Résultats pour : "réseau de neurones"

Linking genetic markers and crop model parameters using neural networks to enhance genomic prediction of integrative traits. Larue Florian, Rouan Lauriane, Pot David, Rami Jean-François, Luquet Delphine, Beurier Grégory. 2024. Frontiers in Plant Science, 15:1393965, 13 p.

Multivariate regional deep learning prediction of soil properties from near-infrared, mid-infrared and their combined spectra. Nyawasha Rumbidzai, Wadoux Alexandre M. J. C., Todoroff Pierre, Chikowo Régis, Falconnier Gatien, Lagorsse Maeva, Corbeels Marc, Cardinael Rémi. 2024. Geoderma Regional, 37:e00805, 11 p.

VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation. Madec Simon, Irfan Kamran, Velumani Kaaviya, Baret Frédéric, David Etienne, Daubige Gaetan, Bernigaud Samatan Lucas, Serouart Mario, Smith Daniel, James Chrisbin, Camacho Fernando, Guo Wei, De Solan Benoit, Chapman Scott, Weiss Marie. 2023. Scientific Data, 10:302, 12 p.

A constrastive semi-supervised deep learning framework for land cover classification of satellite time series with limited labels. Ienco Dino, Gaetano Raffaele, Interdonato Roberto. 2023. Neurocomputing, 567:127031, 11 p.

Deep species distribution modeling from Sentinel-2 image time-series: A global scale analysis on the Orchid family. Estopinan Joaquim, Servajean Maximilien, Bonnet Pierre, Munoz François, Joly Alexis. 2022. Frontiers in Plant Science, 13, 21 p.

Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment. Deneu Benjamin, Servajean Maximilien, Bonnet Pierre, Botella Christophe, Munoz François, Joly Alexis. 2021. PLoS Computational Biology, 17 (4):e1008856, 21 p.

Graph convolutional and attention models for entity classification in multilayer networks. Zangari Lorenzo, Interdonato Roberto, Calió Antinio, Tagarelli Andrea. 2021. Applied Network Science, 6:87, 36 p.

Identification and counting of sorghum panicles using artificial intelligence based drone field phenotyping. Mbaye Modou, Audebert Alain. 2021. Advances in Artificial Intelligence and Machine Learning, 1 (3) : 234-240.

Multi-sensor land cover classification with sparsely annotated data based on Convolutional Neural Networks and Self-Distillation. Gbodjo Jean Eudes, Montet Didier, Ienco Dino, Gaetano Raffaele, Dupuy Stéphane. 2021. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15 p.

Artificial neural networks to distinguish charcoal from Eucalyptus and native forests based on their mineral components. Guedes Ramalho Fernanda Maria, Santos Carvalho Geila, Gerardi Hein Paulo Ricardo, Napoli Alfredo, Wojcieszak Robert, Guimaraães Guilherme Luiz Roberto. 2020. Energy and Fuels, 34 (8) : 9599-9608.

Attention-based recurrent neural network for plant disease classification. Lee Sue Han, Goeau Hervé, Bonnet Pierre, Joly Alexis. 2020. Frontiers in Plant Science, 11:601250, 8 p.

Toward a large‐scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras. Lorieul Titouan, Pearson Katelin D., Ellwood Elizabeth R., Goeau Hervé, Molino Jean-François, Sweeney Patrick W., Yost Jennifer M., Sachs Joel, Mata-Montero Erick, Nelson Gil, Soltis Pamela S., Bonnet Pierre, Joly Alexis. 2019. Applications in Plant Sciences, 7 (3), n.spéc. Emerging Frontiers in Phenological Research:e01233, 14 p.

Deep recurrent neural networks for winter vegetation quality mapping via multitemporal SAR sentinel-1. Ho Tong Minh Dinh, Ienco Dino, Gaetano Raffaele, Lalande Nathalie, Ndikumana Emile, Osman Faycal, Maurel Pierre. 2018. IEEE Geoscience and Remote Sensing Letters, 15 (3) : 464-468.

Land cover classification via multitemporal spatial data by deep recurrent neural networks. Ienco Dino, Gaetano Raffaele, Dupaquier Claire, Maurel Pierre. 2017. IEEE Geoscience and Remote Sensing Letters, 14 (10) : 1685-1689.

Prediction of mass-transfer kinetics on product quality changes during a dehydration-impregnation-soaking process using artificial neural networks. Application to pork curing. Poligne Isabelle, Broyart B., Trystram Gilles, Collignan Antoine. 2002. Lebensmittel-Wissenschaft und Technologie - Food Science and Technology, 35 : 748-756.

A deep learning approach to species distribution modelling. Botella Christophe, Joly Alexis, Bonnet Pierre, Monestiez Pascal, Munoz François. 2018. In : Multimedia tools and applications for environmental and biodiversity informatics. Joly Alexis (ed.), Vrochidis Stefanos (ed.), Karatzas Kostas (ed.), Karppinen Ari (ed.), Bonnet Pierre (ed.). Cham : Springer, 169-199. (Multimedia Systems and Applications) ISBN 978-3-319-76444-3

Utilisation des réseaux de neurones artificiels en vue de l'optimisation d'un procédé de salage / séchage / fumage / cuisson de produits carnes par immersion en solution concentrée. Broyart B., Poligne Isabelle, Trystram Gilles, Collignan Antoine. 2002. In : Système d'information modélisation, optimisation commande en génie des procédés, 24-25 octobre 2002, Toulouse, France. s.l. : s.n., 6 p. SIMO 2002, Toulouse, France, 24 Octobre 2002/25 Octobre 2002.

Quantification du rôle des prédateurs généralistes dans la régulation du charançon du bananier grâce à de l'analyse d'images prises in situ. Tresson Paul. 2021. Montpellier : Montpellier SupAgro, 198 p. Thèse de doctorat : Ecologie des communautés : Montpellier SupAgro