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

这个项目将向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。据估计，有700万个物种尚未被发现，而每年发现的物种只有7000个，要识别所有物种需要大约850年的时间，目前平均每个物种需要21年的时间。该项目旨在通过“Descriptron”加速这一进程。“Descriptron”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密钥生成。与NASA马歇尔太空飞行中心合作，该项目将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结果的可重复性。其影响跨越了生态学、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。这一努力增进了我们对自家后院生物多样性的了解，加速了对地球上未被发现生命的识别。全节肢动物，包括爪足动物、缓步动物、螯足动物、多足动物和潘足动物，是地球上最大和最多样化的分支，估计有700万种尚未被发现。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，显著加快分类学物种描述和密钥生成。这一努力的关键是与美国宇航局马歇尔太空飞行中心的战略合作，为Descriptron的开发提供先进的成像技术。先进的成像技术将极大地加快新的训练数据的开发，这些数据用于节肢动物形态特征的实例分割和文本描述过程的自动化，减少人为错误，确保高度可重复性和客观的结果。通过创建一个骨骼模型库和描述术语（包括颜色、纹理和形状），Descriptron将自动生成一个骨骼化的物种分类描述过程。该项目利用公民科学，通过iNaturalist平台让更广泛的社区参与数据注释过程。这种方法不仅促进了公众对生物多样性的理解和欣赏，而且为项目提供了必要的数据。使用Descriptron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的影响。通过有效地让公民科学家参与进来，加速分类学发现，这个项目具有巨大的潜力，可以促进我们对地球生物多样性的理解，加快生物发现的进程。该奖项反映了美国国家科学基金会的法定使命，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.