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3 results on '"Negri, Mathilde"'

2. Rapport d'activité du Cirad. Réunion - Océan indien 2015 - 2021

Author

Jeuffrault, Eric (ed.), Baldet, Thierry, Cardinale, Eric, Tran, Annelise, Squarzoni, Cécile, Reynaud, Bernard, Nibouche, Samuel, Talibart, Nicolas, Grisoni, Michel, Jourda, Cyril, Robene, Isabelle, Delatte, Hélène, Rouget, Mathieu, Normand, Frédéric, Assemat, Sophie, Mottes, Charles, Huat, Joël, Grechi, Isabelle, Ratnadass, Alain, Soler, Alain, Vayssières, Jonathan, Ripoche, Aude, Versini, Antoine, Todoroff, Pierre, Christina, Mathias, Mialet, Isabelle, Porphyre, Vincent, Cerdan, Claire, Soria, Christian, Negri, Mathilde, Salinier, Jérémy, Miralles, Maëva, Sadaillan, Jean Marc, Jeuffrault, Eric (ed.), Baldet, Thierry, Cardinale, Eric, Tran, Annelise, Squarzoni, Cécile, Reynaud, Bernard, Nibouche, Samuel, Talibart, Nicolas, Grisoni, Michel, Jourda, Cyril, Robene, Isabelle, Delatte, Hélène, Rouget, Mathieu, Normand, Frédéric, Assemat, Sophie, Mottes, Charles, Huat, Joël, Grechi, Isabelle, Ratnadass, Alain, Soler, Alain, Vayssières, Jonathan, Ripoche, Aude, Versini, Antoine, Todoroff, Pierre, Christina, Mathias, Mialet, Isabelle, Porphyre, Vincent, Cerdan, Claire, Soria, Christian, Negri, Mathilde, Salinier, Jérémy, Miralles, Maëva, and Sadaillan, Jean Marc

Published
2022

3. Location-Based Plant Species Prediction Using A CNN Model Trained On Several Kingdoms - Best Method Of GeoLifeCLEF 2019 Challenge

Author

Negri, Mathilde, Servajean, Maximilien, Deneu, Benjamin, Joly, Alexis, Université Paul-Valéry - Montpellier 3 (UPVM), ADVanced Analytics for data SciencE (ADVANSE), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Scientific Data Management (ZENITH), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Servajean, Maximilien, Linda Cappellato, Nicola Ferro, David E. Losada, Henning Müller, Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; This technical report describes the model that achieved the best performance of the GeoLifeCLEF challenge, the objective of which was to evaluate methods for plant species prediction based on their geographical location. Our method is based on an adaptation of the Inception v3 architecture initially dedicated to the classification of RGB images. We modified the input layer of this architecture so as to process the spatialized environmental tensors as images with 77 distinct channels. Using this architecture, we did train several models that mainly differed in the used training data and in the predicted output classes. One of the main objective, in particular, was to compare the performance of a model trained with plant occurrences only to that obtained with a model trained on all available occurrences, including the species of other kingdoms. Our results show that the global model performs consistently better than the plant-specific model. This suggests that the convolutional neural network is able to capture some inter-dependencies among all species and that this information significantly improves the generalisation capacity of the model for any species.

Published
2019

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