PlantCLEF2026

Identify Multi-species Plants in Images of Vegetation Plots

Schedule

• 17 November 2025: Registration opens for all LifeCLEF challenges (registration is free of charge)
• 5 February 2026: Competition Start
• 23 April 2026: Registration closes for all LifeCLEF challenges
• 7 May 2026: Competition Deadline
• 28 May 2026: Deadline for submission of working note papers by participants [CEUR-WS proceedings]
• 30 June 2026: Notification of acceptance of working note papers [CEUR-WS proceedings]
• 6 July 2026: Camera-ready deadline for working note papers.
• 21-24 Sept 2026: CLEF 2026 Jena - Germany

All deadlines are at 11:59 PM CET on a corresponding day unless otherwise noted. The competition organizers reserve the right to update the contest timeline if they deem it necessary.

Motivation

Vegetation plots enable standardized biodiversity assessment, long-term monitoring, and large-scale ecological surveys, providing key data for ecosystem analysis and conservation planning. These images typically cover 50 × 50 cm plots where botanists identify all species and quantify their abundance through biomass, cover, or related indicators. The integration of AI could improve the efficiency of specialists, helping them to extend the scope and coverage of ecological studies. Given the task’s difficulty and the strong results and participation in the 2025 challenge, the task is organized again in a similar format, using the same datasets and Kaggle platform as a second round.

Task description

The task is evaluated as a multi-label classification task that aims to predict all the plant species on the high-resolution plot images. 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).

Data collection

The test set is 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 is composed more conventionally of observations of individual plants, such as those used in previous editions of PlantCLEF. More precisely, it is a subset of the Pl@ntNet training data focusing on south western Europe and covering 7.8k plant species. It contains about 1.4 million images extended with some images with trusted labels aggregated from the GBIF platform to complete the less illustrated species. A second training dataset comprising hundreds of thousands unlabeled vegetation cover images is available (built from the LUCAS cover images dataset), aiming to facilitate the evaluation of self-supervised approaches.

Rules

Using additional data or metadata is permitted on condition that for each run with external data, you submit an equivalent run with only the data supplied, to enable more accurate comparisons.
The training metadata file also includes a gbif_species_id, which can be used to find additional data on the GBIF platform

Self-supervised, semi-supervised or unsupervised approaches are strongly encouraged. Pre-trained models are provided.

Participation requirements

• 1. Subscribe to CLEF (PlantCLEF task) by filling this form
• 2. Go to the Kaggle's PlantCLEF 2026 challenge page and compete

Publication track

All registered participants are encouraged to submit a working-note paper to peer-reviewed LifeCLEF proceedings (CEUR-WS) after the competition ends.
This paper must provide sufficient information to reproduce the final submitted runs.

Only participants who submitted a working-note paper will be part of the officially published ranking used for scientific communication.

The results of the campaign appear in the working notes proceedings published by CEUR Workshop Proceedings (CEUR-WS.org).
Selected contributions among the participants will be invited for publication in the Springer Lecture Notes in Computer Science (LNCS) the following year.

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).