8万种植物图像识别挑战，推动全球植物多样性自动化鉴定
Overview of PlantCLEF 2022: Image-based plant identification at global scale
- 构建多图像多模态数据集，支持8万级物种分类
- 参赛系统在复杂数据下实现高精度植物识别
- 适合生态、农业与生物多样性研究者参考
全球维管植物种类超过30万，其认知对农业、医药等人类文明发展至关重要，尤其在生物多样性危机背景下。传统人工鉴定负担过重，制约新知识积累。近年来自动识别技术取得显著进展，深度学习已具备应对全球植物多样性识别的潜力，尽管面临类别极多、分布极不均衡、标注错误、图像质量参差、内容多样（如照片与腊叶标本）等挑战。PlantCLEF2022挑战赛聚焦这一现实问题，提出包含8万类植物的多图像与元数据分类任务。本文介绍挑战赛资源与评估体系，总结参赛团队的方法与系统，并分析关键发现。
原文摘要 · Abstract (English)
It is estimated that there are more than 300,000 species of vascular plants in the world. Increasing our knowledge of these species is of paramount importance for the development of human civilization (agriculture, construction, pharmacopoeia, etc.), especially in the context of the biodiversity crisis. However, the burden of systematic plant identification by human experts strongly penalizes the aggregation of new data and knowledge. Since then, automatic identification has made considerable progress in recent years as highlighted during all previous editions of PlantCLEF. Deep learning techniques now seem mature enough to address the ultimate but realistic problem of global identification of plant biodiversity in spite of many problems that the data may present (a huge number of classes, very strongly unbalanced classes, partially erroneous identifications, duplications, variable visual quality, diversity of visual contents such as photos or herbarium sheets, etc). The PlantCLEF2022 challenge edition proposes to take a step in this direction by tackling a multi-image (and metadata) classification problem with a very large number of classes (80k plant species). This paper presents the resources and evaluations of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of key findings.
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