用AI识别植被样方图像中的多物种植物，提升生态研究效率
Overview of PlantCLEF 2024: multi-species plant identification in vegetation plot images
- 基于170万张植物图像训练视觉变压器模型
- 测试集覆盖800多种植物，支持弱标签多标签分类
- 适合生态监测与自动化植物识别研究者使用
样方图像对生态学研究至关重要，可实现标准化采样、生物多样性评估、长期监测及远程大范围调查。典型样方图像尺寸为50厘米或1平方米，需植物学家逐个识别其中所有物种。人工智能的引入有望显著提升专家工作效率，拓展研究覆盖范围。为评估该方向进展，PlantCLEF 2024挑战赛提供了一个包含数千张多标签图像的新测试集，由专家标注，涵盖超过800种植物。同时，还提供了170万张个体植物图像的训练集，以及在该数据上预训练的先进视觉变压器模型。任务设定为弱标签多标签分类，目标是从高分辨率样方图像中预测所有存在的植物物种（使用单标签训练数据）。本文详细描述了数据集、评估方法、参赛者采用的方法与模型，以及取得的结果。
原文摘要 · Abstract (English)
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.
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