BD Spokes: SPOKE: MIDWEST: Digital Agriculture - Unmanned Aircraft Systems, Plant Sciences and Education

数字农业谈到中西部大数据中心寻求组织学术、工业和政府部门，围绕农业中数据科学和大数据应用的政策和最佳实践的制定，特别关注无人机系统(UAS)以及植物科学、表型组学和基因组学的大数据生命周期自动化。这一努力是必要的，因为预计全球人口将增长(到2050年将达到95亿人)，这将要求全球农业劳动力生产的粮食比我们今天的农民多70%。从历史上看，农业在种植、社会组织和工业化方面的革命提供了增加粮食生产的手段。然而，未来的革命必须利用现代信息社会提供的优势。该项目将成为这场数据驱动的革命的催化剂，这场革命将具有广泛性和社会性，并解决经济可行、社会可接受和环境敏感的三重底线。虽然最初的重点领域是具体的，但由此产生的最佳做法和伙伴关系建设将转化为其他领域，并使之成为可能，例如遥感系统和农场管理技术。预期的结果是改进和有效利用农业科学中的无人机、成像和基因组学，最终导致更可持续的全球食品和营养系统。将通过数字农业数据科学资源的开放式门户网站加强这些活动的协调，该门户网站旨在整合现有的信息孤岛，促进协作，并促进劳动力发展。教育活动和工具将利用先前存在的培训方案和合作实体，并通过新开发的年度讲习班加以扩大。专题--将利用学术界、产业界和政府代表组成的团队对项目教育活动进行深入学习分析，以确定和完善扩大参与和多样化参与的机制。通过这些努力，合作将改善对数据资产的访问，培训具有相关技能和专业知识的劳动力，并将有助于解决可持续的全球粮食和营养安全挑战。该项目将专注于对农业、无人机和植物科学至关重要的两个知识领域。这两个知识价值主题将与旨在改善农业和农业生产相关环境中由多个高通量传感器和测量平台生成的数据的管理、可访问性、自动化和生命周期价值的横向活动融为一体。大数据的运输、存储、传播和分析的最佳实践将可移植和扩展到农场管理系统和精准农业等其他领域，并将使人们能够访问和使用与UAS和植物科学相关的宝贵数据资产，从而加快实现可持续农业生产的进程。在该项目下开发的许多想法和方法以及连接多个公共机构和私人实体的伙伴关系建设活动将可转移到其他需要大数据的学科，如交通、卫生科学、食品、能源和水，因此将从许多复杂的数据资源中产生创新和发现。这些合作的一个方面是希望建立一支拥有强大数据科学技能的劳动力队伍。为了实现这一目标，项目活动包括本科生、研究生和早期职业科学家参加年度会议、Zoom活动和网络研讨会。将支持和鼓励来自学术界、工业界和政府部门的感兴趣的参与者从事前沿研究和开发领域，如UAS对植物特征的直接数据采集、通过图像分析提取生物特征、大数据处理管道以及数据管理和共享技术。将通过一套问题团队来鼓励与UAS和植物科学相关的创新的多样性，这些团队使用深度学习技术分析面对面和基于网络的培训、目标导向的会议和会议活动的多样性。选择这些深度学习方式是因为它们的可扩充性，并改善了代表性不足群体的获取机会。该项目非常重视劳动力培训和最佳做法。研讨会和网络研讨会，包括黑客马拉松和数据马拉松，将帮助学生和已经就业的人扩大他们的职业发展。

The Digital Agriculture Spoke of the Midwest Big Data Hub seeks to organize academic, industrial, and governmental sectors around the development of policies and best practices for data science and Big Data applications in agriculture, with a particular focus on automating the Big Data lifecycle for unmanned aircraft systems (UAS) and for plant sciences, phenomics, and genomics. This effort is necessitated by the projected growth in the global population (9.5 billion people by 2050), which will require the global agricultural workforce to produce 70% more food than our farmers do today. Historically, agricultural revolutions in cultivation, social organization, and industrialization have provided the means to increase food production. However, future revolutions must leverage the advantages provided by the modern information society. This project will serve as a catalyst for this data-driven revolution, which will be broad and societal in nature and address the triple-bottom line of being economically viable, socially acceptable, and environmentally sensible. Whereas the initial focus areas are specific, the resulting best practices and partnership-building will translate to and enable other areas such as remote sensing systems and farm management techniques. An expected outcome is improved and efficient use of UAS, imaging, and genomics in agricultural sciences, ultimately leading to a more sustainable global food and nutrition system. Coordination of these activities will be enhanced through a Digital Agriculture open web portal of data science resources, designed to integrate existing information silos, facilitate collaboration, and contribute to workforce development. Educational activities and tools will be leveraged from pre-existing traineeship programs and collaborative entities, and broadened with newly developed annual workshops. Special issue-teams of academic, industrial, and governmental representatives will be used to conduct deep-learning analysis of project educational activities to identify and refine mechanisms for broadening and diversifying participation. Through these efforts, the collaboration will improve access to data assets, train a workforce with relevant skills and expertise, and will contribute to solving the Sustainable Global Food and Nutrition Security challenge.This project will focus on two knowledge domains important to agriculture, UAS, and Plant Sciences. These two themes of Intellectual Merit will be melded with cross-cutting activities designed to improve the management, accessibility, automation, and value of the lifecycle for data that are generated by multiple, high-throughput sensor and measurement platforms in contexts related to agriculture and agriculture production. Best practices for transport, storage, dissemination, and analysis of Big Data will be translatable and scalable to other areas such as farm management systems and precision agriculture, and will enable the access to and use of valuable data assets related to UAS and plant sciences, thereby accelerating progress toward sustainable agricultural production. Many of the ideas and methods developed under this project and the partnership-building activities that link multiple public institutions and private entities will be transferable to other disciplines that require Big Data, such as transportation, health sciences, and food, energy, and water, and will therefore generate innovation and discovery from many and complex data resources. One aspect of these partnerships is the desire to build a workforce with strong data science skillsets. To accomplish this, project activities include participation by undergraduate, graduate, and early career scientists in annual meetings, Zoom events, and webinars. Interested participants from the academic, industrial, and governmental sectors will be supported and encouraged to engage in cutting-edge research and development areas such as direct data collection of plant features by UAS, biological feature extraction through image analysis, Big Data processing pipelines, and techniques for data management and sharing. Diversity of innovation related to UAS and Plant Sciences will be encouraged through a suite of issue teams who analyze in-person and web-based trainings, goal-oriented Meetups, and conference events for diversity using deep learning techniques. These modalities for deep learning were selected for their scalability and improved access by underrepresented groups. The project has a heavy emphasis on workforce training and best practices. Workshops and webinars, including hackathons and datathons, will help both students and people already in the workforce expand their professional development.