Overview of PlantCLEF 2025: Multi-Species Plant Identification in Vegetation Quadrat Images
This work addresses the need for botanists and ecologists to accelerate plant biodiversity inventories and expand spatial coverage in ecological studies, though it is incremental as it builds on existing challenge frameworks.
The paper tackles the problem of multi-species plant identification in vegetation quadrat images by introducing the PlantCLEF 2025 challenge, which includes a new test set of 2,105 images covering around 400 species and a large training set of 1.4 million images, with participants achieving results assessed through a multi-label classification task.
Quadrat images are essential for ecological studies, as they enable standardized sampling, the assessment of plant biodiversity, long-term monitoring, and large-scale field campaigns. These images typically cover an area of fifty centimetres or one square meter, and botanists carefully identify all the species present. Integrating AI could help specialists accelerate their inventories and expand the spatial coverage of ecological studies. To assess progress in this area, the PlantCLEF 2025 challenge relies on a new test set of 2,105 high-resolution multi-label images annotated by experts and covering around 400 species. It also provides a large training set of 1.4 million individual plant images, along with vision transformer models pre-trained on this data. The task is formulated as a (weakly labelled) multi-label classification problem, where the goal is to predict all species present in a quadrat image using single-label training data. This paper provides a detailed description of the data, the evaluation methodology, the methods and models used by participants, and the results achieved.