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

亚洲雨林的多样性如何？

• 批准号: 2887670
• 金额: --
• 依托单位: Loughborough University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2887670
• 关键词: HOW; MEGA; DIVERSE; ASIAN; RAINFORESTS

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.

联合国可持续发展目标15“土地上的生活”（联合国，2022年）努力保护陆地生态系统和停止生物多样性的损失。通过分类学出版物记录世界的植物多样性是其保护的关键：在不了解存在哪种物种的情况下，不可能保护它们或评估其潜在意义（Cheek等，2020）。在全球3000多种植物园的网络中，数百万植物标本的标本是植物多样性的保存记录（Heberling等，2019）。正是这些标本已经在草药中，代表了50％的尚未描述的物种（Bebber等人，2010年）。但是，这些标本的准确识别是一个耗时的过程，由于标本的大量，分类学困难组的挑战以及专家数量减少。提高分类学产出的速度是对抗世界栖息地的威胁的关键。通过开发人工智能方法以自动识别样本，该项目旨在加快Cyrtra属（非洲紫罗兰色家族）的分类学努力，是Mega多样性的，一个众所周知的小组，在Aseast Asian雨林中常见的小组，在Kinds siian雨林中（Ands An）（Ands An）（Ands）。更具体地说，它将开发一个层次结构框架，该框架由级联网络组成，该级联网络以不同级别的分类和元学习策略来解决分类，以解决仅有一个样本的极端挑战。级联网络可以利用物种，属，家庭和其他较高分类学水平的知识来提高识别精度。图1显示了要分类的标本室标本的示例。物种水平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，另一个标签可能具有很高的观察结果。直接培训有关此类数据集的深度学习模型将导致过度拟合。为了克服这一挑战，将探索元学习策略（Snell等人，2017年），以提高物种水平识别的准确性。该项目具有以下特定目标：1。开发级联多标签深度体系结构，该体系结构可以在不同分类单元级别执行识别时考虑给定类层次结构的先验知识和草药的关键信息。2。探索有效的元学习和微调方法，以提高分类学不平衡数据集的识别性能3。构建一个易于使用的软件系统，将植物图像分类为具有置信价值的不同标签，并加快发现新物种的发现。