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

arXiv: https://arxiv.org/abs/2509.17602 · HTML: https://mlsanity.com/paper/2509.17602/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 22 Sep 2025.

## Builds on (most cited first, 4 of 4)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization](https://mlsanity.com/paper/2012.04456/) (2020)
- [Overview of PlantCLEF 2024: multi-species plant identification in vegetation plot images](https://mlsanity.com/paper/2509.15768/) (2025)
- [Multi-Label Plant Species Prediction with Metadata-Enhanced Multi-Head Vision Transformers](https://mlsanity.com/paper/2508.10457/) (2025)

## Built on by (most cited first, 6 of 6)

- [LeafNet: A Large-Scale Dataset and Comprehensive Benchmark for Foundational Vision-Language Understanding of Plant Diseases](https://mlsanity.com/paper/2602.13662/) (2026)
- [Plant Taxonomy Meets Plant Counting: A Fine-Grained, Taxonomic Dataset for Counting Hundreds of Plant Species](https://mlsanity.com/paper/2603.21229/) (2026)
- [Training-Free Fine-Grained Semantic Segmentations in Low Data Regimes: A FungiTastic Baseline](https://mlsanity.com/paper/2605.22492/) (2026)
- [Zero-Shot Segmentation through Prototype-Guidance for Multi-Label Plant Species Identification](https://mlsanity.com/paper/2512.19957/) (2025)
- [Multi-Scale ViT Inference with Habitat-Fit Priors and kNN Retrieval for Multi-Species Plant Identification](https://mlsanity.com/paper/2607.14509/) (2026)
- [AgriField-40K: Adapting Vision Models to Agriculture With Efficient Continual Pretraining](https://mlsanity.com/paper/2608.07984/) (2026)

## Abstract (arXiv)

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.