Overview of PlantCLEF 2024: multi-species plant identification in vegetation plot images

• URL: http://arxiv.org/abs/2509.15768v1
• Date: Fri, 19 Sep 2025 08:51:41 GMT
• Title: Overview of PlantCLEF 2024: multi-species plant identification in vegetation plot images
• Authors: Herve Goeau, Vincent Espitalier, Pierre Bonnet, Alexis Joly,
• Abstract summary: 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.
• Score: 2.7110107174608173
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Plot images are essential for ecological studies, enabling standardized sampling, biodiversity assessment, long-term 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.

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 2025: Multi-Species Plant Identification in Vegetation Quadrat Images [2.526933812879881]
  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.
  arXiv  Detail & Related papers  (2025-09-22T11:21:53Z)
• ViewSparsifier: Killing Redundancy in Multi-View Plant Phenotyping [8.348234911002821]
  Plant phenotyping involves analyzing observable characteristics of plants to better understand their growth, health, and development.<n>In the context of deep learning, this analysis is often approached through single-view classification or regression models.<n>To address this, the Growth Modelling (GroMo) Grand Challenge at ACM Multimedia 2025 provides a multi-view dataset featuring multiple plants.
  arXiv  Detail & Related papers  (2025-09-10T12:53:38Z)
• 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)
• A multi-modal dataset for insect biodiversity with imagery and DNA at the trap and individual level [12.817729932901779]
  We present the Mixed Arthropod Sample and Identification (MassID45) dataset for training automatic classifiers of bulk insect samples.<n>It uniquely combines molecular and imaging data at both the unsorted sample level and the full set of individual specimens.<n>Human annotators, supported by an AI-assisted tool, performed two tasks on bulk images: creating segmentation masks around each individual arthropod and assigning taxonomic labels to over 17 000 specimens.
  arXiv  Detail & Related papers  (2025-07-09T16:03:06Z)
• Perceptual Artifacts Localization for Image Synthesis Tasks [59.638307505334076]
  We introduce a novel dataset comprising 10,168 generated images, each annotated with per-pixel perceptual artifact labels. A segmentation model, trained on our proposed dataset, effectively localizes artifacts across a range of tasks. We propose an innovative zoom-in inpainting pipeline that seamlessly rectifies perceptual artifacts in the generated images.
  arXiv  Detail & Related papers  (2023-10-09T10:22:08Z)
• Improving Data Efficiency for Plant Cover Prediction with Label Interpolation and Monte-Carlo Cropping [7.993547048820065]
  The plant community composition is an essential indicator of environmental changes and is usually analyzed in ecological field studies. We introduce an approach to interpolate the sparse labels in the collected vegetation plot time series down to the intermediate dense and unlabeled images. We also introduce a new method we call Monte-Carlo Cropping to deal with high-resolution images efficiently.
  arXiv  Detail & Related papers  (2023-07-17T15:17:39Z)
• PlantDet: A benchmark for Plant Detection in the Three-Rivers-Source Region [4.676030127116814]
  We construct a dataset for Plant detection in the Three-River-Source region (PTRS) It comprises 21 types, 6965 high-resolution images of 2160*3840 pixels, captured by diverse sensors and platforms, and featuring objects of varying shapes and sizes. The PTRS presents us with challenges such as dense occlusion, varying leaf resolutions, and high feature similarity among plants, prompting us to develop a novel object detection network named PlantDet.
  arXiv  Detail & Related papers  (2023-04-11T04:18:56Z)
• 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)
• Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples [52.549928980694695]
  In situ hybridization (ISH) gene expression pattern images from the same developmental stage are compared. labeling training data with precise stages is very time-consuming even for biologists. We propose a deep two-step low-shot learning framework to accurately classify ISH images using limited training images.
  arXiv  Detail & Related papers  (2020-10-20T06:06:06Z)
• 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.