聚焦热带数据匮乏地区植物识别，评估深度学习在稀有物种上的表现
Overview of LifeCLEF Plant Identification task 2019: diving into data deficient tropical countries
- 基于1万种植物的热带雨林数据集，评估模型在数据稀缺区域的表现
- 顶尖系统识别准确率接近专业专家水平，但稀有物种仍难识别
- 适合关注生物多样性保护与小样本植物识别的研究者
得益于深度学习进步和训练数据的丰富，植物自动识别能力显著提升。然而，现有数据仅覆盖约几万种植物，远低于地球近36.9万种的总数。LifeCLEF 2019植物识别挑战赛（PlantCLEF 2019）旨在评估算法在数据匮乏地区的植物识别性能。该挑战基于包含1万种植物的数据集，主要聚焦圭亚那盾地和北亚马孙雨林，这是全球动植物多样性最丰富的区域之一。与往年一样，对参赛系统的表现与顶级热带植物学家进行了对比。本文介绍了挑战赛资源与评估结果，总结了各参赛团队采用的方法与系统，并分析了主要发现。
原文摘要 · Abstract (English)
Automated identification of plants has improved considerably thanks to the recent progress in deep learning and the availability of training data. However, this profusion of data only concerns a few tens of thousands of species, while the planet has nearly 369K. The LifeCLEF 2019 Plant Identification challenge (or "PlantCLEF 2019") was designed to evaluate automated identification on the flora of data deficient regions. It is based on a dataset of 10K species mainly focused on the Guiana shield and the Northern Amazon rainforest, an area known to have one of the greatest diversity of plants and animals in the world. As in the previous edition, a comparison of the performance of the systems evaluated with the best tropical flora experts was carried out. This paper presents the resources and assessments of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes.
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