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

Mathilde Negri 1 Maximilien Servajean 2 Benjamin Deneu 3 Alexis Joly 4
2 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
4 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : 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.
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Submitted on : Wednesday, December 4, 2019 - 9:51:17 AM
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Mathilde Negri, Maximilien Servajean, Benjamin Deneu, Alexis Joly. Location-Based Plant Species Prediction Using A CNN Model Trained On Several Kingdoms - Best Method Of GeoLifeCLEF 2019 Challenge. CLEF 2019 - Conference and Labs of the Evaluation Forum - Information Access Evaluation meets Multilinguality, Multimodality, and Visualization, Sep 2019, Lugano, Switzerland. ⟨hal-02392637⟩

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