Overview of PlantCLEF 2024: multi-species plant identification in vegetation plot images
2024
Goëau, Hervé | Espitalier, Vincent | Bonnet, Pierre | Joly, Alexis | Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [Occitanie])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université de Montpellier (UM) | Département Systèmes Biologiques (Cirad-BIOS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad) | Scientific Data Management (ZENITH) ; Centre Inria d'Université Côte d'Azur ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM) ; Université de Perpignan Via Domitia (UPVD)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Université de Montpellier Paul-Valéry (UMPV)-Université de Perpignan Via Domitia (UPVD)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Université de Montpellier Paul-Valéry (UMPV) | Computer and storage resources by GENCI at IDRIS thanks to the grant 2023-AD010113641R1 on the supercomputer Jean Zay’s the V100 partition. | Guglielmo Faggioli | Nicola Ferro | Petra Galuščáková | Alba García Seco de Herrera | Faggioli Guglielmo (ed.) | Ferro Nicola (ed.) | Galuščáková Petra (ed.) | García Seco de Herrera Alba (ed.)
Source Agritrop Cirad (https://agritrop.cirad.fr/613025/) * Autres projets (id;sigle;titre): 101060693;GUARDEN;(EU) safeGUARDing biodivErsity aNd critical ecosystem services across sectors and scales// 101060639;MAMBO;(EU) Modern Approaches to the Monitoring of BiOdiversity//
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显示更多 [+] 显示较少 [-]英语. Plot images are essential for ecological studies, enabling standardized sampling, biodiversity assessment, longterm monitoring and remote, large-scale surveys. Plot images are typically fifty centimetres or one square meter in size, and botanists meticulously identify all the species found there. The integration of AI could significantly improve the efficiency of specialists, helping them to extend the scope and coverage of ecological studies. To evaluate advances in this regard, the PlantCLEF 2024 challenge leverages a new test set of thousands of multi-label images annotated by experts and covering over 800 species. In addition, it provides a large training set of 1.7 million individual plant images as well as state-of-the-art vision transformer models pre-trained on this data. The task is evaluated as a (weakly-labeled) multi-label classification task where the aim is to predict all the plant species present on a high-resolution plot image (using the single-label training data). In this paper, we provide an detailed description of the data, the evaluation methodology, the methods and models employed by the participants and the results achieved.
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