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

RII Track-4：@NASA：使用神经网络、变压器和计算机视觉信号处理架构自动提取分类物种描述的字符

• 批准号: 2327168
• 负责人: Alex Van Dam
• 金额: $ 18.81万
• 依托单位: University of Puerto Rico Mayaguez
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-15 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327168&HistoricalAwards=false
• 关键词: RII; Track; NASA; Automating; Character

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.

该项目将为波多黎各马亚圭斯大学的一名副教授提供奖学金，并为一名研究生提供培训。 节肢动物门包括昆虫、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，而每年的发现速度仅为 7,000 个物种，因此需要大约 850 年的时间来识别所有物种，目前每个物种平均需要 21 年的时间。该项目旨在通过“Descriptron”来加快这一进程，“Descriptron”是一种突破性的人工智能工具，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。 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捉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。其影响涵盖生态学、进化生物学和发育生物学。 该项目强调公民科学的作用，让更广泛的社区通过 iNaturalist 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，并加速了对地球上未被发现的生命的识别。全节肢动物门包括有甲门、缓步动物门、螯肢动物门、多足动物门和全甲壳动物门，是地球上最大、最多样化的分支，估计有 700 万种物种被发现。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，显着加速物种分类描述和密钥生成。这一努力的关键是与 NASA 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。先进的成像技术将大大加快节肢动物形态特征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 将自动化生成骨架分类物种描述的过程。 该项目通过 iNaturist 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。这种方法不仅促进了公众对生物多样性的理解和欣赏，而且还为该项目提供了重要数据。 Descriptron 的使用有望对生态学、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，该项目具有促进我们对地球生物多样性的理解并加快生物发现进程的巨大潜力。该奖项反映了 NSF 的法定使命，并通过利用基金会的智力优势和更广泛的评估进行评估，被认为值得支持。影响审查标准。