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

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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.