Résumé
Plant diversity is one of the major elements in the development of human civilization and plays a crucial role in the functioning and stability of ecosystems. However, our knowledge of plants is far from complete given the impressive number of nearly 400,000 species, knowing that more than 2,000 species are discovered each year. Over the past two decades, the biodiversity informatics community has made considerable efforts to develop global initiatives, digital platforms and tools to help biologists organize, share, visualize and analyze biodiversity data. However, the burden of systematic plant identification severely penalizes the aggregation of new data and knowledge at the species level. Plant experts spend a lot of time and energy identifying species when their expertise could be more useful in analyzing the data collected. Automated identification has made considerable progress, particularly in recent years, thanks in particular to the development of convolutional neural networks (CNNs), as evidenced by the long-term evaluation of automated plant identification organized as part of the LifeCLEF initiative. Nowadays, the best systems evaluated so far are able to compete with human experts and more and more plant identification applications are being developed such as Pl@ntNet or Seek (iNaturalist). Thanks to their rapidly growing audience, they provide an opportunity to monitor biodiversity on a large scale and to aggregate new specific knowledge. However, these applications face the problem of being either too limited to the flora of particular regions or limited to the most common species, while there are more and more species with a transcontinental range such as naturalized alien species and cultivated plants. Fragmentation of identification in regional floras is less and less a reliable approach, while focusing only on the most common species on the planet is obviously not a better idea in terms of biodiversity. In this talk, we will discuss the possibilities and limitations of deep learning and high performance computing to build a plant identification system working at the scale of the world's flora.