Productions (publis et thèses)

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Productions (publis et thèses)

Productions

Retrouvez ici les publications issues des thèses et post-docs financés et cofinancés par #DigitAg par année, depuis le début du projet.

 

Station Météo1

2023

  • Martin T (2023), Le robot de traite : une machine pour tous… ou presque, Séminaire Nouvelles pratiques agricoles et transformations du travail, RIU Travail INRAE ACT, Mar 2023, Montpellier, France,https://hal.inrae.fr/hal-04123285
     
  • Pasquel D, Roux S, Tisseyre B and Taylor J (2023), A new metric to evaluate spatial crop model performances, Proceedings of the 14th European Conference on Precision Agriculture, Bologna, Italy, July 02-06, https://doi.org/10.3920/978-90-8686-947-3 
     
  • Vargas-Rojas F, Polleres A, Cabrera-Bosquet L, and Symeonidou D (2023) ”PhyQus: Automatic Unit Conversions for Wikidata Physical Quantities.” In 4rd Wikidata Workshop (co-located with ISWC2023)
     
  • Durand M, Dourmad JY, Gaillard C (2023), Prédiction de l’activité journalière de truies gestantes à partir de créneaux ponctuels d’analyse vidéo manuelle, 55ème Journées de la Recherche Porcine, Saint Malo, Session Bien-être animal 
  • Couasnon M, Gaillard C, Durand M (2023) Comportement de truies gestantes après répétition d’une compétition alimentaire, 55ème Journées de la Recherche Porcine, Saint Malo, Session Bien-être animal 
     
  • Deroiné C, Misrach M, Durand M, Gaillard C (2023), Effets à court et moyen terme de différents types de sons sur le comportement de truies gestantes, 55ème Journées de la Recherche Porcine, Saint Malo, Session Bien-être animal 
  • Durand M, Gagnon P, Cloutier L, Dumas G, Guay F, Dourmad JY, Gaillard C (2023), Prédiction de la température corporelle de truies gestantes à l’aide d’une caméra thermique. Session Santé animale, 55ème Journées de la Recherche Porcine, Saint Malo
     
  • Durand M., Largouët C., Bonneau L., Dourmad J.Y., Gaillard C (2023), Prediction of daily nutritional requirements of gestating sows based on their behaviour and Machine learning methods, 14ème European Symposium of Porcine Health Management (ESPHM), Welfare and Nutrition session, Thessalonique, Grèce
     
  • Gaillard C., Ribas C., Durand M (2023) Precision feeding, recent advances for gestating sows and dairy cow, Session 81. Precision Feeding, 74ème Congrès annuel de l’EAAP, Lyon, France, 2023 
     
  • Zaitsev O, Vendel F, Delay E (2023) Cormas: The Software for Participatory Modelling and its Application for Managing Natural Resources in Senegal, The 2nd workshop on Resource AWareness of Systems and Society (RAW 2023), Aug 2023, Limassol, Cyprus
     
  • Castro Alvarado E, Bégué, A., Leroux, L., & Gaetano, R. (2023). A multi-year land use trajectory strategy for non-active agricultural land mapping in sub-humid West Africa, International Journal of Applied Earth Observation and Geoinformation, https://doi.org/10.1016/j.jag.2023.103398
     
  • Martin T, Schnebelin E (2023), Agriculture numérique : une promesse au service d’un nouvel esprit du productivisme, Natures Sciences Sociétés, https://institut-agro-montpellier.hal.science/hal-04066002/
     
  • Gaci B, Abdelghafour F, Ryckewaert M, Mas-Garcia S, Louargant M, Verpont F, Laloum Y, Moronvalle A, Bendoula R, Roger JM (2023), Visible – Near infrared hyperspectral dataset of healthy and infected apple tree leaves images for the monitoring of apple fire blight, Data in Brief, Volume 50, 2023,109532, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2023.109532
     
  • Gaci B, Abdelghafour F, Ryckewaert M, Mas-Garcia S, Louargant M, Verpont F, Laloum Y, Bendoula R, Chaix G,Roger JM (2023), A novel approach to combine spatial and spectral information from hyperspectral images, Chemometrics and Intelligent Laboratory Systems, Volume 240, 104897, ISSN 0169-7439, https://doi.org/10.1016/j.chemolab.2023.104897
     
  • Cyrille Ahmed Midingoyi, Christophe Pradal, Andreas Enders, Davide Fumagalli, Patrice Lecharpentier, Hélène Raynal, Marcello Donatelli, Davide Fanchini, Ioannis N. Athanasiadis, Cheryl Porter, Gerrit Hoogenboom, F.A.A. Oliveira, Dean Holzworth, Pierre Martre (2023), Crop modeling frameworks interoperability through bidirectional source code transformation, Environmental Modelling & Software, https://doi.org/10.1016/j.envsoft.2023.105790
     
  • Vargas-Rojas F, Cabrera-Bosquet L, Symeonidou D (2023), QAVAN: Query-answering approach for actionable numerical relationships over Knowledge Graphs, Knowledge-Based Systems Journal. JCR: Q1, I.F.: 8.8
     
  • Abarnou J, Durand M, Dourmad JY, Gaillard C (2023), Effects of induced thermal conditions on gestating sows’ behaviors and energy requirements, J. Anim. Sci., 10.1093/jas/skac413 
     
  • Durand M, Largouët C, Bonneau de Beaufort L, Dourmad JY, Gaillard C (2023), A dataset to study group-housed sows’ individual behaviours and production responses to different short-term events, Animal Open Space, 2, 100039, 10.1016/j.anopes.2023.100039
     
  • Durand M, Dourmad JY, Julienne A, Couasnon M, Gaillard C (2023,) Effects of a competitive feeding situation on the behaviour and energy requirements of gestating sows, Appl. Anim. Behav. Sci., https://doi.org/10.1016/j.applanim.2023.105884 
     
  • Gaillard C, Deroiné C, Misrach M, Durand M (2023), Effects over time of different types of sounds on gestating sows’ behaviour, Appl. Anim. Behav. Sci., https://doi.org/10.1016/j.applanim.2023.106012
     
  • Ngadi Scarpetta Y (2023), BFASTm-L2, an unsupervised LULCC detection based on seasonal change detection – An application to large-scale land acquisitions in Senegal, International Journal of Applied Earth Observation and Geoinformation, https://www.sciencedirect.com/science/article/pii/S1569843223002030

2022

  • Lemière L, Gosme M, Subsol G, Jaeger M (2022), FSPM applied to agroforestry system co-design, PMA 2022 - 7th International Symposium on Plant Growth Modeling, Simulation, Visualization, and Applications, Oct 2022, Wuxi, China, https://hal.inrae.fr/hal-03996877 
     
  • Martin T (2022), La mise en débat du travail dans les collectifs : Le cas du robot en AOP Reblochon, 5. Rencontres Nationales Travail en Agriculture, RMT Travail en agriculture, Nov 2022, Clermont-Ferrand, France, https://hal.inrae.fr/hal-04123256
     
  • Martin T (2022), Robot de traite : continuités et ruptures dans la division du travail, Politiques de la machine agricole - Approches sociologiques et historiques des trajectoires de mécanisation de l'agriculture (1945-2021), Université Paris Dauphine, Jun 2022, Paris, France, https://hal.inrae.fr/hal-04066017
  • Laclef E, Debus N, Taillandier P, Hassoun P, Parisot, González-García E, Lurette A (2022), Simulation de l’impact de la pratique de l’insémination sans hormones sur les performances et l’alimentation d’un troupeau ovin laitier, 26èmes Rencontres autour des Recherches sur les Ruminants, 7-8 décembre 2022, Paris, France, https://hal.inrae.fr/hal-04033343
  • Laclef E, Debus N, Taillandier P, Hassoun P, Parisot, González-García E, Lurette A (2022), Introducing hormone-free insemination in dairy sheep farms challenges their feeding system design, Presented at the EAAP, 73th Annual Meeting of the European Federation of Animal Science, September 2022, Porto, Portugal, https://hal.inrae.fr/hal-03775185
     
  • Durand M, Gaillard C (2022), Comportements de truies gestantes en situation de compétition alimentaire, 54ème Journées de la Recherche Porcine, en ligne, Session Alimentation animale 
     
  • Orsini C, Durand M, Gaillard C (2022), Effet de l’enrichissement de l’environnement sur le comportement des truies gestantes, 54ème Journées de la Recherche Porcine, en ligne, Session Alimentation animale 
     
  • Abarnou J, Durand M, Gaillard C (2022), Effet de stress thermiques sur le comportement des truies en gestation, 54ème Journées de la Recherche Porcine, en ligne, Session Alimentation animale 
     
  • Lanthony M, Durand M, Guérin C, Gaillard C, Tallet C (2022) Hiérarchie dans les groupes de truies gestantes : méthodes de calcul, caractéristiques et lien avec les données d'alimentation. Session Bien-être animal, reproduction porcine et conduite de l’élevage porcin, 54ème Journées de la Recherche Porcine, en ligne
     
  • Durand M, Abarnou J, Julienne A, Orsini C, Gaillard C (2022) Effect of various short-term events on behaviours of gestating sows. Session 21. Innovative approaches to pig and poultry production, 73ème Congrès annuel de l’EAAP, Porto, Portugal
     
  • Durand M, Simon M, Foisil J, Dourmad JY, Largouët C, Gaillard C (2022)  Evaluation of the physical activity of a group of gestating sows using an artificial neural network. Session 41. Development and external validation of PLF tools for animal behaviour, health and welfare: pigs, sheep, beef and poultry, 73ème Congrès annuel de l’EAAP, Porto, Portugal
     
  • Durand M, Abarnou J, Julienne A, Orsini C, Dourmad JY, Gaillard C (2022), Effect of short-term events on the activity of gestating sows and their nutritional requirements. Session 5b. Impact of heat stress and other environmental challenges on energy and protein metabolism, Grenade, Espagne, 2022 . Animal - science proceedings, 13, p.445-447, 7ème EAAP International Symposium on Energy and Protein Metabolism and Nutrition (ISEP)
  • Bellon-Maurel V, Brossard L, Garcia F, Mitton N, Termier A (2022), Agriculture and Digital Technology: Getting the most out of digital technology to contribute to the transition to sustainable agriculture and food systems, https://hal.inrae.fr/hal-03604970
  • Ryckewaert M, Héran D, Simonneau T, Abdelghafour F, Boulord R, Saurin N, Moura D, Mas Garcia S, Bendoula R (2022), Physiological variable predictions using VIS–NIR spectroscopy for water stress detection on grapevine: Interest in combining climate data using multiblock method, Computers and Electronics in Agriculture, https://doi.org/10.1016/j.compag.2022.106973
  • Lopez-Fornieles E, Brunel G, Rancon F, Gaci B, Metz M, Devaux N, Taylor J, Tisseyre B, Roger J-M (2022), Potential of Multiway PLS (N-PLS) Regression Method to Analyse Time-Series of Multispectral Images: A Case Study in Agriculture, Remote Sensing, https://doi.org/10.3390/rs14010216
     
  • Ryckewaert M, Chaix G, Héran D, Zgouz A, Bendoula R (2022), Evaluation of a combination of NIR micro-spectrometers to predict chemical properties of sugarcane forage using a multi-block approach, Biosystems Engineering, https://doi.org/10.1016/j.biosystemseng.2022.02.019
     
  • Gaci B, Mas Garcia S, Abdelghafour F, Adrian J, Maupas F, Roger J-M (2022), Assessing the potential of a handheld visible-near infrared microspectrometer for sugar beet phenotyping, Journal of Near Infrared Spectroscopy, 30(3):122-129. doi:10.1177/09670335221083448

    Mahmoud R, Casadebaig P, Hilgert N et al. (2022), Species choice and N fertilization influence yield gains through complementarity and selection effects in cereal-legume intercrops, Agronomy Sustainable Devlopment, https://doi.org/10.1007/s13593-022-00754-y
  •  Debus N, Laclef E, Lurette A, Alhamada M, Tesniere A, González-García E, Menassol J.B, Bocquier F (2022), High body condition score combined with a reduced lambing to ram introduction interval improves the short-term ovarian response of milking Lacaune ewes to the male effect, Animal, https://hal.inrae.fr/hal-03651997
     
  • Laclef E, González-García E, Taillandier P, Hassoun P, Parisot S, Allain C, Portes D, Debus N, Lurette A (2022), Alternative hormone free reproduction management of a dairy sheep flock disrupts the farm’s annual feeding system calendar and its associated strategies, Journal of Dairy Science, https://doi.org/10.3168/jds.2022-22080
  • Laclef E, Debus N, Taillandier P, González-García E, Lurette A (2022), Modelling the long-term consequences of implementing hormone-free reproductive management on the sustainability of a dairy sheep farm, Computers and Electronics in Agriculture, http://dx.doi.org/10.2139/ssrn.4237482
     
  • Deneu B, Joly A, Bonnet P, Servajean M and Munoz F (2022), Very high resolution Species Distribution Modelling based on remote sensing imagery: how to capture fine-grained and large-scale vegetation ecology with convolutional neural networks ?, Frontiers in Plant Science, https://hal.inrae.fr/hal-03695760

2021

  • Delpuech X, Cheraiet A, Vergès A, Naud O, Codis S (2021), Pulvélab: an experimental vineyard for the development and evaluation of innovative digital solutions for precision spraying, OenoforumAt: Valtice - Centrum Excelence – Czech Republic, https://www.researchgate.net/publication/349440435_PULVELAB_AN_EXPERIMENTAL_VINEYARD_FOR_THE_DEVELOPMENT_AND_EVALUATION_OF_INNOVATIVE_DIGITAL_SOLUTIONS_FOR_PRECISION_SPRAYING 
     
  • Martin T, Quentin D, Gasselin P (2021), Diversité et spatialité de la France laitière par le prisme des entreprises du robot de traite, 15èmes Journées de Recherches en Sciences Sociales (JRSS), INRA, SFER, CIRAD, Dec 2021, Toulouse, France., https://hal.inrae.fr/hal-03514569
     
  • Schnebelin E (2021), Which digital uses for which ecologisation of agriculture?  The example of cereal farms in South-West France, European Association for Evolutionary Political Economy (EAEPE), 33rd conference, https://ideas.repec.org/p/hal/journl/hal-04009789.html
     
  • Laurent C, Rançon F, López Fornieles E, Scholasch T, Metay A, Taylor J, Tisseyre B (2021), Is it relevant to account for grapevine phenology in time series of satellite images? ECPA 2021, https://www.wageningenacademic.com/doi/10.3920/978-90-8686-916-9_18
     
  • Laurent C, Scholasch T, Tisseyre B, Metay A (2021), Building New Temperature Indices for a local understanding of grapevine physiology, XIIIth International Terroir Congress, Virtual Congress, Adelaide, Australia, https://ives-openscience.eu/6730/
     
  • Ryckewaert M (2021), Comparison between ParSketch-PLSDA and PLSDA in a context of large amounts of spectral data for sunflower genotype discrimination, NIR2021, https://hal.inrae.fr/hal-03783378
     
  • Signe J (2021), Extraction de sous-groupes exceptionnels de séries temporelles, RJCIA 2021 - Rencontres des Jeunes Chercheurs en Intelligence Artificielle, Jul 2021, Bordeaux / Virtual, France, https://hal.science/hal-03298742/document  
     
  • Najm E (2021), Reasoning on Data for Innovation in Agroecology, 2nd Inria-DFKI workshop, invited poster, https://hal-lirmm.ccsd.cnrs.fr/lirmm-03785968/document
     
  • Vargas-Rojas F (2021) Ontological formalisation of mathematical equations for phenomic data exploitation, In European Semantic Web Conference, pp. 176-185. Cham: Springer International Publishing
  • Laclef E, Debus N, Taillandier P, García EG, Lurette A (2021), Exploring the impact of within flock variability on hormone-free dairy sheep farm performances, Presented at the 72nd Annual Meeting of the European Federation of Animal Science, Wageningen Academic Publishers, https://hal.inrae.fr/hal-03376302
     
  • Debus N, Laclef E, Lurette A, Alhamada M, Tesniere A, González-García E, Menassol JB, Bocquier F (2021), Factors influencing the short term ovarian response of milking Lacaune ewes to the male effect, Presented at the EAAP, 72th Annual Meeting of the European Federation of Animal Science, August 2021, Davos, Switzerland, https://hal.inrae.fr/hal-03376369
     
  • Durand M, Julienne A, Dourmad JY, Gaillard C (2022), Effect of feed competition on activity and social behaviour of gestating sows. Session 42. Inclusive livestock nutrition: where we have a trad-off between performance, environmental sustainability and animal welfare, 72ème Congrès annuel de l’European Federation of Animal Science (EAAP), Davos, Suisse
     
  • Durand M, Renaudeau D, Gaillard C (2022), Use of infrared thermography and rectal thermometer to measure body temperature of gestating sows. Session 44. PLF methods for measuring health, welfare and caring for individual animals, 72ème Congrès annuel de l’European Federation of Animal Science (EAAP), Davos, Suisse
     
  • Gaillard C, Durand M (2022), Effect of sudden noises on gestating sows’ behaviour. Session 58. Animal behaviour : from horses to hens., 72ème Congrès annuel de l’European Federation of Animal Science (EAAP), Davos, Suisse
     
  • Tresson P, Tixier P, Puech W, Carval D (2021), The challenge of biological control of Cosmopolites sordidus Germar (Col. Curculionidae): A review, Journal of Applied Entomology, https://agritrop.cirad.fr/599471/
     
  • Tresson P, Carval D, Tixier P, Puech W (2021), Hierarchical Classification of Very Small Objects: Application to the Detection of Arthropod Species, IEEE Access, https://ieeexplore.ieee.org/document/9411844
     
  • Martin T, Gasselin P, Hostiou N, Feron G, Laurens L, Purseigle L (2021), Robots and Transformations of Work on Farms: A Systematic Review, , https://hal.inrae.fr/hal-03259549
     
  • Schnebelin É, Labarthe P, Touzard JM (2021), How digitalisation interacts with ecologisation? Perspectives from actors of the French Agricultural Innovation System, Journal of Rural Studies 86, 599–610, https://doi.org/10.1016/j.jrurstud.2021.07.02
     
  • Pichon L, Brunel G, Payan JC et al. (2021), ApeX-Vigne: experiences in monitoring vine water status from within-field to regional scales using crowdsourcing data from a free mobile phone application, Precision Agriculture, https://doi.org/10.1007/s11119-021-09797-9
     
  • Boumaza K, Kalboussi N, Rapaport A, Roux S, Sinfort C (2021), Optimal control of a crop irrigation model under water scarcity, Optimal Control Applications and Methods, https://dx.doi.org/10.1002/oca.274
     
  • Biancolillo A, Preys S, Gaci B, Le Quere JL, Labouré H, Deuscher Z, Cheynier V, Sommerer N, Fayeulle N, Costet P, Hue C, Boulanger R, Alary K, Lebrun M, Lahon MC, Morel G, Maraval I, Davrieux F, Roger JM (2021), Multi-block classification of chocolate and cocoa samples into sensory poles, Food Chemistry, https://agritrop.cirad.fr/598703/
     
  • Ryckewaert M, Gorretta N, Henriot F, Gobrecht A, Heran D, Moura D, Bendoula R, Roger JM (2021), Potential of high-spectral resolution for field phenotyping in plant breeding: Application to maize under water stress, Computers and Electronics in Agriculture, https://doi.org/10.1016/j.compag.2021.106385
     
  • Oger B, Vismara P, Tisseyre B (2021), Combining target sampling with within field route-optimization to optimise on field yield estimation in viticulture, Precision Agriculture, https://link.springer.com/article/10.1007/s11119-020-09744-0
     
  • Petit J, Ait-Mouheb N, Mas García S, Metz M, Molle B, Bendoula R (2021), Potential of Visible/Near Infrared Spectroscopy coupled with chemometric methods for clogging estimation and discrimination in drip-irrigation, Biosystems Engineering, https://doi.org/10.1016/j.biosystemseng.2021.07.013
     
  • Lacoste M, Cook S, McNee M, Gale D, Ingram J, Bellon-Maurel V et al. (2021), On-Farm Experimentation to transform global agriculture, Nat Food, https://doi.org/10.1038/s43016-021-00424-4
     
  • Ryckewaert M, Metz M, Héran D, George P, Grezes-Besset B, Akbarinia R, Roger JM, Bendoula R (2021), Massive spectral data analysis for plant breeding using parSketch-PLSDA method: discrimination of sunflower genotypes, Biosystems Engineering, https://hal.inrae.fr/hal-03329674 
     
  • Metz M, Abdelghafour F, Roger J.M, Lesnoff M (2021), RoBoost-PLSR: Robust PLS Regression Method Inspired from Boosting Principles, Analytica Chimica Acta, https://publications.cirad.fr/une_notice.php?dk=59766
     
  • Oger B, Laurent C, Vismara P, Tisseyre B (2021), Is the optimal strategy to decide on sampling route always the same from field to field using the same sampling method to estimate yield?, Oeno One, https://doi.org/10.20870/oeno-one.2021.55.1.3334
     
  • Mishra P, Sadeh R, Ryckewaert M, Bino E, Polder G, Boer MP, Rutledge DN, Herrmann I(2021), A generic workflow combining deep learning and chemometrics for processing close-range spectral images to detect drought stress in Arabidopsis thaliana to support digital phenotyping, Chemometrics and Intelligent Laboratory Systems, https://doi.org/10.1016/j.chemolab.2021.104373 
     
  • Mas Garcia S, Ryckewaert M, Abdelghafour F, Metz M, Moura D, Feilhes C, Prezman F, Bendoula R (2021), Combination of multivariate curve resolution with factorial discriminant analysis for the detection of grapevine diseases using hyperspectral imaging. A case study: flavescence dorée, Analyst, Royal Society of Chemistry, https://doi.org/10.1039/D1AN01735G
     
  • Lopez EF, Brunel G, Devaux N, Rancon F, Pichon L, & Tisseyre B (2021), Potential of temporal series of Sentinel-2 images to define zones of vine water restriction, Precision agriculture’21, https://www.wageningenacademic.com/doi/10.3920/978-90-8686-916-9_65
     
  • Midingoyi CA, Pradal C, Enders A, Fumagalli D, Raynal H, Donatelli M, Athanasiadis IN, Porter C, Hoogenboom G, Holzworth D, Garcia F, Thorburn P, Martre P (2021), Crop2ML: An open-source multi-language modeling framework for the exchange and reuse of crop model components, Environmental Modelling and Software, https://doi.org/10.1016/j.envsoft.2021.105055 
     
  • Durand M, Dourmad JY, Largouët C, Tallet C, Gaillard C (2021), Alimentation de précision des truies gestantes : prise en compte de la santé, du comportement et de l’environnement, INRAE Prod. Anim., 34, p.293-304, https://doi.org/10.20870/productions-animales.2021.34.4.5369
     
  • Gaillard C, Durand M, Largouët C, Dourmad JY, Tallet C (2021), Effects of the environment and animal behavior on nutrient requirements for gestating sows: Future improvements in precision feeding, Anim. Feed Sci. Technol., 279, 115034, https://doi.org/10.1016/j.anifeedsci.2021.115034
     
  • Silvie PJ, Martin P, Huchard M, Keip P, Gutierrez A, Sarter S (2021), Prototyping a knowledge-based system to identify botanical extracts for plant health in sub-saharan africa, Plants, https://www.mdpi.com/2223-7747/10/5/896
     
  • Lefort G, Liaubet L, Marty-Gasset N, Canlet C, Vialaneix N, Servien R (2021), Joint Automatic Metabolite Identification and Quantification of a Set of 1H NMR Spectra, Analytical Chemistry, https://pubs.acs.org/doi/10.1021/acs.analchem.0c04232
     
  • Laclef E, Debus N, Taillandier P, González-García E, Lurette A (2021), REPROsheep: A model that integrates individual variability to optimise hormone-free reproduction management strategies for a dairy sheep flock, Computers and Electronics in Agriculture 189, 106412, https://doi.org/10/gmmq9h
  • Censi AM, Ienco D, Gbodjo YJE, Pensa RG, Interdonato R, Gaetano R (2021), Attentive Spatial Temporal Graph CNN for Land Cover Mapping from Multi Temporal Remote Sensing Data, IEEE Access, https://doi.org/10.1109/ACCESS.2021.3055554
     
  • Deneu B, Servajean M, Bonnet P, Botella C, Munoz F, Joly A (2021), Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment, PLoS Computational Biology, https://hal.inrae.fr/hal-03220977
     
  • Deneu B, Joly A, Bonnet P, Servajean M, Munoz F (2021), How Do Deep Convolutional SDM Trained on Satellite Images Unravel Vegetation Ecology?, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), https://hal.inrae.fr/hal-03167637
     
  • Heidsieck G, De Oliveira D, Pacitti E, Pradal C, Tardieu F, Valduriez P (2021), Cache-aware scheduling of scientific workflows in a multisite cloud, Future Generation Computer Systems, https://doi.org/10.1016/j.future.2021.03.012

2020

  • Keip P, Ferre S, Gutierrez A, Huchard M, Silvie S, Martin P (2020), Practical Comparison of FCA Extensions to Model Indeterminate Value of Ternary Data, The 15th International Conference on Concept Lattices and Their Applications (CLA 2020). Tallinn, Estonia, 29-1/06-07/2020, https://agritrop.cirad.fr/596562/
     
  • Gasselin P, Jarrige F, Martin T, Moraine M, Nougaredes B, et al (2020), La souveraineté alimentaire. Concept et conditions d'une mise en œuvre, Séminaire de l'UMR Innovation, UMR Innovation - Collectif AgriCités, Jun 2020, Montpellier, France, https://hal.inrae.fr/hal-03229183
     
  • Fauvel K, Balouek-Thomert D, Melgar D, Silva P, Simonet A, Antoniu G, Costan A, Masson V, Parashar M, Rodero I, and Termier A (2020), A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning, In Proceedings of the 34th AAAI Conference on Artificial Intelligence - AAAI'20, https://dx.doi.org/10.1609/aaai.v34i01.537
     
  • Fauvel K, Masson V, and Fromont E (2020), A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers., In Proceedings of the IJCAI-PRICAI 2020 Workshop on Explainable Artificial Intelligence - IJCAI-PRICAI'20, https://arxiv.org/abs/2005.14501
     
  • Volte G, Bourreau E, Giroudeau R, Naud O (2020), Exact method approaches for the differential harvest problem, 21ème congrès annuel de la société Française de Recherche Opérationnelle et d’Aide à la Décision (ROADEF 2020), https://link.springer.com/chapter/10.1007/978-3-030-58942-4_3
     
  • Commandré Y, Imbert E, Macombe S, Mignon S (2020), Transformation numérique d’une filière d’exportation : le cas des avocats en provenance du Pérou, XXVème Colloque de l’AIM - session pays émergents, Jun 2020, Marrakech, France, https://hal.science/hal-03011913
     
  • Commandré Y (2020), L’utilisation des images des producteurs agricoles pour la transparence alimentaire à l’aide de la blockchain, 9èmes rencontres des Perspectives critiques en management, Oct 2020, Paris, France, https://hal.science/hal-03011924
     
  • Commandré Y (2020), Use of the blockchain for food transparency in France and inclusion of agricultural producers, seminar ‘Building Inclusive Agricultural Value Chains’, Oct 2020, Wageningen, Pays-Bas, https://www.researchgate.net/publication/348995428_Use_of_the_blockchain_for_food_transparency_in_France_and_inclusion_of_agricultural_producers
     
  • Commandré Y (2020), Digitization in long food chains, 36th EGOS Colloquim, Pre-Colloquium PhD Workshop 2020, Jun 2020, Hamburg, Germany, https://www.researchgate.net/publication/348995365_Digitization_in_long_food_chain
     
  • Commandré Y, Imbert E, Macombe S, Mignon S (2020), When value chains digitization reduces producers' autonomy: the case of avocados' producers in Peru, 20th EURAM Annual Conference, Dublin, Ireland, https://www.researchgate.net/publication/348995359_When_value_chains_digitization_reduces_producers'_autonomy_the_case_of_avocados'_producers_in_Peru
     
  • Commandré Y, Mignon S, Macombe C (2020), Transparence alimentaire et blockchain, quelles conséquences pour les producteurs agricoles en France ?, XXIXème conférence de l’AIMS - ST AIMS Systèmes Alimentaires, Jun 2020, Toulouse, France, https://hal.science/hal-03011901
     
  • Lefort G, Liaubet L, Canlet C, Villa N, Servien R (2020), ASICS: identification and quantification of metabolites in complex 1H NMR spectra, European RFMF Metabomeeting 2020, Jan 2020, Toulouse, France, https://hal.inrae.fr/hal-02790207
     
  • Lefort G, Vialaneix N, Quesnel H, Pere MC, Billon Y, et al (2020), Étude de la maturité des porcelets en fin de gestation par une approche métabolomique multifluide, 52. Journées de la Recherche Porcine, Feb 2020, Paris, France. IFIP – Institut du Porc, https://hal.archives-ouvertes.fr/hal-02479994/
     
  • Heidsieck G, De Oliveira D, Pacitti E, Pradal C, Tardieu F, Valduriez P (2020), Distributed Caching of Scientific Workflows in Multisite Cloud., DEXA 2020 : International Conference on Database and Expert Systems Applications, https://dx.doi.org/10.1007/978-3-030-59051-2_4
     
  • Heidsieck G, De Oliveira D, Pacitti E, Pradal C, Tardieu F, Valduriez P (2020), Cache-aware scheduling of scientific workflows in multisite cloud, BDA 2020 : Gestion de Données – Principes, Technologies et Applications, https://doi.org/10.1016/j.future.2021.03.012
     
  • Lee S.H, Goëau H, Bonnet P, Joly A (2020), New perspectives on plant disease characterization based on deep learning, Computers and Electronics in Agriculture, https://doi.org/10.1016/j.compag.2020.105220
     
  • Sarron J, Sané CAB, Borianne P, et al (2020), Is machine learning efficient for mango yield estimation when used under heterogeneous field conditions?, Acta Hortic 201–208, https://doi.org/10.17660/ActaHortic.2020.1279.30
     
  • Fauvel K, Lin T, Masson V, Fromont E, and Termier A (2020), XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification, Mathematics, https://doi.org/10.3390/math9233137
     
  • Fauvel K, Fromont E, Masson V, Faverdin P, and Termier A(2020), Local Cascade Ensemble for Multivariate Data Classification, CoRR, https://dblp.org/rec/journals/corr/abs-2005-03645.html 
     
  • Lefort G, Servien R, Quesnel H et al. (2020), The maturity in fetal pigs using a multi-fluid metabolomic approach, Sci Rep 10, 19912, https://doi.org/10.1038/s41598-020-76709-8
     
  • Gbodjo, Y.J.E, Ienco D, Leroux L, Interdonato R, Gaetano R., Ndao B (2020), Object-Based Multi-Temporal and Multi-Source Land Cover Mapping Leveraging Hierarchical Class Relationships, Remote Sensing, https://inria.hal.science/hal-02931049/
     
  • Martin T, Gasselin P, Hostiou N, Feron G, Laurens L & Purseigle F (2020), Robots and Transformations of Work in Farms -Protocol for a Systematic Review, Protocol for a Systematic Review, https://univ-montpellier3-paul-valery.hal.science/hal-03259549/
     
  • Bélières JF, Randriamitantsoa D, Randrianirina H, Ralisoa N, Crespin-Boucaud A (2020), Étude chaîne de valeur pomme de terre.Partie 1: Importance de la culture de la pomme de terre pour les exploitations agricoles et rentabilité de la production de plants de semence et de consommations, CASEF,  MAEP, https://hal.umontpellier.fr/hal-02963574
     
  • Aleksovska I, Brun F, Raynaud I, Faivre R, Deudon O (2020), Prise en compte de l’incertitude des prévisions météorologiques dans les systèmes de l’aide à la décision, Phloème, 29 janvier 2020, Paris., https://ecophytopic.fr/pic/piloter/prise-en-compte-de-lincertitude-des-previsions-meteorologiques-dans-les-systemes-de
     
  • Deléglise H, Interdonato R, Roche M, Bégué A, Teisseire M, Maître d'Hôtel E (2020), Linking heterogeneous data for strengthening food security systems - Case of agricultural production in West Africa, Global Food Security, https://publications.cirad.fr/une_notice.php?dk=597783
     
  • Deléglise H, Interdonato R, Roche M, Bégué A, Schaeffer C, Cissé A (2020), News mining for food security: the case of Burkina Faso, Global Food Security, https://publications.cirad.fr/une_notice.php?dk=598778
     
  • Aleksovska I, Raynaud L, Faivre R, Brun F and Raynal M (2020), Design and evaluation of calibrated and seamless ensemble weather forecasts for crop protection applications, AMS, https://doi.org/10.1175/WAF-D-20-0128.1
     
  • Chen M, Brun F, Raynal M, Makowski D (2020), Delaying the first grapevine fungicide application reduces exposure on operators by half, Scientific Reports, https://www.hal.inserm.fr/inserm-02875104
     
  • Chen M, Brun F,Raynal M,Makowski D (2020), Forecasting severe grape downy mildew attacks using machine learning, PLoS ONE, https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0230254
     
  • Midingoyi CA, Pradal C, Athanasiadis IN, Donatelli M, Enders A, Fumagalli D, Garcia F, Holzworth D,  Hoogenboom G, Porter C, Raynal H, Thorburn P, Martre P (2020), Reuse of process-based models: automatic transformation into many programming languages and simulation platforms, in silico plants, https://doi.org/10.1093/insilicoplants/diaa007
     
  • Braud A, Dolques X, Gutierrez A, Huchard M, Keip P, Le Ber F, Martin P, Nica C and Silvie P (2020), Dealing with Large Volumes of Complex Relational Data using Relational Concept Analysis, Complex Data Analytics with Formal Concept Analysis, https://agritrop.cirad.fr/601324/
     
  • Lee S.H, Goëau H, Bonnet P, Joly A (2020), Attention-Based Recurrent Neural Network for Plant Disease Classification, Frontiers in Plant Science, https://www.frontiersin.org/articles/10.3389/fpls.2020.601250/full
     
  • Velumani K, Madec S, De Solan B, Lopez-Lozano R, Gillet J, Labrosse J, Jezequel S, Comar A, Baret F (2020), An automatic method based on daily in situ images and deep learning to date wheat heading stage, Field Crops Research, https://hal.inrae.fr/hal-03162912 
     
  • Kako N, Temple L, et Alexandre C (2020), Innovation numérique et transformation structurelle des économies africaines francophones, opportunités risquées pour le développement ?, Technologie et innovation 5, https://doi.org/10.21494/ISTE.OP.2020.0515
     
  • Temple L, Alexandre C (2020), Innovation numérique et transformation structurelle des économies africaines francophones, opportunités risquées pour le développement., Technologies et Innovations, https://agritrop.cirad.fr/595779/
     
  • Laborde A, Jaillais B, Roger JM, Metz M, Bouveresse DJR, Eveleigh L, & Cordella C (2020), Subpixel detection of peanut in wheat flour using a matched subspace detector algorithm and near-infrared hyperspectral imaging, Talanta, https://doi.org/10.1016/j.talanta.2020.120993
     
  • Aichouche F, Kalboussi N, Rapaport A, Harmand J (2020), Modeling and optimal control for production-regeneration systems - preliminary results -, European Control Conference 2020, ECC 2020, https://ieeexplore.ieee.org/document/9143741
     
  • Lamour J, Le Moguedec G, Naud O, Léchaudel M, Taylor J, et al. (2020), Evaluating the drivers of banana flowering cycle duration using a stochastic model and on farm production data, Precision Agriculture, https://link.springer.com/article/10.1007/s11119-020-09762-y
     
  • Metz M, Lesnoff M, Abdelghafour F, Akbarinia R, Masseglia F, Roger JM(2020), A “big-data” algorithm for KNN-PLS, Chemometrics and Intelligent Laboratory Systems, https://hal.inrae.fr/hal-02899789
     
  • Metz M, Biancolillo A, Lesnoff M, Roger JM (2020), A note on spectral data simulation, Chemometrics and Intelligent Laboratory Systems, https://agritrop.cirad.fr/595280/
     
  • Pichon L, Taylor J, Tisseyre B (2020), Using smartphone leaf area index data acquired in a collaborative context within vineyards in southern France, Oeno One, https://doi.org/10.20870/oeno-one.2020.54.1.2481
     
  • Cheraïet A, Naud O, Carra M, Codis S, Lebeau F, Taylor J (2020), An algorithm to automate the filtering and classifying of 2D LiDAR data for site-specific estimations of canopy height and width in vineyards, Biosystems Engineering, https://institut-agro-montpellier.hal.science/hal-03110685/ 
     
  • Hieu Nguyen B, Van Milgen J, Taghipoor M (2020), A procedure to quantify the feed intake response of growing pigs to perturbations, Animal, https://doi.org/10.1017/S1751731119001976
     
  • Goel R, Valentin S, Delaforge A, Fadloun S, Sallaberry A, Roche M, Poncelet P (2020), EpidNews: Extracting, exploring and annotating news for monitoring animal diseases, Journal of Computer Languages, https://doi.org/10.1016/j.cola.2019.100936    
     
  • Valentin S, Arsevska E, Falala S, De Goër J, Lancelot R, Mercier A, Rabatel G, Roche M (2020), PADI-web: A multilingual event-based surveillance system for monitoring animal infectious diseases, Computers and Electronics in Agriculture, https://hal.science/hal-02503294
     
  • Ienco D, Eudes Gbodjo YJ, Gaetano R, Interdonato R (2020), Weakly supervised learning for land cover mapping of satellite image time series via attention-based CNN, IEEE Access, https://hal.inrae.fr/hal-02941804 
     
  • Valentin S, Arsevska E, Falala S, (...), Rabatel G, Roche M (2020), PADI-web: A multilingual event-based surveillance system for monitoring animal infectious diseases, Computers and Electronics in Agriculture, 10.1016/j.compag.2019.105163
     
  • Valentin S, Arsevska E, Mercier A, Falala S, Rabatel J, Lancelot R, Roche M (2020), PADI-web: An Event-Based Surveillance System for Detecting, Classifying and Processing Online News, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), https://doi.org/10.1007/978-3-030-66527-2_7
     
  • Valentin S, Arsevska E, Falala S, De Goër J, Lancelot R, Mercier A, Rabatel J, Roche M (2020), PADI-web: A multilingual event-based surveillance system for monitoring animal infectious diseases, Computers and Electronics in Agriculture, https://doi.org/10.1016/j.compag.2019.105163
     
  • Crespin-Boucaud A, Lebourgeois V, Lo Seen D, Castets M, Bégué A (2020), Agriculturally consistent mappingof smallholder farming systems using remote sensing and spatial modelling, ISPRS—Int. Arch. Photogramm.Remote Sens. Spat. Inf. Sci, https://publications.cirad.fr/une_notice.php?dk=594282
     
  • Gbodjo JE, Ienco D and Leroux L (2020), Towards Spatio-Spectral analysis of Sentinel-2 Time Series data for land cover mapping, IEEE Geosci. Remote Sensing Lett., https://hal.inrae.fr/hal-02950334
     
  • Ienco D, Gbodjo J.E, Gaetano R, Interdonato R (2020), Generalized Knowledge Distillation for Multi-Sensor Remote Sensing Classification: An Application to Land Cover Mapping, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, https://doi.org/10.5194/isprs-annals-V-2-2020-997-2020
     
  • Gbodjo JE, Ienco D, Leroux L (2020), Toward Spatio-Spectral Analysis of Sentinel-2 Time Series Data for Land Cover Mapping, IEEE Geoscience and Remote Sensing Letters, https://hal.inrae.fr/hal-02950334
  • Crespin-Boucaud A, Lebourgeois V, Lo Seen D, Castets M, Bégué A (2020), Agriculturally consistent mapping of smallholder farming systems using remote sensing and spatial modelling, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 10.5194/isprs-archives-XLII-3-W11-35-2020
     
  • Valentin S, Mercier A, Lancelot R, Roche M, Arsevska E (2020), Monitoring online media reports for early detection of unknown diseases: Insight from a retrospective study of COVID-19 emergence, Transboundary and Emerging Diseases, https://doi.org/10.1111/tbed.13738
     
  • Goel R, Valentin S, Delaforge A, (...), Roche M, Poncelet P (2020), EpidNews: Extracting, exploring and annotating news for monitoring animal diseases, Journal of Computer Languages, https://doi.org/10.1016/j.cola.2019.100936
     
  • Heidsieck G, De Oliveira D, Pacitti E, Pradal C, Tardieu F, Valduriez P (2020), Cache-aware Scheduling of Scientific Workflows in Multisite Cloud, Future Generation Computer Systems,  https://doi.org/10.1016/j.future.2021.03.012  
     
  • Bonnet P, Joly A, Faton JM, Brown S, Kimiti D, Deneu B, Servajean M, Affouard A, Lombardo JC, Mary L, Vignau C, Munoz F (2020), How citizen scientists contribute to monitor protected areas thanks to automatic plant identification tools, Ecological Solutions and Evidence, https://doi.org/10.1002/2688-8319.12023
     
  • Joly A, Goëau H, Kahl S, Deneu B, Servajean M, Cole E, Picek L, Ruiz de Castañeda R, Bolon I, Durso A, Lorieul T, Botella C, Glotin H, Champ J, Eggel I, Vellinga WP, Bonnet P, Müller H (2020), Overview of LifeCLEF 2020: A System-Oriented Evaluation of Automated Species Identification and Species Distribution Prediction, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), https://link.springer.com/chapter/10.1007/978-3-030-58219-7_23
     
  • 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, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10.1007/978-3-662-62271-1_2
     
  • Heidsieck G, De Oliveira D, Pacitti E, Pradal C, Tardieu F, and Valduriez P (2020), Efficient Execution of Scientific Workflows in the Cloud through Adaptive Caching, TLDKS Journal, https://doi.org/10.1007/978-3-662-62271-1_2
     
  • Heidsieck G, De Oliveira D, Pacitti E, Pradal C, Tardieu F, Valduriez P (2020), Distributed caching of scientific workflows in multisite cloud, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), https://hal.inrae.fr/hal-02962579

2019

  • Tomasso L (2019), Multipass. “Episode Analyse juridique contractuelle des données de l’agriculture numérique”, Le Réseau Mixte Technologique du Numérique Agricole, https://numerique.acta.asso.fr/multipass-analyse-juridique-contractuelle-des-donnees-de-lagriculture-numerique/
     
  • Aleksovska I, Raynaud L, Faivre R, Brun F, Raynal M, Deudon O, Souverain F (2019), Accounting for the uncertainty of weather forecasts in decision support systems used for crop management, 12th EFITA International Conference, 27-29 June, 2019, Rhodes island, Greece, https://www.cabdirect.org/cabdirect/abstract/20193089494
     
  • Cheraiet A, Carra M, Lienard A, Codis S, Vergès A, Delpuech X, Naud O (2019), Investigation on LiDAR based indicators for predicting agrochemical deposition within a vine field., Precision Agriculture 2019, Proceedings of the 12th European Conference on Precision Agriculture, https://doi.org/10.3920/978-90-8686-888-9
     
  • G Heidsieck, D De Oliveira, E Pacitti, C Pradal, F Tardieu, P Valduriez (2019), Adaptive Caching for Data-Intensive Scientific Workflows in the Cloud, Dexa 2019:International Conference on Database and Expert Systems Applications, 452-466, https://agritrop.cirad.fr/593357/
     
  • Keip P, Ouzerdine A, Huchard M, Silvie P, Martin P (2019), Navigation conceptuelle dans une base de connaissances sur l'usage des plantes en santé animale et végétale, CORIA 2019, 16th French Information Retrieval Conference, https://agritrop.cirad.fr/593472/1/Keip_et_al_2019a.pdf
     
  • Keip P, Gutierrez A, Huchard M, Le Ber F, Sarter S, Silvie P, Martin P (2019), Effects of input data formalisation in relational concept analysis for a data model with a ternary relation, International Conference on Formal Concept Analysis (ICFCA 2019), https://link.springer.com/chapter/10.1007%2F978-3-030-21462-3_13
     
  • Bazin A, Carbonnel J, Huchard M, Kahn G, Keip P, Ouzerdine A (2019), On-demand relational concept analysis, IFCA 2019, https://arxiv.org/abs/1803.07847
     
  • Borianne P, Sarron J, Borne F and Faye E (2019), Deep mangoes: from fruit detection to cultivar identification in color images of mango trees, DISP'19 - International Conference on Digital image and Signal Processing, Oxford, Royaume-Uni, https://hal.umontpellier.fr/hal-02295256
     
  • Tresson P, Tixier P, Puech W, Carval D (2019), Insect interaction analysis based on object-detection and convolutional neural networks., IEEE MMSP, 10.1109/MMSP.2019.8901798
     
  • Sarron J, Sané C.A.B, Diatta P, Malézieux É, Faye É (2019), Plant diversity affects the productivity of Senegalese mango orchards: evidences from UAV photogrammetry, 4th World Congress of Agroforestry, https://agritrop.cirad.fr/592664/
     
  • Taleb Bendiab A, Ryckewaert M, Heran D, Vigreux C, Escalier R, Kribich R, Bendoula R (2019), Optical sensor combined with chemometric methods for the spray deposits characterization, NIR2019, Gold Coast (Australie), septembre 2019, https://inria.hal.science/hal-02319691/
     
  • Lachia N, Pichon L, Tisseyre B, (2019), A collective framework to assess the adoption of precision agriculture in France: Description and preliminary results after two years, Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, https://www.wageningenacademic.com/doi/10.3920/978-90-8686-888-9_105
     
  • Rabatel G, Lamour J, Moura D, Naud O (2019), A multispectral processing chain for chlorophyll content assessment in banana fields by UAV imagery, Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, https://doi.org/10.3920/978-90-8686-888-9_51
     
  • Kalboussi N, Roux S, Boumaza K, Sinfort C, Rapaport A (2019), About modeling and control strategies for scheduling crop irrigation, IFAC-PapersOnLine, https://doi.org/10.1016/j.ifacol.2019.11.007
     
  • Lamour J, Leroux C, Le Moguédec G, Naud O, Léchaudel M, Tisseyre B, (2019), Disentangling the sources of chlorophyll-content variability in banana fields, Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, https://doi.org/10.3920/978-90-8686-888-9_37
     
  • Oger B, Vismara P, Tisseyre B (2019), Échantillonnage sous contraintes en viticulture de précision, Proc. 12th European Conference on Precision Agriculture (ECPA 2019)., pp. 173-179, 2019, https://hal.science/lirmm-01924365/
     
  • Oger B, Vismara P, Tisseyre B (2019), Combining target sampling with route-optimization to optimise yield estimation in viticulture, Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, https://doi.org/10.1007/s11119-020-09744-0
     
  • Laurent C, Baragatti M, Taylor J, Scholasch T, Metay A, Tisseyre B (2019), Evaluation of a functional Bayesian method to analyse time series data in precision viticulture, 12th European Conference on Precision Agriculture (ECPA 2019), Montpellier SupAgro, Montpellier, France, https://dx.doi.org/10.3920/978-90-8686-888-9_7
     
  • Leroux C, Taylor J, Tisseyre B (2019), Production gap analysis - an operational approach to yield gap analysis using historical high-resolution yield data sets, Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, https://doi.org/10.3920/978-90-8686-888-9_8
     
  • Leroux C, Jones H, Tisseyre B (2019), An iterative region growing algorithm to generate fuzzy management zones within fields, Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, https://hal.inrae.fr/hal-02609780
     
  • Brunel G, Pichon L, Taylor J, Tisseyre B (2019), Easy water stress detection system for vineyard irrigation management, Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, https://doi.org/10.3920/978-90-8686-888-9_115
     
  • Pichon L, Leroux C, Geraudie V, Taylor J, Tisseyre B, (2019), Investigating the harmonization of highly noisy heterogeneous datasets hand-collected over the same study domain, Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, https://doi.org/10.3920/978-90-8686-888-9_91
     
  • Laurent C, Baragatti M, Taylor J, Tisseyre B, Metay A, Scholasch T (2019), Data mining approaches for time series data analysis in viticulture. Potential of the BLiSS (Bayesian Functional Linear Regression with Sparse Step functions) method to identify temperature effects on yield potential, Accepted to the 21st Group of international Experts for Cooperation on Viti-vinicultural Systems INternational Meeting (GiESCO 2019) , Aristotle UNiversity of Thessaloniki, Thessaloniki, Greece, https://ives-openscience.eu/4385/
     
  • Piot-Lepetit I, Florez M, Gauche K, Understanding the determinants of IT adoption in Agriculture using an integrated TAM-TOE model: A bibliometric analysis, 170e séminaire de l’EAAE, Montpellier les 15-17 mai 2019.  Session sur « Digital agriculture and Food chains », https://hal.inrae.fr/hal-02789959
     
  • Biao B, Temri L, Lachia N (2019), Co-construction of innovation processes: What types of innovation networks in digital agriculture? 170e séminaire de l’EAAE, Montpellier les 15-17 mai 2019.  Session sur « Digital agriculture and Food chains », https://hal.inrae.fr/hal-02788884
     
  • Piot-Lepetit I, Florez M, Gauche K (2019), Identifying the determinants of IT adoption in Agriculture: On the use of an integrated TAM-TOE model, 13e JRSS (Journées de Recherches en Sciences Socilaes (SFER-Inra-Cirad), Bordeaux, 12-13 décembre 2019. Session « Agriculture numériques et nouveaux usages en amont et à l’aval, https://hal.science/hal-02437653
     
  • Araba N and François-Heude A (2019), Price discovery and volatility spillovers in the French wheat market, Journées doctorales Augustin Cournot organisées par l’Université de Strasbourg, https://hal.science/hal-03088859
     
  • Lefort G, Liaubet L, Canlet C, Quesnel H, Vialaneix N, Servien R (2019), ASICS: a new R package for identification and quantification of metabolites in complex 1H NMR spectra, useR! 2019, https://hal.inrae.fr/hal-02789726
     
  • Gauthier R, Largouët C, Gaillard C, Cloutier L, Guay F, Dourmad JY (2019), Modélisation dynamique de l’utilisation des nutriments et des besoins individuels chez la truie en lactation, 51. Journées de la Recherche Porcine, https://hal.inrae.fr/hal-02738447
     
  • Nguyen Ba, H, Van Milgen J, Taghipoor M (2019), Modelling the feed intake response of growing pigs to diets contaminated with mycotoxins, Proceedings of the 9th Workshop on Modelling Nutrient Digestion and Utilization in Farm Animals, https://doi.org/10.1017/S175173112000083X
     
  • Fize J, Roche M, Teisseire M (2019), Matching heterogeneous textual data using spatial features, IEEE International Conference on Data Mining Workshops, ICDMW, https://agritrop.cirad.fr/589684/
     
  • Fize J, Roche M, Teisseire M (2019), Mapping Heterogeneous Textual Data: A Multidimensional Approach Based on Spatiality and Theme, Lecture Notes in Computer Science, https://doi.org/10.1007/978-3-030-34770-3_25
     
  • Deneu B, Servajean M, Botella C, Joly A (2019), Evaluation of deep species distribution models using environment and  co-occurrences, International Conference of the Cross-Language Evaluation Forum for European Languages, https://doi.org/10.48550/arXiv.1909.08825
     
  • Heidsieck G, De Oliveira D, Pacitti E, Pradal C, Tardieu F, Valduriez P (2019), Adaptive Caching for Data-Intensive Scientific Workflows in the Cloud, Dexa 2019: Database and Expert Systems Applications, https://sde.hal.science/hal-02174445/
     
  • Heidsieck G, De Oliveira D, Pacitti E, Pradal C, Tardieu F, Valduriez P (2019), Efficient Execution of Scientific Workflows in the Cloud through Adaptive Caching, BDA2019: Gestion de Données – Principes, Technologies et Applications, https://doi.org/10.1007/978-3-662-62271-1_2
  • Chloé A et Florentin Bationo M (2019), Une transformation des services de conseil agricole grâce au numérique ?, Grain de sel janvier – juin 2019 (no 77), 2p., https://agritrop.cirad.fr/593190/
     
  • Raynal M, Vergnes M, Brun F, Chen M (2019), Pronostic d’apparition du Mildiou : un challenge participatif en vue d’améliorer les Outils d’Aide à la Décision de demain, Technique IFV, https://rd-agri.fr/detail/DOCUMENT/acta_113
     
  • Faye É, Sarron J, Diatta J, Borianne P (2019), PixFruit Mangue : un outil d'acquisition, de gestion, et de partage de données pour une normalisation de la filière Mangue en Afrique de l'Ouest aux services de ses acteurs, AgriNumA, https://agritrop.cirad.fr/592757/
     
  • Lefort G, Vialaneix N, Quesnel H, Pere MC, Iannuccelli N, Canlet C, Paris A, Servien R, Liaubet L, Study of fetal pig maturity in relation with neonatal survival using a multi-fluids metabolomic approach, Presented at 15. Annual Conference of the Metabolomics Society (Metabolomics 2019), La Haye, NLD, https://hal.inrae.fr/hal-02791588
     
  • Chen M, Brun F, Raynal M., Debord C, Makowski D (2019), Use of probabilistic expert elicitation for assessing risk of appearance of grape downy mildew, Crop Protection, https://doi.org/10.1016/j.cropro.2019.104926
     
  • Chen M, Brun F, Raynal M, Makowski D (2019), Timing of grape downy mildew onset in bordeaux vineyards, Phytopathology, https://doi.org/10.1094/PHYTO-12-17-0412-R

    Chen M, Brun F, Raynal M,  Debord C & Makowski D (2019), Use of probabilistic expert elicitation for assessing risk of appearance of grape downy mildew, Crop Protection, https://doi.org/10.1016/j.cropro.2019.104926
     
  • Tresson P, Tixier P, Puech W, Bagny Beile L, Roudine S, Pagès C, Carval  D (2019), CORIGAN: Assessing multiple species and interactions within images, Methods in Ecology & Evolution, https://doi.org/10.1111/2041-210X.1328
     
  • Tresson P, Tixier P, Puech W, Carval D (2019), Insect interaction analysis based on object detection and CNN, IEEE 21st International Workshop on Multimedia Signal Processing, https://ieeexplore.ieee.org/document/8901798
     
  • Lefort G, Liaubet L, Canlet C (2019), ASICS: an R package for a whole analysis workflow of 1D 1H NMR spectra, Bioinformatics, https://doi.org/10.1093/bioinformatics/btz248
     
  • Bendiab AT, Ryckewaert M, Heran D, Escalier R, Kribich RK, Vigreux C, Bendoula R (2019), Coupling waveguide-based micro-sensors and spectral multivariate analysis to improve spray deposit characterization in agriculture, Sensors, https://inria.hal.science/hal-02297422/
     
  • Araya-Alman M, Leroux C, Acevedo-Opazo C, Guillaume S, Valdés-Gómez H, Verdugo-Vásquez N, Pañitrur-De la Fuente C, Tisseyre B (2019), A new localized sampling method to improve grape yield estimation of the current season using yield historical data, Precision Agriculture, https://link.springer.com/article/10.1007/s11119-019-09644-y
     
  • Lamour J, Naud O, Lechaudel M, Le Moguédec G, Taylor J and Tisseyre B (2019), Spatial analysis and mapping of banana crop properties: issues of the asynchronicity of the banana production and proposition of a statistical method to take it into account, Precision Agriculture, https://hal.umontpellier.fr/hal-02492098v1
     
  • Leroux C, Jones H, Pichon L, Taylor J, Tisseyre B, (2019), Automatic harmonization of heterogeneous agronomic and environmental spatial data, Precision Agriculture, https://link.springer.com/article/10.1007/s11119-019-09650
     
  • Cheraiet A, Carra M, Lienard A, Codis S, Vergès A, Delpuech X, Naud O (2019), Investigation on LiDAR-based indicators for predicting agrochemical deposition within a vine field, Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019, https://doi.org/10.3920/978-90-8686-888-9
     
  • Taylor J, Tisseyre B, Leroux C (2019), A simple index to determine if within-field spatial production variation exhibits potential management effects: application in vineyards using yield monitor data, Precision Agriculture, https://hal.inrae.fr/hal-02608141
     
  • Leroux C, Tisseyre B, (2019), How to measure and report within-field variability: a review of common indicators and their sensitivity, Precision Agriculture, https://hal.science/hal-02607864/
     
  • Leroux C, Jones H, Clenet A, Tisseyre B (2019), Knowledge discovery and unsupervised detection of within-field yield defective observations, Computers and Electronics in Agriculture, https://doi.org/10.1016/j.compag.2018.12.024
     
  • Leroux C, Taylor J, and Tisseyre B (2019), Production gap analysis – an operational approach to yield gap analysis using historical high-resolution yield data sets, Precision Agriculture,  https://doi.org/10.3920/978-90-8686-888-9
     
  • Pichon L, Leroux C, Macombe C, Taylor J, Tisseyre B, (2019), What relevant information can be identified by experts on unmanned aerial vehicles’ visible images for precision viticulture?, Precision Agriculture, https://link.springer.com/article/10.1007/s11119-019-09634-0
     
  • Kalboussi N, Roux S, Cheviron B, Harmand J, Rapaport A, Sinfort C (2019), Apport de la modélisation pour l’aide à la décision en vue de la réutilisation agricole des eaux usées traitées, Journal International Sciences et Techniques de l'Eau et de l'Environnement (JISTEE), Vol. 3 (1), pp.102-107, https://hal.science/hal-02623553/
     
  • Devaux N, Crestey T, Leroux C, Tisseyre B (2019), Potential of Sentinel-2 satellite images to monitor vine fields grown at a territorial scale, Oeno One, https://hal.inrae.fr/hal-02609421
     
  • Fauvel K, Masson V, Fromont É, Faverdin P, Termier A (2019), Towards sustainable dairy management - A machine learning enhanced method for estrus detection, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, https://inria.hal.science/hal-02190790
     
  • Gauthier R, Largouët C, Gaillard C, Cloutier L, Guay F, Dourmad JY (2019), Dynamic modeling of nutrient use and individual requirements of lactating sows, Journal of Animal Science, https://doi.org/10.1093/jas/skz167
     
  • Gaillard C, Gauthier R, Cloutier L, Dourmad JY (2019), Exploration of individual variability to better predict the nutrient requirements of gestating sows, Animal, https://doi.org/10.1093/jas/skz320
  • Yawogan Gbodjo JE, Ienco D, Leroux L (2019), Toward Spatio–Spectral Analysis of Sentinel-2 Time Series Data for Land Cover Mapping, IEEE Geoscience and Remote Sensing Letters, https://hal.inrae.fr/hal-02950334

2018

  • Renier L, Cardona A, Lécrivain E (2018), New arrangements for an agroecological management of animal health. The case of French farmers learning homeopathy, Presented at 13. European IFSA Symposium, Chania, Crete, GRC, https://prodinra.inra.fr/record/435567
     
  • Alexandre C (2018), Technologies de l’information et La Communication et Accompagnement Des Agriculteurs En Afrique de l’Ouest : Quelles Nouvelles Configurations Des Services de Conseil Agricole ? Proposition d’une Grille d’analyse, Conférence RRI, Nîmes, Atelier « Changements Organisationnels et Conseil : Nouvelles Formes d’accompagnement Du Processus d’innovations », 17p., https://agritrop.cirad.fr/592308/
     
  • Gauthier R, Guay F, Brossard L, Largouët C, Dourmad JY (2018), Precision feeding of lactating sows: development of a decision support tool to handle variability, EAAP 2018 - 69th Annual Meeting of the European Federation of Animal Science, Wageningen Academic Publishers, Dubrovnik, Croatia, https://hal.inrae.fr/view/index/identifiant/hal-01949645
     
  • Goel R, Sallaberry A, Fadloun S, (...), Valentin S, Poncelet P (2018), EpidNews: An epidemiological news explorer for monitoring animal diseases, 11th International Symposium on Visual Information Communication and Interaction, VINCI 2018; Vaxjo; Sweden; 13 August 2018 through 15 August 2018, https://www.hal.inserm.fr/AGROPOLIS/lirmm-01911990
     
  • Fize J, Roche M, Teisseire M (2018), Gemedoc: A text similarity annotation platform, Lecture Notes in Computer Science, https://hal.inrae.fr/hal-02608035
     
  • Valentin S, Lancelot R, Roche M (2018), How to combine spatio-temporal and thematic features in online news for enhanced animal disease surveillance? 22nd International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2018; Metropol Palace HotelBelgrade; Serbia; 3 September 2018 through 5 September 2018, https://doi.org/10.1016/j.procs.2018.07.283
     
  • Lee SH, Chan CS, Remagnino P (2018), Multi-Organ Plant Classification Based on Convolutional and Recurrent Neural Networks, IEEE Transactions on Image Processing, https://ieeexplore.ieee.org/document/8359391
     
  • Sarron J, Malézieux E, Bassirou Sané CA and Faye E (2018), Mango Yield Mapping at the Orchard Scale Based on Tree Structure and Land Cover Assessed by UAV, Remote Sensing, https://doi.org/10.3390/rs10121900
     
  • Kalboussi N, Rapaport A, Bayen T, ...Ellouze F, Harmand J (2018), Optimal control of membrane-filtration systems, IEEE Transactions on Automatic Control, https://hal.inrae.fr/view/index/identifiant/hal-01854430
     
  • Leroux C, Jones H, Pichon L, Guillaume S, Lamour J, Taylor J, Naud O, Crestey T, Lablee JL and Tisseyre B (2018), GeoFIS: An Open Source, Decision-Support Tool for Precision Agriculture Data, Agriculture, https://hal.science/hal-02068798
     
  • Kalboussi N, Harmand J, Rapaport A, ...Ellouze F, Ben Amar N (2018), Optimal control of physical backwash strategy - towards the enhancement of membrane filtration process performance, Journal of Membrane Science, https://www.hal.inserm.fr/MIPS/hal-01591027v1
     
  • Cerón-Vivas A, Kalboussi N, Morgan-Sagastume JM, Harmand J, Noyola A (2018), Model assessment of the prevailing fouling mechanisms in a submerged membrane anaerobic reactor treating low-strength wastewater, Bioresource Technology, https://doi.org/10.1016/j.biortech.2018.08.017
     
  • Alexandre C (2018), Comment l’utilisation des technologies de l’information et de la communication transforme-t-elle les dispositifs de conseil agricole ? Une enquête auprès de 16 services au Burkina Faso, Fiche de capitalisation AFD. Processus de réflexion sur le conseil agricole. Montpellier : CIRAD-AFD, 19p., https://doi.org/10.13140/RG.2.2.20335.25763
     
  • Bendiab AT, Ryckewaert M, Heran D, Escalier R, Kribich R.K, Vigreux C., Bendoula R (2018), Chalcogenide rib waveguides for the characterization of spray deposits, Optical Materials, https://doi.org/10.1016/j.optmat.2018.10.021
     
  • Kalboussi, Roux, Cheviron, Harmand, Rapaport, Sinfort (2018), Contribution of modeling to the decision support for agriculture reuse of treated wastewater, Journal International Sciences de l'Eau, du Climat et de l'Environnement, https://hal.science/hal-01975462/
     
  • Tisseyre B, Leroux C, Pichon L, Geraudie V, Sari T (2018), How to define the optimal grid size to map high resolution spatial data? Precision Agriculture 19(5), pp. 957-971, https://hal.inrae.fr/hal-02607457
     
  • Fauvel K, Masson V, Faverdin P, Termier A (2018), Data Science Techniques for Sustainable Dairy Management, ERCIM News, https://hal.univ-cotedazur.fr/ERCIM-NEWS/hal-01951807
     
  • Arsevska E, Valentin S, Rabatel J, De Herve JDG, Falala S, Lancelot R, Roche M (2018), Web monitoring of emerging animal infectious diseases integrated in the French Animal Health Epidemic Intelligence System, Web of sciences, https://doi.org/10.1371/journal.pone.0199960
     
  • Pradal S, Cohen-Boulakia, Heidsieck G, Pacitti E, Pradal C, Tardieu F, Valduriez P (2018), Distributed Management of Scientific Workflows for High-Throughput Plant Phenotyping., ERCIM News 2018, Smart Farming (pp.36-37), https://hal.inria.fr/hal-01948568
     
  • Leroux C, Jones H, Clenet A, Dreux B, Becu M, Tisseyre B (2018), A general method to filter out defective spatial observations from yield mapping datasets, Precision Agriculture,  https://hal.science/hal-02607448/
     
  • Leroux C, Jones H, Taylor J, Clenet A, Tisseyre B (2018), A zone-based approach for processing and interpreting variability in multi- temporal yield data sets, Computers and Electronics in Agriculture, https://doi.org/10.1016/j.compag.2018.03.029
     
  • Tisseyre B, Leroux C, Pichon L (2018), How to define the optimal grid size to map high resolution spatial data? Precision Agriculture, https://hal.inrae.fr/hal-02607457

2017

  • Tisseyre B, Leroux C (2017), How significantly different are your within field zones? Advances in Animal Biosciences, https://doi.org/10.1017/S2040470017000012
     
  • Leroux C, Jones H, Clenet A, Tisseyre B (2017), A New Approach for Zoning Irregularly-Spaced, Within-Field Data, Computers and Electronics in Agriculture, https://doi.org/10.1016/j.compag.2017.07.025
     
  • Leroux C, Jones H, Clenet A, Dreux B, Becu M and Tisseyre B (2017), Simulating yield datasets: an opportunity to improve data filtering algorithms, 11. European Conference on Precision Agriculture. ECPA 2017, Jul 2017, Edinburgh, United Kingdom, https://hal.inrae.fr/hal-02785008

Date de modification : 15 décembre 2023 | Date de création : 08 août 2022 | Rédaction : GL