M., Durand, S., Kimmel, E., Letellier, T., Merceron, G., Laine, M., Guerche, C., Loaec, M., Steinbach, D., Laporte, M. A., Arnaud, E., Quesneville, H., & Adam-Blondon, A. F. (2019). Applying FAIR Principles to Plant Phenotypic Data Management in GnpIS. Plant Phenomics, 2019, 1–15. https://doi.org/10.34133/2019/1671403
  18. Prudent M., Dequiedt S., Sorin C., Girodet S., Nowak V., Duc G., Salon C., Maron P.A. (2020). The diversity of soil microbial communities matters when legumes face drought. Plant Cell Environ. 43:1023-1035.
  19. Reynolds D, Baret F, Welcker C, Bostrom A, Ball J, Cellini F, Lorence A, Chawade A, Khafif M, Noshita K, Mueller-Linow M, Zhou J, Tardieu F (2019) What is cost-efficient phenotyping? Optimizing costs for different scenarios. Plant Science 282: 14-22. doi:10.1016/j.plantsci.2018.06.015.
  20. Rincent R, Malosetti M, Ababaei B, Touzy G, Mini A, Bogard M, Martre P, Le Gouis J, van Eeuwijk F (2019) Using crop growth model stress covariates and AMMI decomposition to better predict genotype by environment interactions. Theoretical and Applied Genetics 132:3399-3411
  21. Roch, L., Dai, Z., Gomes, E., Bernillon, S., Wang, J., Gibon, Y., Moing, A. (2019). Fruit salad in the lab: comparing botanical species to help deciphering fruit primary metabolism. Frontiers in Plant Science, 10, 836. DOI: 10.3389/fpls.2019.00836
  22. Salon, C., Baussart, C., Bernard, C., Bourion, V., Jeudy, C., Lamboeuf, M., Martinet, J., Moreau, D., Prudent, M., Voisin, A.-S. (2019). Phénotypage racinaire haut débit et ses applications à l'étude des interactions plante x microorganisme. Sélectionneur Français, 70, 65-75.
  23. Sartori K, Vasseur F, Violle C, Baron E, Gerard M, Rowe N, Ayala-Garay OJ, Christophe A, Jalón LGd, Masclef D, Harscouet E, Granado MdR, Chassagneux A, Kazakou E, Vile D (2019) Leaf economics and slow-fast adaptation across the geographic range of Arabidopsis thaliana. Scientific Reports 9: 10758. doi:10.1038/s41598-019-46878-2.
  24. Selby, P., Abbeloos, R., Backlund, J. E., Basterrechea Salido, M., Bauchet, G., … Benites-Alfaro, O. E. (2019). BrAPI—an application programming interface for plant breeding applications. Bioinformatics, 35(20), 4147–4155. https://doi.org/10.1093/bioinformatics/btz190
  25. Touzy G, Rincent R, Bogard M, Lafarge S, Dubreuil P, Mini A, Deswarte J-C, Beauchene K, Le Gouis J, Praud S (2019). Using environmental clustering to identify specific drought tolerance QTLs in bread wheat (T. aestivum L.). Theoretical and Applied Genetics 132:2859-2880. doi.org/10.1007/s00122-019-03393-2
  26. Verhertbruggen, Y., Falourd, X., Sterner, M., Guillon, F., Girousse, C., Foucat, L., Le Gall, S., Chateigner-Boutin, A-L., Saulnier, L., Challenging the putative structure of mannan in wheat (Triticum aestivum) endosperm, Carbohydrate Polymers 224: 115063. doi.org/10.1016/j.carbpol.2019.115063.
  27. Alvarez Prado S, Sanchez I, Cabrera-Bosquet Ll, Grau A, Welcker C,Tardieu F and Hilgert N. 2019. Cleaning or not cleaning phenotypic datasets for outlier plants in genetic analyses? Journal of Experimental Botany, Vol. 70 n°15: 3693-3698. doi:10.1093/jxb/erz191
  28. Avramova V, Meziane A, Bauer E, Blankenagel S, Eggels S, Gresset S, Grill E, Niculaes C, Ouzunova M, Poppenberger B, Presterl T, Rozhon W, Welcker C, Yang Z, Tardieu F, Schön C-C (2019) Carbon isotope composition, water use efficiency, and drought sensitivity are controlled by a common genomic segment in maize. Theoretical and Applied Genetics 132: 53-63.
  29. Beauchêne K, Leroy F, Fournier A, Huet C, Bonnefoy M, Lorgeou J, de Solan B, Piquemal B, Thomas and Cohan J-P (2019) Management and Characterization of Abiotic Stress via PhénoField®, a High-Throughput Field Phenotyping Platform. Front. Plant Sci. 10:904. doi: 10.3389/fpls.2019.00904
  30. Chen TW, Cabrera Bosquet L, Alvarez Prado S, Perez R, Artzet S, Pradal C, Coupel-Ledru A, Fournier C, Tardieu F (2019) Genetic and environmental dissection of biomass accumulation in multi-genotype maize canopies. Journal of Experimental Botany 70: 2523–2534. doi:10.1093/jxb/ery309
  31. Guérin C, Roche J, Allard V, Ravel C, Bouzidi MF, Mouzeyar S (2019) Genome-wide analysis, expansion and expression of the NAC family under abiotic stresses in bread wheat (T. aestivum L.). PLos ONE 14: e0213390
  32. Millet EJ, Kruijer W,  Coupel-Ledru A, Alvarez Prado S, Cabrera-Bosquet L, Lacube S, Charcosset A, Welcker C, van Eeuwijk F, Tardieu F (2019) Genomic prediction of maize yield across European environmental conditions. Nature Genetics 51, 952-956. https://doi.org/10.1038/s41588-019-0414-y
  33. Neveu, P., A. Tireau, N. Hilgert, V. Nègre, J. Mineau-Cesari, N. Brichet, R. Chapuis, I. Sanchez, C. Pommier, B. Charnomordic, F. Tardieu and L. Cabrera-Bosquet. 2019. Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System. New Phytologist 221: 588-601. https://doi.org/10.1111/nph.15385
  34. Roitsch T, Cabrera-Bosquet L, Fournier A, Ghamkhar K, Jiménez-Berni J, Pinto F, Ober ES (2019) Review: New sensors and data-driven approaches—A path to next generation phenomics. Plant Science 282: 2-10. doi:10.1016/j.plantsci.2019.01.011

2018

  1. Balti R, Le Balc’h R, Brodu N, Gilbert M, Le Gouic B, Le Gall S, Sinquin C, Masse A (2018) Concentration and purification of Porphyridium cruentum exopolysaccharides by membrane filtration at various cross-flow velocities. Process Biochemistry 74: 175-184.
  2. Belin E., Gillard N., Douarre C., Franconi F., Rojas Varela J., Chapeau-Blondeau F., Demilly D., Adrien J., Maire E., Rousseau D. « Evaluation of 3D/2D Imaging and Image Processing Techniques for the Monitoring of Seed Imbibition ». Journal of Imaging. 2018. Vol. 4 n°7 p. 83,1-16
  3. Bergès S, Vile D, Vazquez Rovere C, Blanc S, Yvon M, Bediee A, Rolland G, Dauzat M, Van Munster M (2018) Interactions Between Drought and Plant Genotype Change Epidemiological Traits of Cauliflower mosaic virus. Frontiers in Plant Science 9: 1-11
  4. Courtial J., Hamama L., Helesbeux J. - J., Lecomte M., Renaux Y., Guichard E., Voisine L., Yovanopoulos C., Hamon B., Ogé L., Richomme P., Briard M., Boureau T., Gagné S., Poupard P., Berruyer R. « Aldaulactone – an original phytotoxic secondary metabolite involved in the aggressiveness of Alternaria dauci on carrot ». Frontiers in Plant Science. 2018. Vol. 9 p. 502
  5. Dubois M, Selden K, Bediee A, Rolland G, Baumberger N, Noir S, Bach L, Lamy G, Granier C, Genschik P (2018) SIAMESE-RELATED1 is regulated post-translationally and participates in repression of leaf growth under moderate drought. Plant Physiology 176: 2834-2850.
  6. Etienne, P., Diquelou, S., Prudent, M., Salon, C., Maillard, A., & Ourry, A. (2018). Macro and micronutrient storage in plants and their remobilization when facing scarcity: The case of drought. Agriculture, 8(1), 14.
  7. Fernique, P., Pradal, C., (2018). AutoWIG: Automatic generation of Python bindings for C++ libraries. PeerJ Computer Science, 4:e149. https://doi.org/10.7717/peerj-cs.149
  8. Gadea A, Le Lamer A. C, Le Gall S, Jonard C, Ferron S, Catheline D, Ertz D, Le Pogam P, Boustie J, Lohézic-Le Devehat F, Charrier M (2018) Intrathalline Metabolite Profiles in the Lichen Argopsis friesiana Shape Gastropod Grazing Patterns. J Chem Ecol. 44 (5): 471-482.
  9. Garbez M., Symoneaux R., Belin E., Caraglio Y., Chéné Y., Donès N., Durand J. - B., Hunault G., Relion D., Sigogne M., Rousseau D., Galopin G. « Ornamental plants architectural characteristics in relation to visual sensory attributes: a new approach on the rose bush for objective evaluation of the visual quality ». European Journal of Horticultural Science. 2018.
  10. Garin G, Pradal C, Fournier C, Claessen D, Houlès V, Robert C (2018) Modelling interaction dynamics between two foliar pathogens in wheat: a multi-scale approach. Annals of Botany 121: 927-940.
  11. Grimaud F, Faucard D, Tarquis L, Pizzut-Serin S, Roblin P, Morel S, Le Gall S, Falourd X, Rolland-Sabaté A, Lourdin D, Moulis C, Remaud-Siméon M, Potocki-Veronese G (2018) Enzymatic synthesis of polysaccharide-based copolymers. Green Chem. 20: 4012-4022.
  12. Journaux A, Mineau J, Negre V (2018) Du phénotypage au Big Data. Cahier des Techniques de l'INRA: 167-169.
  13. Koch G, Rolland G, Dauzat M, Bediee A, Baldazzi V, Bertin N, Guedon Y, Granier C (2018) Are compound leaves more complex than simple ones? A multi-scale analysis. Annals of Botany 122: 1173-1185.
  14. Lahaye M, Bouin C, Barbacci A, Le Gall S, Foucat L (2018) Water and cell wall contributions to apple mechanical properties. Food Chem. 268: 386-394.
  15. Lamari N, Zhendre V, Urrutia M, Bernillon S, Maucourt M, Deborde C, Prodhomme D, Jacob D, Ballias P, Rolin D, Sellier H, Rabier D, Gibon Y, Giauffret C, Moing A (2018) Metabotyping of 30 maize hybrids under early-sowing conditions reveals potential marker-metabolites for breeding. Metabolomics 14:132.
  16. Luquet D., Perrier L., Clément-Vidal A., Jaffuel S., Verdeil J.L., Roques S., Soutiras A., Baptiste C., Fabre D., Bastianelli D., Bonnal L., Sartre P., Rouan L., Pot D. 2018. Genotypic covariations of traits underlying sorghum stem biomass production and quality and their regulations by water availability: insight from studies at organ and tissue levels. GCB Bioenergy. doi: 10.1111/gcbb.12571.
  17. Pallas B., Bluy S., Ngao J., Martinez S., Clément-Vidal A., Kelner J.J., Costes E. 2018. Growth and carbon balance are differently regulated by tree and shoot fruiting contexts: an integrative study on apple genotypes with contrasted bearing patterns. Tree Physiology Volume 00, 1–14. doi:10.1093/treephys/tpx166.
  18. Parent B, Leclere M, Lacube S, Semenov MA, Welcker C, Martre P, Tardieu F (2018) Maize yields over Europe may increase in spite of climate change, with an appropriate use of the genetic variability of flowering time. Proceedings of the National Academy of Sciences of the United States of America 115: 10642-10647.
  19. Pradal C, Cohen-Boulakia S, Heidsieck G, Pacitti E, Tardieu F, Valduriez P (2018) Distributed management of scientific workflows for high-throughput plant phenotyping. ERCIM News 113: 36-37.
  20. Reynolds D, Baret F, Welcker C, Bostrom A, Ball J, Cellini F, Lorence A, Chawade A, Khafif M, Noshita K, Mueller-Linow M, Zhou J, Tardieu F (2018) What is cost-efficient phenotyping? Optimizing costs for different scenarios. Plant Science (in press) doi:10.1016/j.plantsci.2018.06.015
  21. Rincent R, Charpentier J-P, Faivre-Rampant P, Paux E, Le Gouis J, Bastien C, Segura V (2018) Phenomic selection: a low-cost and high-throughput method based on indirect predictions. Proof of concept on wheat and poplar. G3 8:3961-3972
  22. Rymaszewski W, Dauzat M, Bédiée A, Rolland G, Luchaire N, Granier C, Hennig J, Vile D (2018) Measurement of Arabidopsis thaliana Plant Traits Using the PHENOPSIS Phenotyping Platform. Bio-Protocol 8: e2739.
  23. Samiei S., Rasti P., Daniel H., Belin E., Richard P., Rousseau D. « Toward a computer vision perspective on the visual impact of vegetation in symmetries of urban environments ». Symmetry. 2018. Vol. 10 n°12 p. 666
  24. Sartori KFR, Vasseur F, Violle C, Baron E, Gerard M, Rowe N, Ayala-Garay OJ, Christophe A, Garcia De Jalon L, Masclef D, Harscouet E, Del Rey Granado M, Chassagneux A, Kazakou E, Vile D (2018) Leaf economics guides slow-fast adaptation across the geographic range of A. thaliana. bioRxiv: 487066.
  25. Shinozaki Y, Ezura K, Hu J, Okabe Y, Bénard C, Prodhomme D, Gibon Y, Sun T-P, Ezura H, Ariizumi T (2018) Identification and functional study of a mild allele of SlDELLA gene conferring the potential for improved yield in tomato. Scientific Reports 8:12043.
  26. Tardieu F, Simonneau T, Muller B (2018) The Physiological Basis of Drought Tolerance in Crop Plants: A Scenario-Dependent Probabilistic Approach. Annual review of plant biology 69: 733-759
  27. Turc O, Tardieu F (2018) Drought affects abortion of reproductive organs by exacerbating developmentally-driven processes, via expansive growth and hydraulics. Journal of Experimental Botany 69: 3245-3254.
  28. Blanchet, N., Casadebaig, P., Debaeke, P., Duruflé, H., Gody, L., Gosseau, F., Langlade, N.B., Maury, P., 2018. Data describing the eco-physiological responses of twenty-four sunflower genotypes to water deficit. Data in Brief 21, 1296–1301
  29. Gosseau, F., Blanchet, N., Varès, D., Burger, P., Campergue, D., Colombet, C., Gody, L., Liévin, J.-F., Mangin, B., Tison, G., Vincourt, P., Casadebaig, P., Langlade, N.B., 2018. Heliaphen, an Outdoor High-Throughput Phenotyping Platform for Genetic Studies and Crop Modeling. Front. Plant Sci. 9. https://doi.org/10.3389/fpls.2018.01908
  30. van Eeuwijk F, Bustos-Korts D, Millet EJ, Boer M, Kruijer W, Thompson A, Malosetti M, Iwata H, Quiroz R, Kuppe C, Muller O, Blazakis KN, Yu K, Tardieu F, Chapman S (2018) Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding. Plant Science (in press) doi:10.1016/j.plantsci.2018.06.018

2017

  1. Al Makdessi N, Jean P-A, Ecarnot M, Gorretta N, Rabatel G, Roumet P., 2017. How plant structure impacts the biochemical leaf traits assessment from in-field hyperspectral images: A simulation study based on light propagation modeling in 3D virtual wheat scenes. Field Crops Research, 205, 95-105. http://dx.doi.org/10.1016/j.fcr.2017.02.001
  2. AL Saddik H., Han S.M., Simon J.C., Brousse O., Zunino E., Salon C., Cointault F. (2017). Solution de détection des maladies de la vigne par imagerie de drone. Diagnostic et réduction des pesticides à la parcelle. Revue des oenologues et des techniques vitivinicoles et œnologiques (162) : 32-34.
  3. Brichet, N., Fournier, C., Turc, O., Strauss, O., Artzet, S., Pradal, C., Welcker, C., Tardieu, F., Cabrera-Bosquet, L., 2017. A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform. Plant Methods. 13, 96.
  4. Cohen-Boulakia S., Belhajjame K., Collin O.,  Chopard J., Froidevaux C., Gaignard A., Hinsen K., Larmande P., Le Bras Y., Lemoine F., Mareuil F., Menager H., Pradal C., and Blanchet C. Scientific workflows for computational reproducibility in the life sciences: Status challenges and opportunities  In Future Generation Computer Systems, Volume 75, October 2017, Pages 284-298. https://doi.org/10.1016/j.future.2017.01.012
  5. Cointault F., Han, S., Rabatel G., Jay S., Rousseau D., Billiot B., Simon J.-C., Salon C. (2017). 3D imaging systems for agricultural applications. In: Julio C. Rodriguez-Quiñonez, Oleg Sergiyenko, Developing and applying optoelectronics in machine vision: IGI Global (Advances in Computational Intelligence and Robotics (ACIR)), 2017. 236-272
  6. Colombié, S., Beauvoit, B., Nazaret, C., Bénard, C., Vercambre, G., Le Gall, S., Biais, B., Cabasson, C., Maucourt, M., Bernillon, S., Moing, A., Dieuaide-Noubhani, M., Mazat, J. P., Gibon, Y. (2017) Respiration climacteric in tomato fruits elucidated by constraint-based modelling. New Phytol, 213 (4), 1726-1739.
  7. Coupel-Ledru A, Tyerman S, Masclef D, Lebon E, Christophe A, Edwards EJ, Simonneau T (2017) Abscisic acid down-regulates hydraulic conductance of grapevine leaves in isohydric genotypes only. Plant Physiology 175: 1121-1134
  8. Dambreville A, Griolet M, Rolland G, Dauzat M, Bédiée A, Balsera C, Muller B, Vile D, Granier C (2017) Phenotyping oilseed rape growth-related traits and their responses to water deficit: the disturbing pot size effect. Functional Plant Biology 44: 35-45
  9. Delalande M, Gavaland A, Mistou MN, Burger P, Meunier F, Marandel R, Miglionico G, Fargier S, Doussan C., 2017, Mesure de l’eau du sol : questions, méthodes et outils. Exemples d’application sur 2 plateformes champs du réseau « PHENOME », Cahier des techniques de l’INRA, 90, 1-32.
  10. Dzale Yeumo E., Alaux M., Arnaud E., Aubin S., Baumann U., Buche P., Cooper L., Ćwiek-Kupczyńska H., Davey R.P., Fulss R.A., Jonquet C., Laporte M.-A., Larmande P., Pommier C., Protonotarios V., Reverte C., Shrestha R., Subirats I., Venkatesan A., Whan A., Quesneville H. (2017) Developing data interoperability using standards: A wheat community use case. F1000Research 6:1843. [online] URL: http://dx.doi.org/10.12688/f1000research.12234.1
  11. Krzyzaniak Y, Brousse O, Cointault F, Heloir MC, Moreau E, Salon C, Simon JC, Trarieux C, Trouvelot S, Vaillant-Gaveau N, Adrian M (2017). Associer biostimulants, SDP, systèmes d’imagerie et de pulvérisation au service de la santé du vignoble. Revue des oenologues et des techniques vitivinicoles et œnologiques, 43-45
  12. Lacube S, Fournier C, Palaffre C, Millet EJ, Tardieu F, Parent B (2017) Distinct controls of leaf widening and elongation by light and evaporative demand in maize. Plant Cell and Environment 40: 2017-2028
  13. Lancelot, E., Bertrand, D., Hanafi, M. & Jaillais, B., (2017) Near-infrared hyperspectral imaging for following imbibition of single wheat kernel sections. Vibrational Spectroscopy, 92, 46-53.
  14. Liu, S., F. Baret, B. Andrieu, P. Burger and M. Hemmerlé (2017). Estimation of Wheat Plant Density at Early Stages Using High Resolution Imagery. Frontiers in Plant Science 8(739).
  15. Liu, S., F. Baret, D. Allard, X. Jin, B. Andrieu, P. Burger, M. Hemmerlé and A. Comar (2017). A method to estimate plant density & plant spacing heterogeneity: application to wheat crops. Plant Methods 13(1): 38.
  16. Liu, S., F. Baret, F. Boudon, S. Thomas, K. Zhao, C. Fournier, B. Andrieu, I. Kamran and B. de Solan (2017). Estimating wheat Green area index from ground-based LiDAR measurement through 3D ADEL-Wheat model. Agricultural and Forest Meteorology. Volume 247, 15 December 2017, Pages 12-20 https://doi.org/10.1016/j.agrformet.2017.07.007
  17. Madec, S., F. Baret, B. de Solan, S. Thomas, D. Dutartre, S. Jezequel, M. Hemmerlé, G. Colombeau and A. Comar (2017). High-Throughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates. Frontiers in Plant Science, 2017 Nov 27; 8:2002. doi: 10.3389/fpls.2017.02002. eCollection 2017
  18. Pradal C., Artzet S., Chopard J., Dupuis D., Fournier C., Mielewczik M., Nègre V., Neveu P., Parigot D., Valduriez P., Cohen-Boulakia S.. 2017. InfraPhenoGrid: A scientific workflow infrastructure for plant phenomics on the Grid. Future Generation Computer System, 67: p. 341-353.
  19. Roy J, Tardieu F, Tixier-Boichard M, Schurr U (2017) European infrastructures for sustainable agriculture. Nature Plants 3: 756-758
  20. Rymaszewski W, Vile D, Bediee A, Dauzat M, Luchaire N, Kamrowska D, Granier C, Hennig J (2017) Stress-Related Gene Expression Reflects Morphophysiological Responses to Water Deficit. Plant Physiology 174: 1913-1930
  21. Salon C., Avice J.-C., Colombie S., Dieuaide-Noubhani M., Gallardo-Guerrero K., Jeudy C., Ourry A., Prudent M., Voisin A.-S., Rolin D. (2017). Fluxomics links cellular functional analyses to whole-plant phenotyping. Darwin Review. Journal of Experimental Botany, 68 (9), 2083-2098.
  22. Tardieu F, Cabrera Bosquet L, Pridmore T, Bennett M (2017) Plant Phenomics, From Sensors to Knowledge. Current biology 27: R770-R783
  23. Tardieu, F ; Draye, X. ; Javaux, M. (2017). Update: Root Water Uptake and Ideotypes of the Root System: Whole-Plant Controls Matter. Vadose Zone Journal. 16. 10.2136/vzj2017.05.0107.
  24. Badouin, H., Gouzy, J., Grassa, C.J., Murat, F., Staton, S.E., Cottret, L., Lelandais-Brière, C., Owens, G.L., Carrère, S., Mayjonade, B., Legrand, L., Gill, N., Kane, N.C., Bowers, J.E., Hubner, S., Bellec, A., Bérard, A., Bergès, H., Blanchet, N., Boniface, M.-C., Brunel, D., Catrice, O., Chaidir, N., Claudel, C., Donnadieu, C., Faraut, T., Fievet, G., Helmstetter, N., King, M., Knapp, S.J., Lai, Z., Le Paslier, M.-C., Lippi, Y., Lorenzon, L., Mandel, J.R., Marage, G., Marchand, G., Marquand, E., Bret-Mestries, E., Morien, E., Nambeesan, S., Nguyen, T., Pegot-Espagnet, P., Pouilly, N., Raftis, F., Sallet, E., Schiex, T., Thomas, J., Vandecasteele, C., Varès, D., Vear, F., Vautrin, S., Crespi, M., Mangin, B., Burke, J.M., Salse, J., Muños, S., Vincourt, P., Rieseberg, L.H., Langlade, N.B., 2017. The sunflower genome provides insights into oil metabolism, flowering and Asterid evolution. Nature 546, 148–152. https://doi.org/10.1038/nature22380
  25. Debaeke, P., Casadebaig, P., Flenet, F., Langlade, N., 2017. Sunflower crop and climate change: vulnerability, adaptation, and mitigation potential from case-studies in Europe. OCL 24, D102. https://doi.org/10.1051/ocl/2016052
  26. Videcoq P, Barbacci A, Assor C, Magnenet V, Arnould O, Le Gall S, Lahaye M (2017) Examining the contribution of cell wall polysaccharides to the mechanical properties of apple parenchyma tissue using exogenous enzymes. J Exp Bot. 68 (18): 5137-5146.
  27. Alvarez Prado S, Cabrera-Bosquet L, Grau A, Coupel-Ledru A, Millet E, Welcker C, Tardieu F (2018) Phenomics allows identification of genomic regions affecting maize stomatal conductance with conditional effects of water deficit and evaporative demand. Plant, Cell and Environment 41: 314-326.

2016

  1. Allard A., C.A.M. Bink M., Martinez S., Kelner J.J., Legave J.M., di Guardo M., A. Di Pierro E., Laurens F., W. van de Weg E., Costes E. 2016. Detecting QTLs and putative candidate genes involved in budbreak and flowering time in an apple multiparental population. Journal of Experimental Botany. doi:10.1093/jxb/erw130.
  2. Bac-Molenaar J.A., Granier C., Vreugdenhil D., Keurentjes J.J.B. (2016) Genome wide association mapping of time-dependent growth responses to moderate drought stress in Arabidopsis. Plant, Cell & Environment. 39, 88-102. [multi-partners]
  3. Baerenfaller K., Massonnet C., Hennig L., Russenberger D., Sulpice R., Walsh S., Stitt M., Granier C., & Gruissem W. (2016) A long photoperiod relaxes energy management in Arabidopsis leaf six. Current Plant Biology 2: 34-45.
  4. Bénard C & Gibon Y (2016) Measurement of enzyme activities and optimization of continuous and discontinuous assays. Current Protocols in Plant Biology 1: 247-262.
  5. Bouchet, A. S., Laperche, A., Bissuel-Belaygue, C., Baron, C., Morice, J., Rousseau-Gueutin, M., ... & Maes, O. (2016). Genetic basis of nitrogen use efficiency and yield stability across environments in winter rapeseed. BMC genetics, 17(1), 131
  6. Cabrera-Bosquet L., Fournier C., Brichet N., Welcker C., Suard B., Tardieu F. (2016) High-throughput estimation of incident light, light interception and radiation-use efficiency of thousands of plants in a phenotyping platform. New Phytologist 212:269-81. http://dx.doi.org/10.1111/nph.14027
  7. Coupel-Ledru A., Lebon E., Christophe A., Gallo A., Gago P., Pantin F., Doligez A., Simonneau T. (2016) Reduced night time transpiration is a relevant breeding target for high water-use efficiency in grapevine. Proceedings of the National Academy of Sciences 113: 8963-8968. 10.1073/pnas.1600826113
  8. Ćwiek-Kupczyńska, H, Altmann, T, Arend, D, Arnaud, E, Chen, D, Cornut, G, Fiorani, F, Frohmberg, W, Junker, A, Klukas, C, Lange, M, Mazurek, C, Nafissi, A, Neveu, P, van Oeveren, J, Pommier, C, Poorter, H, Rocca-Serra, P, Sansone, S-A, Scholz, U, van Schriek, M, Seren, Ü, Usadel, B, Weise, S, Kersey, P, and Krajewski, P 2016 Measures for interoperability of phenotypic data: minimum information requirements and formatting. Plant Methods. DOI: http://doi.org/10.1186/s13007-016-0144-4 (Phenome mentioned in acknowledgements)
  9. Dauzat M., Dambreville A., Bresson J., Vile D., Muller B., Nègre V., Koch G., Vasseur F., Bédiée A., Desigaux M., Fourré D., Granier C. (2016) PHENOPSIS : Quelles évolutions technologiques du premier automate de phénotypage des plantes ? Le Cahier des Techniques de l’INRA (89).
  10. Dheilly E, Le Gall S, Guillou M. C, Renou J. P, Bonnin E, Orsel M, Lahaye M (2016) Cell wall dynamics during apple development and storage involves hemicellulose modifications and related expressed genes. BMC Plant Biol. 16: 201.
  11. Fernandez O, Urrutia M, Bernillon S, Giauffret C, Tardieu F, Le Gouis J, Langlade N, Charcosset A, Moing A & Gibon Y (2016) Fortune telling : metabolic markers of plant performance. Metabolomics 12: #158.
  12. Gómez-Candón D., Virlet N., Labbé S., Jolivot A., Regnard J.L. 2016. Field phenotyping of water stress at tree scale by UAV-sensed imagery: new insights for thermal acquisition and calibration. Precision Agric. DOI 10.1007/s11119-016-9449-6.
  13. Jeudy C, Adrian M, Baussard C, Bernard C, Bernaud E, Bourion V, Busset H, Cabrera L, Cointault F, Han S, Moreau D, Pivato B, Prudent M, Truong HT, Vernoud V, Voisin AS, Wipf D, Salon C. 2016. High throughput image acquisition of plant roots with RhizoTubes. Plant Methods. 12:31.
  14. Le Gall S, Even S, Lahaye M (2016) Fast Estimation of Dietary Fiber Content in Apple. J Agric Food Chem. 64: 1401-1405.
  15. Lièvre M., Granier C., Guédon Y. (2016) Identifying developmental phases in Arabidopsis thaliana rosette using integrative segmentation models. New Phytologist. 210, 1466-1478
  16. Oury V, Caldeira CF, Prodhomme D, Pichon JP, Gibon Y, Tardieu F, Turc O (2016) Is Change in Ovary Carbon Status a Cause or a Consequence of Maize Ovary Abortion in Water Deficit during Flowering? Plant Physiology 171: 997-1008
  17. Oury V, Tardieu F, Turc O (2016) Ovary Apical Abortion under Water Deficit Is Caused by Changes in Sequential Development of Ovaries and in Silk Growth Rate in Maize. Plant Physiology 171: 986-996
  18. Postic F., Doussan C., 2016, Benchmarking electrical methods for rapid estimation of root biomass, Plant Methods, 12:33, DOI 10.1186/s13007-016-0133-7
  19. Turc O, Bouteille M, Fuad-Hassan A, Welcker C, Tardieu F (2016) The growth of vegetative and reproductive structures (leaves and silks) respond similarly to hydraulic cues in maize. New Phytologist 212: 377-388
  20. Yang W., Pallas B., Durand J-B., Martinez S., Han M., Costes E. 2016. The impact of long-term water stress on tree architecture and production is related to changes in transitions between vegetative and reproductive growth in the ‘Granny Smith’ apple cultivar. Tree Physiology , 36 (11), pp.1369-1381.

2015

  1. Bac-Molenaar J.A., Vreugdenhil D., Granier C., Keurentjes J.J.B. (2015) Genome wide association mapping of growth dynamics detects time-specific and general QTLs. Journal of Experimental Botany. 66 (18) 5567-5580. [multi-partners]
  2. Beauchêne K (2015) Les innovations dans les tuyaux. Tolérance à la sécheresse : Phénofield est opérationnel -Perspectives Agricoles N°422
  3. Bénard C, Bernillon S, Biais B, Osorio S, Maucourt M, Ballias P, Deborde C, Colombié S, Cabasson C, Jacob D, Vercambre G, Gautier H, Rolin D, Génard M, Fernie AR, Gibon Y & Moing A (2015) Metabolomic profiling in tomato reveals diel compositional changes in fruit affected by source-sink relationships. Journal of Experimental Botany 66: 3391-3404.
  4. Bresson J., Vasseur F., Dauzat M., Koch G., Granier C. & Vile D. (2015) Quantifying spatial heterogeneity of whole-plant chlorophyll fluorescence during growth and in response to water stress. Plant Methods. 11:23. [Research & Development]
  5. Colombié S, Nazaret C, Bénard C, Biais B, Mengin V, Solé M, Fouillen L, Dieuaide-Noubhani M, Mazat JP, Beauvoit B & Gibon (2015) Modelling central metabolic fluxes by constraint-based optimization reveals metabolic reprogramming of developing Solanum lycopersicum (tomato) fruit. Plant J. 81: 24-39. doi: 10.1111/tpj.12685. Epub 2014 Nov 10.
  6. Dapp M., Reinders J., Bédiée A., Balsera C., Bucher E., Theiler G., Granier G. & Paszkowski J. (2015) Heterosis and inbreeding depression of epigenetic Arabidopsis hybrids. Nature Plants 1: 15092
  7. De Solan B, Baret F, Doussan C (2015) Capteurs. Un autre regard sur les cultures ; Fonctionnement des capteurs : De la mesure physique à la variable agronomique ; Capteurs et sélection variétale. Vers un nouveau potentiel de développement ; Aide au pilotage des cultures. Des technologies très complémentaires ; Evaluation des systèmes de culture. De nouveaux outils pour une agriculture performante" - Perspectives Agricoles N°419
  8. Gouache D, Pinochet X (2015) Organisation de la recherche – Une mutualisation des moyens et des financements -Perspectives Agricoles N°420
  9. Lopez G, Pallas B, Martinez S, Lauri P-É, Regnard J-L, Durel C-É, Costes E (2015) Genetic variation of morphological traits and transpiration in an apple core collection under well-watered conditions: Towards the identification of morphotypes with high water use efficiency. doi:10.1371/journal.pone.0145540
  10. Massonnet C., Dauzat M., Bédiée A., Vile D. & Granier C. (2015) Individual leaf area of early flowering arabidopsis genotypes is more affected by drought than late flowering ones: a multi-scale analysis in 35 genetically modified lines. American Journal of Plant Sciences. 6, 955-971. [Research]
  11. Paweł Krajewski, Dijun Chen, Hanna Ćwiek, Aalt D.J. van Dijk, Fabio Fiorani, Paul Kersey, Christian Klukas, Matthias Lange, Augustyn Markiewicz, Jan Peter Nap, Jan van Oeveren, Cyril Pommier, Uwe Scholz, Marco van Schriek, Björn Usadel and Stephan Weis (2015) Towards recommendations for metadata and data handling in plant phenotyping. Journal of Experimental Botany, Vol. 66, No. 18 pp. 5417–5427 doi:10.1093/jxb/erv271
  12. Pinochet, X., & Langlade, N. (2015). Phénotypage du tournesol: la recherche passe au haut débit. Perspectives agricoles, (424), 60-63
  13. Virlet N., Costes E., Martinez S., Kelner J.J., Regnard J.L. 2015. Multispectral airborne imagery in the field reveals genetic determinisms of morphological and transpiration traits of an apple tree hybrid population in response to water deficit. Journal of Experimental Botany. doi:10.1093/jxb/erv355.

2014

  1. Caldeira CF, Jeanguenin L, Chaumont F, Tardieu F (2014) Circadian rhythms of hydraulic conductance and growth are enhanced by drought and improve plant performance. Nature Communications 5:5365. ('Phenodyn' in M&M)
  2. Celton J.M., Kelner J.J., Martinez S., Bechti A., Khelifi Touhami A., James M.J., Durel C.E., Laurens F., Costes E. 2014. Fruit self-thinning: a trait to consider for genetic improvement of apple tree. PLOS ONE, March 2014, Volume 9, Issue 3, e91016.
  3. Coupel-Ledru, Aude, Éric Lebon, Angélique Christophe, Agnès Doligez, Llorenç Cabrera-Bosquet, Philippe Péchier, Philippe Hamard, Patrice This, et Thierry Simonneau. 2014. « Genetic Variation in a Grapevine Progeny (Vitis Vinifera L. Cvs Grenache×Syrah) Reveals Inconsistencies between Maintenance of Daytime Leaf Water Potential and Response of Transpiration Rate under Drought ». Journal of Experimental Botany, juin, eru228. doi:10.1093/jxb/eru228 
  4. Desnoues E, Gibon Y, Baldazzi V, Signoret V, Génard M & Quilot-Turion B (2014) Profiling sugar metabolism during fruit development in a peach progeny with different fructose-to-glucose ratios. BMC Plant Biology 14:336.
  5. Pinochet, X. (2014). L'experimentation dopee par le haut debit. Perspectives agricoles, (411), 78-80
  6. Schmalenbach I, Lei Z, Reymond M, Jimenez-Gomez JM (2014) The relationship between flowering time and growth responses to drought in the Arabidopsis Landsberg erecta x Antwerp-1 population. Front. Plant Sci., 11 November 2014 | http://dx.doi.org/10.3389/fpls.2014.00609
  7. Segonne SM, Bruneau M, Celton JM, Le Gall S, Francin-Allami M, Juchaux M, Laurens F, Orsel M, Renou JP. 2014. Multiscale investigation of mealiness in apple: an atypical role for a pectin methylesterase during fruit maturation. BMC Plant Biol. 2014 Dec 31;14(1):375. doi: 10.1186/s12870-014-0375-3.
  8. Vilmus I, Ecarnot M, Verzelen N, Roumet P. 2014. Monitoring N Leaf Resorption Kinetics by Near-Infrared Spectroscopy during Grain Filling in Durum Wheat in Different N Availability Conditions. Crop Science. 53:284–296. doi: 10.2135/cropsci2013.02.0099
  9. Virlet N., Lebourgeois V., Martinez S., Costes E., Labbé S., Regnard J.L. 2014. Stress indicators based on airborne thermal imagery for field phenotyping a heterogeneous tree population for response to water constraints. Journal of Experimental Botany, Vol. 65, No. 18, pp. 5429–5442. doi:10.1093/jxb/eru309.