用AI识别植被样方图像中的多物种，助力生态监测提速
Overview of PlantCLEF 2025: Multi-Species Plant Identification in Vegetation Quadrat Images
- 基于140万张植物图像预训练视觉变压器模型
- 挑战赛涵盖2105张高分辨率图像与约400个物种
- 适合生态学家和计算机视觉研究者参与
样方图像在生态研究中至关重要，支持标准化采样、植物多样性评估、长期监测及大规模野外调查。这些图像通常覆盖50厘米或1平方米区域，需植物学家逐一鉴定其中所有物种。引入AI可加速物种清单编制并扩大研究范围。为评估该领域进展，PlantCLEF 2025挑战赛采用新测试集：2,105张高分辨率多标签图像，由专家标注，涵盖约400个物种；同时提供包含140万张个体植物图像的训练集，以及基于该数据预训练的视觉变换器（Vision Transformer）模型。任务设定为弱标签多标签分类，目标是利用单标签训练数据预测样方图像中所有存在的物种。本文详述数据构建、评估方法、参赛者所用方法与模型及其成果。
原文摘要 · Abstract (English)
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.
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