Articles

Sapoukhina N, Boureau T and Rousseau D (2022) Plant disease symptom segmentation in chlorophyll fluorescence imaging with a synthetic dataset. Front. Plant Sci. 13:969205. doi: 10.3389/fpls.2022.969205

Turgut, K., Dutagaci, H., & Rousseau, D. (2022). RoseSegNet: An attention-based deep learning architecture for organ segmentation of plants. Biosystems Engineering, 221, 138-153.

Nizar Bouhlel, Vahid Akbari, Stéphane Méric and David Rousseau, Multivariate Statistical Modeling for Multitemporal SAR Change Detection Using Wavelet Transforms and Integrating Subband Dependencies. IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-18, 2022, Art no. 5237018, doi: 10.1109/TGRS.2022.3215783.

Bouhlel, N., & Rousseau, D. (2022). A Generic Formula and Some Special Cases for the Kullback–Leibler Divergence between Central Multivariate Cauchy Distributions. Entropy, 24(6), 838. https://doi.org/10.3390/e24060838

Ahmad, A., Sala, F., Paiè, P., Candeo, A., D'Annunzio, S., Zippo, A., ... & Rousseau, D. (2022). On the robustness of machine learning algorithms toward microfluidic distortions for cell classification via on-chip fluorescence microscopy. Lab on a Chip, 22(18), 3453-3463.

Turgut, K., Dutagaci, H., Galopin, G., & Rousseau, D. (2022). Segmentation of structural parts of rosebush plants with 3D point-based deep learning methods. Plant Methods, 18(1), 1-23.

Mohammad-Razdari, A., Rousseau, D., Bakhshipour, A., Taylor, S., Poveda, J., & Kiani, H. (2022). Recent advances in E-monitoring of plant diseases. Biosensors and Bioelectronics, 113953.

Rayan Eid, Claudine Landès, Alix Pernet, Emmanuel Benoît, Pierre Santagostini, Angélina El Ghaziri and Julie Bourbeillon. DIVIS: a semantic DIstance to improve the VISualisation of heterogeneous phenotypic datasets. BioData Mining, BioMed Central, 2022, 15 (1), pp.10. ⟨10.1186/s13040-022-00293-y⟩.

Osei-Kwarteng, M., Ayipio, E., Moualeu-Ngangue, D., Buck-Sorlin, G., & Stützel, H. (2022). Interspecific variation in leaf traits, photosynthetic light response, and whole-plant productivity in amaranths (Amaranthus spp. L.). PloS one, 17(6), e0270674.

Baleghi, Y., & Rousseau, D. (2021). An analytical proof on suitability of Cauchy-Schwarz Divergence as the aggregation criterion in Region Growing Algorithm. Image and Vision Computing, 104312.

Douarre, C., Crispim-Junior, C. F., Gelibert, A., Germain, G., Tougne, L., & Rousseau, D. (2021). CTIS-Net: a neural network architecture for compressed learning based on Computed Tomography Imaging Spectrometers. IEEE Transactions on Computational Imaging.

ElMasry, G., Mandour, N., Ejeez, H., Demilly, D., Al-Rejaie, S., Verdier, J., Rousseau, D. (2021). Multichannel imaging for monitoring chemical composition and germination capacity of cowpea (Vigna unguiculata) seeds during development and maturation. The Crop Journal.

Debs, N., Cho, T. H., Rousseau, D., Berthezène, Y., Buisson, M., Eker, O., Frindel, C. (2021). Impact of the reperfusion status for predicting the final stroke infarct using deep learning. NeuroImage: Clinical, 29, 102548.

Chéné, Y., Belin, É., Coadou, F., Chapeau-Blondeau, F., Hardouin, L., & Rousseau, D. (2021). Instrumentation et capteurs innovants appliqués au phénotypage automatisé des végétaux. In Instrumentation et Interdisciplinarité (pp. 239-244). EDP Sciences.

Zhang Y, Henke M, Buck-Sorlin GH., Li Y, Xu H, Liu X, Li T. (2021). Estimating canopy leaf physiology of tomato plants grown in a solar greenhouse: Evidence from simulations of light and thermal microclimate using a Functional-Structural Plant Model. Agricultural and Forest Meteorology, 307, 108494.

Ramananjatovo, T., Chantoiseau, E., Guillermin, P., Guénon, R., Delaire, M., Buck-Sorlin, GH., & Cannavo, P. (2021). Growth of Vegetables in an Agroecological Garden-Orchard System: The Role of Spatiotemporal Variations of Microclimatic Conditions and Soil Properties. Agronomy, 11(9), 1888.

Ramananjatovo, T., Chantoiseau, E., Buck-Sorlin, GH., Guillermin, P., Guénon, R., Delaire, M. and Cannavo, P. (2021). Microclimatic conditions affect lettuce growth in apple tree-lettuce intercropping. Acta Hortic. 1327, 237-244; DOI: 10.17660/ActaHortic.2021.1327.31; https://doi.org/10.17660/ActaHortic.2021.1327.31

Beroueg A, Buck-Sorlin GH, Couvreur V, Danjon F, Delory BM, et al.. (2021). Loïc Pagès, founding scientist in root ecology and modelling. in silico Plants, Oxford Academic, 3 (2), ⟨10.1093/insilicoplants/diab035⟩. ⟨hal-03517541⟩.

Julie Bourbeillon, Thomas Coisnon, Damien Rousselière, Julien Salanié. Characterising the Landscape in the Analysis of Urbanisation Factors: Methodology and Illustration for the Urban Area of Angers. Economie et Statistique / Economics and Statistics, INSEE, 2021, 528-529, pp.109 - 128. ⟨10.24187/ecostat.2021.528d.2062⟩.

Rachid Boumaza, Pierre Santagostini, Smail Yousfi and Sabine Demotes-Mainard. dad: an R Package for Visualisation, Classification and Discrimination of Multivariate Groups Modelled by their Densities. The R Journal (2021) 13:2, pages 179-207.

Zine-El-Abidine, M., Dutagaci, H., Galopin, G., & Rousseau, D. (2020). Assigning Apples to Individual Trees in Dense Orchards using 3D Color Point Clouds.Biosystem engineering.

Douarre, C., Crispim-Junior, C. F., Gelibert, A., Tougne, L., & Rousseau, D. (2020). On the value of CTIS imagery for neural-network-based classification: a simulation perspective. Applied optics, 59(28), 8697-8710.

Samiei, S., Rasti, P., Richard, P., Galopin, G., & Rousseau, D. (2020). Toward Joint Acquisition-Annotation of Images with Egocentric Devices for a Lower-Cost Machine Learning Application to Apple Detection. Sensors, 20(15), 4173.

Samiei, S., Rasti, P., Vu, J. L., Buitink, J., & Rousseau, D. (2020). Deep learning-based detection of seedling development. Plant Methods, 16(1), 1-11.

Ahmad, A., Frindel, C., & Rousseau, D. (2020). Detecting differences of fluorescent markers distribution in single cell microscopy: textural or pointillist feature space? Frontiers in Robotics and AI, 7, 39.

Dutagaci, H., Rasti, P., Galopin, G., & Rousseau, D. (2020). ROSE-X: an annotated data set for evaluation of 3D plant organ segmentation methods. Plant methods, 16(1), 1-14. [A97]

Garbez, M., Belin, E., Chéné, Y., Dones, N., Hunault, G., Relion, D., Rousseau D. & Galopin, G. (2020). A new approach to predict the visual appearance of rose bush from image analysis of 3D videos. Eur. J. Hortic. Sci, 85, 182-190.

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.

Leclerc, P., Ray, C., Mahieu-Williame, L., Alston, L., Frindel, C., Brevet, P. F., Montcel, B. & Rousseau, D. (2020). Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy. Scientific reports, 10(1), 1-9.

Debs, N., Rasti, P., Victor, L., Cho, T. H., Frindel, C., & Rousseau, D. (2020). Simulated perfusion MRI data to boost training of convolutional neural networks for lesion fate prediction in acute stroke. Computers in Biology and Medicine, 116, 103579.

Xu, L., Yang, Z., Ding, W., & Buck-Sorlin, GH. (2020). Physics-based algorithm to simulate tree dynamics under wind load. International Journal of Agricultural and Biological Engineering, 13(2), 26-32.

Langensiepen, M., Jansen, M. A., Wingler, A., Demmig-Adams, B., Adams III, W. W., Dodd, I. C., ... Buck-Sorlin GH & Munné-Bosch, S. (2020). Linking integrative plant physiology with agronomy to sustain future plant production. Environmental and experimental botany, 178, 104125.

Wang, W., Celton, J. M., Buck-Sorlin, GH., Balzergue, S., Bucher, E., & Laurens, F. (2020). Skin Color in Apple Fruit (Malus× domestica): Genetic and Epigenetic Insights. Epigenomes, 4(3), 13.

SA-E14-1 Chapeau-Blondeau, F., & Belin, E. (2020). Fourier-transform quantum phase estimation with quantum phase noise. Signal Processing, 170, 107441.

SA-E14-2 Leclerc, P., Ray, C., Mahieu-Williame, L., Alston, L., Frindel, C., Brevet, P. F. & Rousseau, D. (2020). Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy. Scientific reports, 10(1), 1-9.

SA-E14-3 Méline, V., Brin, C., Lebreton, G., Ledroit, L., Sochard, D., Hunault, G., ... & Belin, E. (2020). 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, 11, 213.

SA-E14-4 Desgeorges, T., Liot, S., Lyon, S., Bouviere, J., Kemmel, A., Trignol, A., Rousseau D, & Chazaud, B. (2019). Open-CSAM, a new tool for semi-automated analysis of myofiber cross-sectional area in regenerating adult skeletal muscle. Skeletal muscle, 9(1), 2.

SA-E14-5 Rasti, P., Wolf, C., Dorez, H., Sablong, R., Moussata, D., Samiei, S., & Rousseau, D. (2019). Machine Learning-Based Classification of the Health State of Mice Colon in Cancer Study from Confocal Laser Endomicroscopy. Scientific Reports, 9(1), 1-11.

SA-E14-6  Rasti, P., Ahmad, A., Samiei, S., Belin, E., & Rousseau, D. (2019). Supervised image classification by scattering transform with application to weed detection in culture crops of high density. Remote Sensing, 11(3), 249.

SA-E14-7 ElMasry, G., Mandour, N., Wagner, M. H., Demilly, D., Verdier, J., Belin, E., & Rousseau, D. (2019). Utilization of computer vision and multispectral imaging techniques for classification of cowpea (Vigna unguiculata) seeds. Plant methods, 15(1), 24.

SA-E14-8 Gillard, N., Belin, É., & Chapeau-Blondeau, F. (2019). Stochastic resonance with unital quantum noise. Fluctuation and Noise Letters, 18(03), 1950015.

SA-E14-9 Debs, N., Rasti, P., Victor, L., Cho, T. H., Frindel, C., & Rousseau, D. (2020). Simulated perfusion MRI data to boost training of convolutional neural networks for lesion fate prediction in acute stroke. Computers in Biology and Medicine, 116, 103579.

SA-E14-10 Douarre, C., Crispim-Junior, C. F., Gelibert, A., Tougne, L., & Rousseau, D. (2019). Novel data augmentation strategies to boost supervised segmentation of plant disease. Computers and Electronics in Agriculture, 165, 104967.

SA-E14-11 Samiei, S., Rasti, P., Daniel, H., Belin, E., Richard, P., & Rousseau, D. (2018). Toward a Computer Vision Perspective on the Visual Impact of Vegetation in Symmetries of Urban Environments. Symmetry, 10(12), 666.

SA-E14-12 Zweifel, S., Buquet, J., Caruso, L., Rousseau, D., & Raineteau, O. (2018). “FlashMap”-A Semi-Automatic Tool for Rapid and Accurate Spatial Analysis of Marker Expression in the Subventricular Zone. Scientific reports, 8(1), 1-13.

SA-E14-13 Zondaka, Z., Harjo, M., Khorram, M. S., Rasti, P., Tamm, T., & Kiefer, R. (2018). Polypyrrole/carbide-derived carbon composite in organic electrolyte: Characterization as a linear actuator. Reactive and Functional Polymers, 131, 414-419.

SA-E14-14 Douma, I., Rousseau, D., Sallit, R., Kodjikian, L., & Denis, P. (2018). Toward quantitative and reproducible clinical use of OCT-Angiography. PloS one, 13(7).

SA-E14-15 Gillard, N., Belin, E., & Chapeau-Blondeau, F. (2018). Enhancing qubit information with quantum thermal noise. Physica A: Statistical Mechanics and its Applications, 507, 219-230.

SA-E14-16 Giacalone, M., Rasti, P., Debs, N., Frindel, C., Cho, T. H., Grenier, E., & Rousseau, D. (2018). Local spatio-temporal encoding of raw perfusion MRI for the prediction of final lesion in stroke. Medical image analysis, 50, 117-126.

SA-E14-17 Garbez, M., Symoneaux, R., Belin, É., Caraglio, Y., Chéné, Y., Dones, N., ... & Rousseau, D. (2018). Ornamental plants architectural characteristics in relation to visual sensory attributes: a new approach on the rose bush for objective evaluation of the visual quality.

SA-E14-18 Gillard, N., Belin, E., & Chapeau-Blondeau, F. (2017). Qubit state detection and enhancement by quantum thermal noise. Electronics Letters, 54(1), 38-39.

SA-E14-19 Murtin, C., Frindel, C., Rousseau, D., & Ito, K. (2018). Image processing for precise three-dimensional registration and stitching of thick high-resolution laser-scanning microscopy image stacks. Computers in biology and medicine, 92, 22-41.

SA-E14-20 Gillard, N., Belin, E., & Chapeau-Blondeau, F. (2017). Stochastic antiresonance in qubit phase estimation with quantum thermal noise. Physics Letters A, 381(32), 2621-2628.

SA-E14-21 Chambon, A., Boureau, T., Lardeux, F., & Saubion, F. (2018). Logical characterization of groups of data: a comparative study. Applied Intelligence, 48(8), 2284-2303.

SA-E14-22 Denancé, Nicolas, et al. "Two ancestral genes shaped the Xanthomonas campestris TAL effector gene repertoire." New Phytologist 219.1 (2018): 391-407.

SA-E14-23 Ahmad, A., Frindel, C., & Rousseau, D. (2020). Detecting differences of fluorescent markers distribution in single cell microscopy: textural or pointillist feature space?. Frontiers in Robotics and AI, 7, 39.

SA-E14-24 Dutagaci, H., Rasti, P., Galopin, G., & Rousseau, D. (2020). ROSE-X: an annotated data set for evaluation of 3D plant organ segmentation methods. Plant methods, 16(1), 1-14.

SA-E14-25 Alston, L., Mahieu-Williame, L., Hebert, M., Kantapareddy, P., Meyronet, D., Rousseau, D.,  Montcel, B. (2019). Spectral complexity of 5-ALA induced PpIX fluorescence in guided surgery: a clinical study towards the discrimination of healthy tissue and margin boundaries in high and low grade gliomas. Biomedical optics express, 10(5), 2478-2492.

SA-E14-26 Sdika, M., Alston, L., Rousseau, D., Guyotat, J., Mahieu-Williame, L., & Montcel, B. (2019). Repetitive motion compensation for real time intraoperative video processing. Medical image analysis, 53, 1-10.

SA-E14-27 Alston, L., Rousseau, D., Hébert, M., Mahieu-Williame, L., & Montcel, B. (2018). Nonlinear relation between concentration and fluorescence emission of protoporphyrin IX in calibrated phantoms. Journal of biomedical optics, 23(9), 097002.

SA-E14-28 Courtial, Julia, et al. "Aldaulactone–an original phytotoxic secondary metabolite involved in the aggressiveness of Alternaria dauci on carrot." Frontiers in plant science 9 (2018): 502.

SA-E14-29 M. Garbez, E. Belin, Y. Chéné, N. Donès, G. Hunault, D. Relion, M. Sigogne, Symoneaux R., D. Rousseau, G. Galopin European Journal of Horticultural Science, 2020 (sous presse)

RA-E14-1  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, 109025.

RA-E14-2 ElMasry, G., Mandour, N., Al-Rejaie, S., Belin, E., & Rousseau, D. (2019). Recent applications of multispectral imaging in seed phenotyping and quality monitoring—An overview. Sensors, 19(5), 1090.

Chapitres d'ouvrages

S Hamdy, P Rasti, A Charrier, D Rousseau ; Advances in seed phenotyping and applications to seed testing/monitoring and breeding ; Focus on seed phenotyping with X-Ray imaging (to appear 2021).

E Belin, D Rousseau ; Biospeckle Imaging; A compendium of imaging modalities for biological and preclinicial research (IOP 2021).

E. Belin & D. Rousseau. (2021). Biospeckle imaging. Imaging Modalities for Biological and Preclinical Research: A Compendium, 1, I-8, ISBN: 978-0-7503-3059-6. IOP ebooks. Bristol, UK: IOP Publishing. [ https://ui.adsabs.harvard.edu/link_gateway/2021imb1.book...36R/doi:10.1088/978-0-7503-3059-6ch36 | 10.1088/978-0-7503-3059-6ch36 ]

F. Chapeau-Blondeau, E. Belin. Quantum signal processing for quantum phase estimation: Fourier transform versus maximum likelihood approaches, Annals of Telecommunications - annales des télécommunications, Springer, 2020, 75 (11-12), pp.641-653.

Gestion et coordination de livres scientifiques/édition de livres scientifiques

Special issue on Low-Cost Sensors and Vectors for Plant Phenotyping

https://www.mdpi.com/journal/sensors/special_issues/sensors_vectors_plant_phenotyping

Interventions dans des conférences /congrès et séminaires de recherche

N. Bouhlel and D. Rousseau, « Multi-Temporal SAR Change Detection using Wavelet Transforms, » 2022 30th European Signal Processing Conference (EUSIPCO), 2022, pp. 538-542, Belgrade, Serbia, https://ieeexplore.ieee.org/document/9909568

Nizar Bouhlel, Félix Mercier, David Rousseau, « Détection de changement dans les images SAR polarimétriques hétérogènes », 28ième GRETSI, 6-9 septembre 2022, Nancy, France.

P. Bouillon, A.L. Fanciullino, S. Balzergue, S. Hanteville, E. Belin, et al.Development and comparison of phenotypic methods for colour assessment and polyphenolic composition evaluation in red flesh apples. In IHC 2022 31st International Horticultural Congress, aug. 2022. Angers, France.

Julie Bourbeillon, Martel Céline, Maurin Alice, Christine Vandenkoornhuyse. En quoi l'accompagnement des élèves facilite-t-il leur engagement dans le cadre d'un travail collaboratif en mode hybride ? L'exemple d'un Wiki collaboratif. 32ème Congrès de l’Association Internationale de Pédagogie Universitaire, May 2022, Rennes, France.

Alix Pernet, Rayan Eid, Claudine Landès, Emmanuel Benoît, Pierre Santagostini, et al.. Construction of a semantic distance for inferring structure of the variability between 19th century Rosa varieties. IHC 2022 31st International Horticultural Congress, Aug 2022, Angers, France. ⟨hal-03823016⟩.

G. ElMasry, R. ElGamal, N. Mandour, S. Al-Rejaie, E. Belin, D Rousseau. Thermal imaging applications in seed quality evaluation, 13th International Conference on Agrophysics: Agriculture in changing climate, nov 2021, Lublin, Poland.

G. ElMasry, N. Mandour, N. Morsy, D. ElKhouly, S. Al-Rejaie, E. Belin, D Rousseau. High throughput phenotyping of cowpea seeds during developmental stages using multichannel imaging, 13th International Conference on Agrophysics: Agriculture in changing climate, nov 2021, Lublin, Poland.

M. Redon, T. Boureau, D. Rousseau, E. Belin. Approches d’active learning appliquées aux données du système robotisé de phénotypage Phenobean. Journée d’animation scientifique de l’axe ASM Biogenouest, 2021, Angers, France, 2021.

P. Santagostini, A. El Ghaziri. linmodel – Un package fournissant une application shiny pour les modèles linéaires et les tests non paramétriques. Rencontres R 2021, Paris, France.

E. Belin, T. Boureau. Phenotic : plate-forme d’imagerie pour semences et plantes. Journée d’animation Imabio, Angers, France, 2020.

E. Belin, R. Gardet, D. Demilly, T. Boureau. L’imagerie au service de l’évaluation de la qualité des semences et plantules, Congrès Gen2bio du réseau Biogenouest, 2020.

E. Belin, R. Gardet, T. Boureau. Phénotypage à haut-débit des stress biotiques sur les parties aériennes des plantes. Congrès Gen2bio du réseau Biogenouest, 2020.

Lysiane Hauguel, Tanguy Lallemand, Rayan Eid, Fabrice Dupuis, Sylvain Gaillard, et al.. ELTerm: a terminology module for a plant data management system. Journée Ouvertes de Biologie, Informatique, Mathématiques, JOBIM 2020, Jun 2020, Montpellier (virtuel), France.

D. Rousseau. Lowering the cost of spectral imaging and machine learning: Application to plant disease detection Chemo20219.

MA-E14-1  : Ordinal clustering of seed populations with data extracted from RGB imaging and X-ray tomography Hadhami Garbouge, Pierre Santagostini, Aurélie Charrier, Didier Demilly, David Rousseau UseR conference, 2019, Toulouse, France

MA-E14-2 : When spectro-imaging meets machine learning Clément Douarre, Laure Tougne, Carlos Crispim-Junior, Anthony Gelibert, David Rousseau Workshop on Machine Learning Assisted Image Formation, Jul 2019, Nice, France

MA-E14-3 : Revisiting SIFT for plant foliage in RGB images acquired on a turntable Helin Dutagaci, Etienne Belin, David Rousseau 7th International Workshop on Image Analysis Methods for the Plant Sciences, Jul 2019, Lyon, France

MA-E14-4A strategy for multimodal canopy images registration Clément Douarre, Carlos Crispim-Junior, Anthony Gelibert, David Rousseau, Laure Tougne 7th International Workshop on Image Analysis Methods in the Plant Sciences, Jul 2019, Lyon, France

MA-E14-5Deep learning based detection of cells in 3D light sheet fluorescence microscopy Ali Ahmad, Carole Frindel, Pejman Rasti, David Sarrut, David Rousseau Quantitative BioImaging Conference (QBI 2019), 2019, Rennes, France

MA-E14-6Graph encoding of multiscale structural networks from binary images with application to bio imaging

Nicolas Parisse, Aurélien Gourrier, Rachel Genthial, Delphine Débarre, Andrea Bassi, David Rousseau Computer Vision Problems in Plant Phenotyping (CVPPP 2018), Sep 2018, Newcastle, United Kingdom

MA-E14-7Perfusion MRI in stroke as a regional spatio-temporal texture Noelie Debs, Mathilde Giacalone, Pejman Rasti, Tae-Hee Cho, Carole Frindel, David Rousseau ISMRM 27th Annual Meeting & Exhibition, Jun 2018, Paris, France

MA-E14-8Synchrotron X-Ray Phase-Contrast Imaging To Simulate Diffusion Tensor MRI: Application to Tractograhy Timoté Jacquesson, Julie Bosc, Hugo Rositi, Marlène Wiart, Fabien Chauveau, Françoise Peyrin, David Rousseau, Carole Frindel Joint Annual Meeting ISMRM-ESMRMB 2018, 2018, Non spécifié, France

MA-E14-9Learning on Deep Network without the Hot Air by Scattering Transform Application to Weed Detection in Dense Culture Pejman Rasti, Ali Ahmad, Etienne Belin, David Rousseau 7th International Workshop on Image Analysis for Plant Science (IAMPS), 2018, Nottingham, United Kingdom

Articles publiés dans des actes de conférences/congrès

Buck-Sorlin, G.H., Tavkhelidze, A., Kurth, W. 2022. A model of water and carbohydrate transport in fruit-bearing apple tree branches: effect of pruning-induced modifications in architecture. International symposium on innovative perennial crops management, International Horticultural Congress 2022, Angers, 14 – 19 August, 2022.

Buck-Sorlin, G.H., Bournet, P.-E., Rossdeutsch, L., Truffault, V. 2022. Optimizing photosynthetic activity of high-wire cucumber production systems using a functional-structural plant modelling approach. International symposium on innovative technologies and production strategies for sustainable controlled environment horticulture, International Horticultural Congress 2022, Angers, 14 – 19 August, 2022.

Domingo Molina Aiz, F., Buck-Sorlin, G.H., Marcelis, L.F.M., Fatnassi, F. 2022. How can plant modelling be a leverage for cropping system improvement by integrating plant physiology and smart horticulture? Workshop W5, International Horticultural Congress 2022, Angers, 14 – 19 August, 2022.

CP-E14-1Data augmentation techniques for deep learning: A tutorial David Rousseau, Sa Tasftaris ICASSP, 2019, Brighton, United Kingdom

CP-E14-2Data augmentation from RGB to chlorophyll fluorescence imaging Application to leaf segmentation of Arabidopsis thaliana from top view images Natalia Sapoukhina, Salma Samiei, Pejman Rasti, David Rousseau CVPR, 2019, Long Beach, États-Unis

Cp-E14-3Evaluation of the realism of an MRI simulator for stroke lesion prediction using convolutional neural network Noelie Debs, Meghane Decroocq, Tae-Hee Cho, David Rousseau, Carole Frindel MICCAI, 2019, Shenzhen, China

cp-E14-4 Digital image processing with quantum approaches Nicolas Gillard, Etienne Belin, François Chapeau-Blondeau 8th International Conference on Image and Signal Processing, ICISP 2018., 2018, Cherbourg, France. pp.360-369

cp-E14-5 Low-cost vision machine for high-throughput automated monitoring of heterotrophic seedling growth on wet paper support Pejman Rasti, Didier Demilly, Landry Benoit, Etienne Belin, Sylvie Ducournau, François Chapeau-Blondeau, David Rousseau Computer Vision Problems in Plant Phenotyping (CVPPP 2018), 2018, Newcastle, United Kingdom

cp-E14-6 An Image Processing Method Based on Features Selection for Crop Plants and Weeds Discrimination Using RGB Images Ali Ahmad, Rémy Guyonneau, Franck Mercier, Etienne Belin International Conference on Image and Signal Processing, ICISP 2018, 2018, Cherbourg, France. pp.3-10

cp-E14-7 On the value of graph-based segmentation for the analysis of structural networks in life sciences Denis Bujoreanu, Pejman Rasti, David Rousseau 2017 25th European Signal Processing Conference (EUSIPCO), 2017, Kos, Greece. pp.2664-2668

Outils et produits électroniques

Logiciels

Santagostini P, Bouhlel N (2023). mcauchyd: Multivariate Cauchy Distribution; Kullback-Leibler Divergence_. R package version 1.0.2, https://CRAN.R-project.org/package=mcauchyd.

Santagostini P, Bouhlel N (2022). mggd: Multivariate Generalised Gaussian Distribution; Kullback-Leibler Divergence_. R package version 1.0.2, https://CRAN.R-project.org/package=mggd.

Boumaza R, Santagostini P, Yousfi S, Hunault G, Bourbeillon J, Pumo B, Demotes-Mainard S (2021). dad: Three-Way / Multigroup Data Analysis Through Densities. R package version 4.0.0, https://CRAN.R-project.org/package=dad.

David Rousseau in the framework of industrial partnership with ZEISS enabled to boost significantly the sells of microscope Z1 while speeding up registration of images of 100 Go from several hours to some minutes. Fiji plugin published in Computers in Medicine and Biology 2018.

Bases de données

David Rousseau, Pejman Rasti: Annotated data set on colon cancer (SA-E14-5)

David Rousseau, Pejman Rasti: Organisation of AgTech Data challenge, first national data challenge organized on AgTech (SA-E14-6)

Instruments et méthodologies

Prototypes

David Rousseau, Pejman Rasti: ANR LABCOM ESTIM  (2017-2020) networks of depth imaging camera for the monitoring of 300 000 seedling delivered to AREXOR

Platformes and observatoires

PHENOTIC Platform labeled BIOGENOUEST, IBISA member of national infrastructure PHENOME

Autres produits

Activités éditoriales

E. Belin & D. Rousseau. (2021). Biospeckle imaging. Imaging Modalities for Biological and Preclinical Research: A Compendium, 1, I-8, ISBN: 978-0-7503-3059-6. IOP ebooks. Bristol, UK: IOP Publishing. [ https://ui.adsabs.harvard.edu/link_gateway/2021imb1.book...36R/doi:10.1088/978-0-7503-3059-6ch36 | 10.1088/978-0-7503-3059-6ch36 ]