Overview of PlantCLEF 2025: Multi-Species Plant Identification in Vegetation Quadrat Images

• URL: http://arxiv.org/abs/2509.17602v1
• Date: Mon, 22 Sep 2025 11:21:53 GMT
• Title: Overview of PlantCLEF 2025: Multi-Species Plant Identification in Vegetation Quadrat Images
• Authors: Giulio Martellucci, Herve Goeau, Pierre Bonnet, Fabrice Vinatier, Alexis Joly,
• Abstract summary: 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.<n>The goal is to predict all species present in a quadrat image using single-label training data.
• Score: 2.526933812879881
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Overview of LifeCLEF Plant Identification task 2020 [2.961584451143903]
  The LifeCLEF 2020 Plant Identification challenge (or "PlantCLEF 2020") was designed to evaluate to what extent automated identification on the flora of data deficient regions can be improved by the use of herbarium collections.<n>It is based on a dataset of about 1,000 species mainly focused on the South America's Guiana Shield, an area known to have one of the greatest diversity of plants in the world.<n>The challenge was evaluated as a cross-domain classification task where the training set consist of several hundred thousand herbarium sheets and few thousand of photos to enable learning a mapping between the two domains.
  arXiv  Detail & Related papers  (2025-09-23T06:35:19Z)
• Overview of PlantCLEF 2021: cross-domain plant identification [2.961584451143903]
  The LifeCLEF 2021 plant identification challenge was designed to assess the extent to which automated identification of flora can be improved by using herbarium collections.<n>It is based on a dataset of about 1,000 species mainly focused on the Guiana Shield of South America.<n>The challenge was evaluated as a cross-domain classification task where the training set consisted of several hundred thousand herbarium sheets and a few thousand photos.
  arXiv  Detail & Related papers  (2025-09-23T06:26:24Z)
• Overview of PlantCLEF 2022: Image-based plant identification at global scale [2.961584451143903]
  It is estimated that there are more than 300,000 species of vascular plants in the world.<n>Deep learning techniques now seem mature enough to address the ultimate but realistic problem of global identification of plant biodiversity.<n>The PlantCLEF2022 challenge edition proposes to take a step in this direction by tackling a multi-image (and metadata) classification problem.
  arXiv  Detail & Related papers  (2025-09-22T11:40:21Z)
• Overview of PlantCLEF 2023: Image-based Plant Identification at Global Scale [2.961584451143903]
  The PlantCLEF2023 challenge aims to address a multi-image (and metadata) classification problem involving an extensive set of classes.<n>This paper provides an overview of the challenge's resources and evaluations, summarizes the methods and systems employed by participating research groups, and presents an analysis of key findings.
  arXiv  Detail & Related papers  (2025-09-22T11:34:10Z)
• Overview of PlantCLEF 2024: multi-species plant identification in vegetation plot images [2.7110107174608173]
  The PlantCLEF 2024 challenge leverages a new test set of thousands of multi-label images annotated by experts and covering over 800 species.<n>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.<n>The aim is to predict all the plant species present on a high-resolution plot image.
  arXiv  Detail & Related papers  (2025-09-19T08:51:41Z)
• Multi-Label Plant Species Prediction with Metadata-Enhanced Multi-Head Vision Transformers [0.0]
  We present a multi-head vision transformer approach for multi-label plant species prediction in vegetation plot images.<n>The task involves training models on single-species plant images while testing on multi-species quadrat images, creating a drastic domain shift.<n>Our methodology leverages a pre-trained DINOv2 Vision Transformer Base (ViT-B/14) backbone with multiple classification heads for species, genus, and family prediction.
  arXiv  Detail & Related papers  (2025-08-14T08:56:58Z)
• BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learning [60.80381372245902]
  We find emergent behaviors in biological vision models via large-scale contrastive vision-language training.<n>We train BioCLIP 2 on TreeOfLife-200M to distinguish different species.<n>We identify emergent properties in the learned embedding space of BioCLIP 2.
  arXiv  Detail & Related papers  (2025-05-29T17:48:20Z)
• Species196: A One-Million Semi-supervised Dataset for Fine-grained Species Recognition [30.327642724046903]
  Species196 is a large-scale semi-supervised dataset of 196-category invasive species. It collects over 19K images with expert-level accurate annotations Species196-L, and 1.2M unlabeled images of invasive species Species196-U.
  arXiv  Detail & Related papers  (2023-09-25T14:46:01Z)
• Spatial Implicit Neural Representations for Global-Scale Species Mapping [72.92028508757281]
  Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location. Traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets. We use Spatial Implicit Neural Representations (SINRs) to jointly estimate the geographical range of 47k species simultaneously.
  arXiv  Detail & Related papers  (2023-06-05T03:36:01Z)
• Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping [59.0626764544669]
  In this study, we use Deep Learning methods to semantically segment grapevine leaves images in order to develop an automated object detection system for leaf phenotyping. Our work contributes to plant lifecycle monitoring through which dynamic traits such as growth and development can be captured and quantified.
  arXiv  Detail & Related papers  (2022-10-24T14:37:09Z)
• Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection [60.83360138070649]
  The paper presents an approach for analyzing aerial images of a potato crop using deep neural networks. The main objective is to demonstrate automated spatial recognition of a healthy versus stressed crop at a plant level. Experimental validation demonstrated the ability for distinguishing healthy and stressed plants in field images, achieving an average Dice coefficient of 0.74.
  arXiv  Detail & Related papers  (2021-06-14T21:57:40Z)
• Two-View Fine-grained Classification of Plant Species [66.75915278733197]
  We propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species. A deep metric based on Siamese convolutional neural networks is used to reduce the dependence on a large number of training samples and make the method scalable to new plant species.
  arXiv  Detail & Related papers  (2020-05-18T21:57:47Z)
• Automatic image-based identification and biomass estimation of invertebrates [70.08255822611812]
  Time-consuming sorting and identification of taxa pose strong limitations on how many insect samples can be processed. We propose to replace the standard manual approach of human expert-based sorting and identification with an automatic image-based technology. We use state-of-the-art Resnet-50 and InceptionV3 CNNs for the classification task.
  arXiv  Detail & Related papers  (2020-02-05T21:38:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.