LifeCLEF Plant Identification Task 2014

• URL: http://arxiv.org/abs/2509.23900v1
• Date: Sun, 28 Sep 2025 14:16:15 GMT
• Title: LifeCLEF Plant Identification Task 2014
• Authors: Herve Goeau, Alexis Joly, Pierre Bonnet, Souheil Selmi, Jean-Francois Molino, Daniel Barthelemy, Nozha Boujemaa,
• Abstract summary: The LifeCLEFs plant identification task provides a testbed for a system-oriented evaluation of plant identification about 500 species trees and herbaceous plants.<n>The main originality of this data is that it was specifically built through a citizen sciences initiative conducted by Tela Botanica, a French social network of amateur and expert botanists.<n>This overview presents more precisely the resources and assessments of task, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main eval- uation results.
• Score: 2.4049084513913983
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The LifeCLEFs plant identification task provides a testbed for a system-oriented evaluation of plant identification about 500 species trees and herbaceous plants. Seven types of image content are considered: scan and scan-like pictures of leaf, and 6 kinds of detailed views with un- constrained conditions, directly photographed on the plant: flower, fruit, stem & bark, branch, leaf and entire view. The main originality of this data is that it was specifically built through a citizen sciences initiative conducted by Tela Botanica, a French social network of amateur and expert botanists. This makes the task closer to the conditions of a real- world application. This overview presents more precisely the resources and assessments of task, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main eval- uation results. With a total of ten groups from six countries and with a total of twenty seven submitted runs, involving distinct and original methods, this fourth year task confirms Image & Multimedia Retrieval community interest for biodiversity and botany, and highlights further challenging studies in plant identification.

Related papers

• Towards Ancient Plant Seed Classification: A Benchmark Dataset and Baseline Model [62.98256440452042]
  We construct the first Ancient Plant Seed Image Classification dataset.<n>It contains 8,340 images from 17 genus- or species-level seed categories excavated from 18 archaeological sites across China.<n>In both quantitative and qualitative analyses, our approach surpasses existing state-of-the-art image classification methods, achieving an accuracy of 90.5%.
  arXiv  Detail & Related papers  (2025-12-20T07:18:22Z)
• Plant identification based on noisy web data: the amazing performance of deep learning (LifeCLEF 2017) [2.961584451143903]
  The LifeCLEF plant identification challenge is an important milestone towards automated plant identification systems.<n>The LifeCLEF 2017 challenge aimed at evaluating to what extent a large noisy training dataset collected through the web and containing a lot of labelling errors can compete with a smaller but trusted training dataset checked by experts.<n>This paper presents more precisely the resources and assessments of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes.
  arXiv  Detail & Related papers  (2025-09-25T07:47:43Z)
• 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 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)
• PlantCamo: Plant Camouflage Detection [60.685139083469956]
  This paper introduces a new challenging problem of Plant Camouflage Detection (PCD) To address this problem, we introduce the PlantCamo dataset, which comprises 1,250 images with camouflaged plants. We conduct a large-scale benchmark study using 20+ cutting-edge COD models on the proposed dataset. Our PCNet surpasses performance thanks to its multi-scale global feature enhancement and refinement.
  arXiv  Detail & Related papers  (2024-10-23T06:51:59Z)
• Generating Diverse Agricultural Data for Vision-Based Farming Applications [74.79409721178489]
  This model is capable of simulating distinct growth stages of plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. Our dataset includes 12,000 images with semantic labels, offering a comprehensive resource for computer vision tasks in precision agriculture.
  arXiv  Detail & Related papers  (2024-03-27T08:42:47Z)
• 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)

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.