RII Track-4:@NASA: Automating Character Extraction for Taxonomic Species Descriptions Using Neural Networks, Transformer, and Computer Vision Signal Processing Architectures

该项目将为波多黎各马亚圭斯大学的一名副教授提供研究金，并为一名研究生提供培训。 节肢动物，包括昆虫，蜘蛛和千足虫，是地球上最多样化的门，已知物种超过101万种。估计有700万个物种尚未被发现，而每年的发现率仅为7,000个物种，需要大约850年才能识别所有物种，目前每个物种平均需要21年。该项目旨在通过“Descriptron”加速这一过程，这是一种开创性的人工智能工具，利用机器学习和计算机视觉来加速物种描述和分类关键字生成。 该项目将与美国宇航局马歇尔太空飞行中心合作，利用先进的成像技术自动捕获和描述节肢动物形态特征，减少人为错误并确保可重复的结果。其影响横跨生态学、进化生物学和发育生物学。 该项目强调公民科学的作用，通过iNaturalist让更广泛的社区参与数据注释，促进公众参与科学发现。全节肢动物门（Panarthropoda）是地球上最大、最具多样性的分支，约有700万个物种尚未被发现。全节肢动物门（Panarthropoda）包括爪亚目（Onychophora）、缓步动物门（Tardigrada）、螯肢动物门（Chelicerata）、多足动物门（Myriapoda）和全甲壳动物门（Pancrustacea）。通过人工智能（AI）管道“Descriptron”，该项目将通过利用最先进的变压器，卷积神经网络和计算机视觉技术，显着加快分类物种描述和密钥生成。这一奋进的关键是与美国宇航局的马歇尔太空飞行中心的战略合作，为Descriptron的发展提供先进的成像技术。先进的成像技术将大大加快节肢动物形态特征的实例分割和文本描述过程自动化所需的新型训练数据的开发，减少人为错误并确保高度可重复的客观结果。通过创建一个用于骨片和描述性术语（包括颜色、纹理和形状）的模型库，Descriptron将自动化产生分类学物种描述的过程。 该项目通过iNaturalist平台让更广泛的社区参与数据注释过程，从而利用公民科学。这种方法不仅有助于公众了解和欣赏生物多样性，而且还为项目提供了必要的数据。描述子的使用有望在生态学、进化生物学和发育生物学等依赖于准确形态学数据的各个领域产生广泛影响。通过有效地让公民科学家参与并加速分类学发现，该项目具有极大的潜力，可以促进我们对地球生物多样性的理解，加快生物发现进程。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project would provide a fellowship to an Associate professor and training for a graduate student at the University of Puerto Rico Mayaguez. Arthropoda, a group that includes insects, spiders, and millipedes, is Earth's most diverse phylum with over 1.01 million known species. With an estimated 7 million species yet to be discovered, and a discovery rate of just 7,000 species per year, it would take around 850 years to identify them all, a process currently taking an average of 21 years per species. This project aims to expedite this through "Descriptron", a groundbreaking artificial intelligence tool leveraging machine learning and computer vision to accelerate species descriptions and taxonomic key generation. In collaboration with NASA Marshall Space Flight Center, the project will utilize advanced imaging technology to automate the capture and description of arthropod morphological features, reducing human error and ensuring reproducible results. The implications extend across ecology, evolutionary biology, and developmental biology. Emphasizing the role of citizen science, the project involves the wider community in data annotation via iNaturalist, fostering public participation in scientific discovery. This endeavor advances our understanding of biodiversity in our own backyards and accelerates the identification of undiscovered life on Earth.Panarthropoda, encompassing Onychophora, Tardigrada, Chelicerata, Myriapoda, and Pancrustacea, is Earth's largest and most diverse clade, with an estimated 7 million species yet to be discovered. Through "Descriptron", an artificial intelligence (AI) pipeline, this project will significantly accelerate taxonomic species descriptions and key generation through the utilization of state-of-the-art transformers, convolutional neural networks, and computer vision techniques. Key to this endeavor is a strategic collaboration with NASA's Marshall Space Flight Center, providing advanced imaging technology for Descriptron's development. Advanced imaging techniques will greatly speed up the development of novel training data needed for the automation of instance segmentation and text description process of arthropod morphological features, reducing human error and ensuring highly reproducible, objective results. By creating a library of models for sclerites and descriptive terms including color, texture, and shape, Descriptron will automate the process of producing a skeletonized taxonomic species description. This project leverages citizen science by engaging the broader community in the data annotation process via the iNaturalist platform. This approach not only facilitates public understanding and appreciation of biodiversity but also contributes essential data to the project. The use of Descriptron promises wide-reaching impacts across various fields such as ecology, evolutionary biology, and developmental biology that depend upon accurate morphological data. By effectively involving citizen scientists and accelerating taxonomic discovery, this project holds substantial potential to advance our understanding of Earth's biodiversity and expedite the biodiscovery process.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.