ODOMATIC: Automatic Species Identification, Functional Morphology, and Feature Extraction to alleviate the taxonomic impediment and broaden citizen science tools.

生物体的命名和分类是生物学建立的基本原则。事实上，我们生活的几乎每一个方面--从我们吃的食物到我们服用的药物，从我们住的房子到我们欣赏的自然风景--都与地球上的生物多样性有关。所有物种都必须被命名和编目，以便人类了解我们周围的世界。负责识别和描述物种的分类学科学领域，在近几十年来已经减少了其专业劳动力;这是不幸的，因为人类活动导致生命多样性前所未有的下降，对分类学的需求从未如此之大。该项目将通过引入一套基于网络的开源工具，用于从图像中识别物种和测量图像标本进行比较研究，从而有助于减轻当前和未来分类学家在识别和编目生物多样性方面的负担。这些软件工具将通过公共资助的CyVerse网络平台免费提供。此外，一个名为ODOMATIC的识别蜻蜓和豆娘的系统将在现有的网络平台OdonataCentral上实现，使研究人员和爱好者能够从翅膀的图像中准确识别这些昆虫。将在新泽西和亚拉巴马的不同城市社区提供一系列免费讲习班，以增加奥多纳塔中心的多样性？的用户群，鼓励参与STEM领域，并普遍增加对自然世界的欣赏和理解。一系列的Google Hangout活动将鼓励拉丁美洲、非洲和亚洲的世界蜻蜓协会成员使用ODOMATIC。此外，来自罗格斯大学纽瓦克分校的本科生将被招募来帮助设计和培训ODOMATIC，为他们提供研究，编程和分类学方面的宝贵经验。这项工作将有助于减少分类障碍-迫切需要更多的分类产品，如物种描述和标本鉴定，由更少的分类学家工作人员完成-解决耗时但必要的任务，即物种鉴定。该项目的目标包括发布一个新开发的系统，用于从蜻蜓（蜻蜓和豆娘）的翅膀图像中自动识别蜻蜓，该系统使用计算机视觉和机器学习来描述和分类物种。 将通过OdonataCentral网站（odonatacentral.org）部署一个界面，使用该系统进行物种识别。 此外，将开发独立工具，用于自动描述和放置生物图像中标本的几何形态测量标志，用于基于形态学的比较研究。 这些目标的成功完成将有利于牙科社区提供牙齿和豆娘识别。 更广泛地说，这里创建的工具将允许研究其他生物群体的生物学家从他们的标本图像中快速提取形态数据。

The naming and classifying of living organisms is a fundamental principal upon which biology is founded. Indeed, almost every facet of our lives - from the food we eat to the medicine we take, the houses we live in to the natural scenery we admire - has a connection to the biodiversity present on Earth. All species must be named and cataloged in order for humans to understand the world around us. The scientific field of taxonomy, responsible for identifying and describing species, has incurred a reduction in its specialist workforce in recent decades; this is unfortunate because the need for taxonomy has never been greater as human activities result in unprecedented decline in the diversity of life. This project will help to reduce the burden placed on current and future taxonomists to identify and catalog biodiversity by introducing a set of open-source, web-based tools for identifying species from images and for measuring imaged specimens for comparative studies. These software tools will be made freely available through the publicly-funded CyVerse web platform. In addition, a system for identifying dragonflies and damselflies called ODOMATIC will be implemented on an existing web presence, OdonataCentral, allowing researchers and enthusiasts to accurately identify these insects from images of their wings. A series of free workshops will be offered in diverse urban communities in New Jersey and Alabama to increase the diversity of OdonataCentral?s user base, encourage participation in the STEM fields, and generally increase appreciation and understanding of the natural world. A series of Google Hangout events will encourage international user-ship for ODOMATIC from World Dragonfly Association members in Latin America, Africa, and Asia. In addition, undergraduate students from Rutgers University-Newark will be recruited to help in designing and training ODOMATIC, giving them valuable experience in research, programming and taxonomy. This work will help to reduce the taxonomic impediment - an urgent need for more taxonomic products, like species descriptions and specimen identifications, from fewer taxonomist workers - by addressing the time-consuming, but necessary, task that is species identification. Objectives of this project include release of a newly-developed system for automatically identifying Odonata (dragonflies and damselflies) from images of their wings, which uses computer vision and machine learning to characterize and classify species. An interface will be deployed making species identification using this system accessible through the OdonataCentral website (odonatacentral.org). In addition, stand-alone tools will be developed for automatically describing and placing geometric morphometric landmarks on specimens in biological imagery for use in morphology-based comparative studies. The successful completion of these objectives will benefit the odonatological community by providing dragonfly and damselfly identification. More broadly, the tools created here will allow biologists studying other groups of organisms to rapidly extract morphological data from images of their specimens.