HOW MEGA-DIVERSE ARE ASIAN RAINFORESTS? DEVELOPMENT OF INNOVATIVE AI MODELS TO UNDERSTAND TROPICAL PLANT BIODIVERSITY

联合国可持续发展目标15“陆地上的生命”（联合国，2022年）致力于保护陆地生态系统，阻止生物多样性的丧失。通过分类学出版物记录世界植物多样性是保护植物多样性的关键：不了解存在哪些物种，就不可能保护它们或评估它们的潜在意义（Cheek et al., 2020）。在全球3000多个植物标本馆网络中，数百万个植物标本馆标本是植物多样性的保存记录（Heberling et al., 2019）。正是这些已经在植物标本室的标本，代表了50%的尚未被描述的物种（Bebber et al. 2010）。然而，由于标本数量庞大，分类困难的类群面临挑战，专家数量不断减少，对这些标本的准确鉴定是一个耗时的过程。加快分类学成果的产生速度是采取行动应对对世界栖息地的威胁的关键。通过开发人工智能方法来自动识别标本，该项目旨在加速对Cyrtandra属（非洲紫罗兰科）的分类工作，Cyrtandra属是东南亚热带雨林中常见的一个种类繁多但鲜为人知的群体（Atkins等人，2021）。更具体地说，它将开发一个分层框架，该框架由级联网络组成，以解决不同级别的分类问题，并采用元学习策略来解决只有一个样本可用的极端挑战。级联网络可以利用种、属、科等更高分类学水平的知识来提高识别精度。图1显示了一个要分类的植物标本馆标本的例子。物种水平上的标本数据通常是不平衡的，即一个类标签可能只有一个观察结果，而另一个可能有非常多的观察结果。在这类数据集上直接训练深度学习模型会导致过拟合。为了克服这一挑战，将探索元学习策略（Snell et al. 2017），以提高物种水平识别的准确性。本项目有以下具体目标：1。开发一种层叠式多标签深度体系结构，在进行不同分类单元级别的识别时，可以考虑给定类层次结构的先验知识和标本馆的关键信息。探索有效的元学习和微调方法，以提高分类不平衡数据集的识别性能3。建立一个易于使用的软件系统，将植物标本室图像分类为具有置信度值的不同标签，加快新物种的发现。

The United Nations Sustainable Development Goal 15 'Life on Land' (UN, 2022) strives to protect terrestrial ecosystems and halt biodiversity loss. Documenting the world's plant diversity through taxonomic publications is key to its conservation: without understanding which species are present, it is not possible to protect them or to evaluate their potential significance (Cheek et al., 2020). In the worldwide network of over 3000 herbaria, millions of herbarium specimens are preserved records of plant diversity (Heberling et al., 2019). It is these specimens, already in herbaria, that represent >50% of yet-to-be described species (Bebber et al. 2010). However, the accurate identification of these specimens is a time-consuming process, due to the large volume of specimens, challenges in taxonomically difficult groups and a decreasing number of experts. Increasing the speed that taxonomic outputs are produced is key to acting against the threats to the world's habitats.By developing artificial intelligence methods to automatically identify specimens, this project aims to accelerate taxonomic efforts in the genus Cyrtandra (African violet family), a mega-diverse, poorly-known group, common in Southeast Asian rain forests (Atkins et al., 2021). More specifically, it will develop a hierarchical framework that consists of a cascade network to address classification at different levels and a meta-learning strategy to solve extreme challenges where only one sample is available. The cascade networks can make use of the knowledge of species, genus, family and other higher taxonomic levels to improve identification accuracy. Figure 1 shows an example of a herbarium specimen to be classified. Specimen data at the species level is often imbalanced, i.e one class label might just have one observation and the other might have a very high number of observations. Directly training deep learning models on such kinds of datasets will result in overfitting. To overcome this challenge, the meta-learning strategy (Snell et al. 2017) will be explored to improve the accuracy of species-level recognition.This project has the following specific objectives:1. Develop a cascade multi-label deep architecture which can take the prior knowledge of a given class hierarchy and key information of herbaria into account when performing identification at different taxon levels.2. Explore effective meta-learning and fine-tuning methods to improve the identification performance on taxonomically unbalanced datasets3. Build an easy-to-use software system to classify herbarium images into different labels with confidence values and speed up the discovery of new species.