PlantCLEF 2024

multi-species plant identification in vegetation plot images

Motivation

Vegetation plot inventories are essential for ecological studies, enabling standardized sampling, biodiversity assessment, long-term monitoring and remote, large-scale surveys. They provide valuable data on ecosystems, biodiversity conservation, and evidence-based environmental decision-making. Plot images are typically one square meter in size, and botanists meticulously identify all the species found there. In addition, they quantify species abundance using indicators such as biomass, qualification factors, and areas occupied in photographs. The integration of AI could significantly improve specialists' efficiency, helping them extend the scope and coverage of ecological studies.

Data collection

The test set will be a compilation of several image datasets of plots in different floristic contexts, such as Pyrenean and Mediterranean floras, all produced by experts. The training set will be composed more conventionally of observations of individual plants, such as those used in previous editions of PlantCLEF. More precisely, it will be a subset of the PlantCLEF2023 data focused on Europe and covering 15k plant species. It will contain about 1 million images with trusted labels (aggregated from the GBIF platform) and as many images with potentially noisy labels aggregated through web scraping (based on Google and Bing search engines).

Task description

The main difficulty of the task lies in the shift between the test data (high-resolution multi-label images of vegetation plots) and the training data (single-label images of individual plants). The task will be evaluated as a multi-label classification task that aims to predict all the plant species on the high-resolution plot images. The participants will first have access to the training set, and a few months later, they will be provided with the whole test set. Self-supervised, semi-supervised or unsupervised approaches will be strongly encouraged, and a starter package with pre-trained models will be provided.

How to participate ?

Coming soon!

CEUR Working Notes

For detailed instructions, please refer to https://clef2024.clef-initiative.eu/index.php?page=Pages/publications.html.

A summary of the most important points:

• All participating teams with at least one graded submission, regardless of the score, should submit a CEUR working notes paper.
• Submission of reports is done through EasyChair – please make absolutely sure that the author (names and order), title, and affiliation information you provide in EasyChair match the submitted PDF exactly
• Strict deadline for Working Notes Papers: 31 May 2024
• Strict deadline for CEUR-WS Camera Ready Working Notes Papers: 8 July 2024
• Templates are available here
• Working Notes Papers should cite both the LifeCLEF 2024 overview paper as well as the PlantCLEF task overview paper, citation information will be added in the Citations section below as soon as the titles have been finalized.

Schedule

• Jan 2024: registration opens for all LifeCLEF challenges
• Jan-March 2024: training and test data release
• 6 May 2024: deadline for submission of runs by participants
• 13 May 2024: release of processed results by the task organizers
• 31 May 2024: deadline for submission of working note papers by participants [CEUR-WS proceedings]
• 24 June 2024: notification of acceptance of participant's working note papers [CEUR-WS proceedings]
• 8 July 2024: camera ready copy of participant's working note papers and extended lab overviews by organizers
• 9-12 Sept 2024: CLEF 2024 Grenoble - France

Credit

This project has received funding from the European Union’s Horizon research and innovation program under grant agreement No 101060639 (MAMBO project) and No 101060693 (GUARDEN project).