Overview of PlantCLEF 2022: Image-based plant identification at global scale - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Conference Papers Year : 2022

Overview of PlantCLEF 2022: Image-based plant identification at global scale


It is estimated that there are more than 300,000 species of vascular plants in the world. Increasing our knowledge of these species is of paramount importance for the development of human civilization (agriculture, construction, pharmacopoeia, etc.), especially in the context of the biodiversity crisis. However, the burden of systematic plant identification by human experts strongly penalizes the aggregation of new data and knowledge. Since then, automatic identification has made considerable progress in recent years as highlighted during all previous editions of PlantCLEF. Deep learning techniques now seem mature enough to address the ultimate but realistic problem of global identification of plant biodiversity in spite of many problems that the data may present (a huge number of classes, very strongly unbalanced classes, partially erroneous identifications, duplications, variable visual quality, diversity of visual contents such as photos or herbarium sheets, etc). The PlantCLEF2022 challenge edition proposes to take a step in this direction by tackling a multi-image (and metadata) classification problem with a very large number of classes (80k plant species). This paper presents the resources and evaluations of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of key findings.
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lirmm-03793591 , version 1 (01-10-2022)



  • HAL Id : lirmm-03793591 , version 1


Hervé Goëau, Pierre Bonnet, Alexis Joly. Overview of PlantCLEF 2022: Image-based plant identification at global scale. CLEF 2022 - Conference and Labs of the Evaluation Forum, Sep 2022, Bologne, Italy. pp.1916-1928. ⟨lirmm-03793591⟩
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