CISE-MSI: RCBP-ED: CNS: Data Science and Engineering for Agriculture Automation

CISE-MSI：RCBP-ED：CNS：农业自动化数据科学与工程

• 批准号: 2131269
• 负责人: Junwhan Kim
• 金额: $ 29.99万
• 依托单位: University of the District of Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2131269&HistoricalAwards=false
• 关键词: CISE; MSI; RCBP; ED; CNS

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Data science is an interdisciplinary field in which statistical, computer science, and mathematical concepts overlap for the acquisition, analysis, and extraction of valuable insights from large datasets. Similarly, data engineering promotes the flow and accession of data through the developing and application of software tools, interfaces and mechanisms. Together, tools from these fields are often applied to other domains (e.g., medicine, agriculture, urban planning) to generate new understanding from data. But insights gained through data science and engineering tools are far superior when applied in a transdisciplinary and collaborative approach that includes expertise in both the data tools and the fields to which they are being applied. To develop better insights into improving the state of nutrition-associated disorders in urban “food deserts,” the Department of Computer Science and Information Technology at the University of the District of Columbia will expand its capacity to train Accelerated Bachelor’s/Master’s students using an integrated training model in data science and engineering (IMDSE). This new program will build research capacity through transdisciplinary education program that systematically integrates data science, data engineering, and agriculture automation.The IMDSE program will catalyze scientific discovery and innovation by training students in data science and engineering and urban aquaponics automation. Aquaponics automation focuses on creating sustainable food production in urban areas and requires knowledge and application of deep machine learning, cloud computing, data science and engineering technologies, internet-of-things technologies, sensor and actuator technologies, wireless communications, and web programming, as well as agriculture, agronomy, nutrition, biology, public health, and environmental sciences. This IMDSE program will be a first and original foundation for training undergraduate and graduate students with both experiential and transdisciplinary expertise in targeted interdisciplinary and convergent research areas. While the initial target is on agriculture automation, other research areas requiring insights from big data science and engineering will be included as the program grows into a national-scale program.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.

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。数据科学是一个交叉学科领域，在从大数据集中获取、分析和提取有价值的见解方面，统计学、计算机科学和数学概念重叠。同样，数据工程通过开发和应用软件工具、接口和机制来促进数据的流动和获取。这些领域的工具通常一起应用于其他领域(例如，医学、农业、城市规划)，以从数据中产生新的理解。但是，通过数据科学和工程工具获得的洞察力在应用于跨学科和协作方法时要优越得多，这种方法包括数据工具和它们所应用的领域的专业知识。为了更好地了解如何改善城市“食物沙漠”中营养相关疾病的状况，哥伦比亚特区大学计算机科学与信息技术系将扩大其能力，使用数据科学与工程(IMDSE)的综合培训模式来培训加速的学士/硕士学生。这一新计划将通过系统整合数据科学、数据工程和农业自动化的跨学科教育计划来建设研究能力。IMDSE计划将通过培训学生在数据科学与工程以及城市水培自动化方面促进科学发现和创新。水培自动化侧重于在城市地区创造可持续的粮食生产，需要深度机器学习、云计算、数据科学和工程技术、物联网技术、传感器和执行器技术、无线通信和网络编程以及农业、农学、营养、生物学、公共卫生和环境科学的知识和应用。这一IMDSE项目将是培养在目标跨学科和融合研究领域具有经验和跨学科专业知识的本科生和研究生的第一个和独创的基础。虽然最初的目标是农业自动化，但随着该计划成长为国家级计划，需要大数据科学和工程洞察力的其他研究领域将包括在内。该奖项反映了NSF的法定使命，通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。