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

arXiv: https://arxiv.org/abs/2509.15768 · HTML: https://mlsanity.com/paper/2509.15768/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 19 Sep 2025.

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

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Data Augmentation using Random Image Cropping and Patching for Deep CNNs](https://mlsanity.com/paper/1811.09030/) (2018)
- [Multi-Label Plant Species Classification with Self-Supervised Vision Transformers](https://mlsanity.com/paper/2407.06298/) (2024)

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

- [Overview of PlantCLEF 2025: Multi-Species Plant Identification in Vegetation Quadrat Images](https://mlsanity.com/paper/2509.17602/) (2025)
- [Energy-Efficient Plant Monitoring via Knowledge Distillation](https://mlsanity.com/paper/2604.27178/) (2026)
- [Zero-Shot Segmentation through Prototype-Guidance for Multi-Label Plant Species Identification](https://mlsanity.com/paper/2512.19957/) (2025)
- [Self-Supervised Learning of Plant Image Representations](https://mlsanity.com/paper/2604.27538/) (2026)
- [PHOEBI: An Open-World Benchmark for Multi-Label Bacterial Identification in Phase-Contrast Microscopy](https://mlsanity.com/paper/2606.22890/) (2026)
- [Multi-Scale ViT Inference with Habitat-Fit Priors and kNN Retrieval for Multi-Species Plant Identification](https://mlsanity.com/paper/2607.14509/) (2026)

## Abstract (arXiv)

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.