HCC: Medium: Optimizing Interactive Machine Learning Tools to Support Plant Scientists using Human Centered Design

HCC：中：优化交互式机器学习工具以支持植物科学家使用以人为本的设计

• 批准号: 2312643
• 负责人: Lisa Anthony
• 金额: $ 119.91万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312643&HistoricalAwards=false
• 关键词: HCC; Medium; Optimizing; Interactive; Machine

Understanding the structure and function of plants, especially roots as they change over time, is essential to understand how plants adapt to changing climates and ensure sustainable production. Cutting-edge advances in this understanding are benefitting from huge increases in the diversity and sheer amount of data available from sensors. For example, plant science uses sensor technology called minirhizotron (MR) systems to study root development. These sensors capture color images of plant roots through cameras placed into the soil in clear tubes. Preparing these images to be used in scientific research requires enormous amounts of time, labor, and effort, due to the need for human interpretation of the data. Machine learning algorithms can automate some of the preparation, but we do not know how to design a system to help humans and machine learning algorithms work best together. This project develops methods and tools to support plant scientists (of varying backgrounds and expertise levels, including youth and other novices) working in tandem with machine learning to better utilize MR systems. Outcomes will include advances in both the interactive machine learning experience for human image labelers, as well as the relationship between participating in labeling and self-identification as scientists. The project will have broad implications for sensor-based data science in plant science and beyond, addressing multiple issues of global importance. The U.S. continues to experience a shortage of scientists-in-training, and the project will advance and evaluate efforts to draw more students into science. Science education programs in this project, which involve youth in designing the human-machine system, can help youth from marginalized backgrounds learn how science works and help them see themselves as future scientists. This project provides the tools needed to significantly reduce the analysis bottleneck of the plant root data generated by MR systems and, in the long term, enable larger-scale MR-based studies that may have significant global importance. The focus of this project is to develop interactive machine learning tools targeted to support plant scientists of varying expertise levels using a human-centered design approach. To accomplish these goals, this project triangulates findings from mixed methods, including laboratory studies of experienced labelers, participatory co-design workshops with stakeholders from diverse backgrounds, and summer participatory science experiences with Florida 4-H partner programs. The laboratory studies contribute new understanding of how human labeler behavior (such as annotation quantity and quality) affects machine learning algorithm performance, and vice versa. The participatory co-design workshops focus on designing interactive machine learning data visualization and labeling tools based in a human-centered understanding of plant scientists of varying expertise, including scientists, emerging scientists, and non-scientists, both youth and adults, as end users. Finally, the summer science experiences inform on how to scale this approach to broader domains and user populations beyond those traditionally engaged in STEM as youth. This project will facilitate higher throughput in the analysis of MR systems data in plant science, enabling future impacts to productivity, sustainability, and resilience of agricultural and natural ecosystems. It will also impact the throughput of human-centered machine learning in science in general. Methods from this project will also generalize to other similar labeling domains, such as human anatomy (blood vessels, neurons) or hydrology (river deltas, coastlines). Involving marginalized youth through partnerships with 4-H also grows the nation’s prospective STEM workforce.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.

了解植物的结构和功能，尤其是随着时间的流逝而变化，对于了解植物如何适应改变气候并确保可持续生产至关重要。这种理解的最先进的进步将受益于传感器可获得的多样性和大量数据的巨大增加。例如，植物科学使用称为Minirhizotron（MR）系统的传感器技术来研究根源的发展。这些传感器通过透明管中放入土壤中的相机捕获植物根的颜色图像。由于需要人类对数据的解释，因此准备用于科学研究的图像需要大量的时间，劳动和努力。机器学习算法可以自动化一些准备工作，但是我们不知道如何设计一个系统来帮助人类和机器学习算法最有效。该项目开发了支持植物科学家的方法和工具（具有不同背景和专业水平，包括青年和其他小说），与机器学习一起工作以更好地利用MR系统。结果将包括人类图像标签者的交互式机器学习经验，以及参与标签和作为科学家的自我认同之间的关系。该项目将对植物科学及其他地区的基于传感器的数据科学具有广泛的影响，以解决全球重要性的多个问题。美国继续遭受培训科学家的短缺，该项目将促进并评估吸引更多学生进入科学的努力。该项目中的科学教育计划涉及青年设计人机系统，可以帮助来自边缘化背景的年轻人学习科学的工作方式，并帮助他们将自己视为未来的科学家。该项目提供了所需的工具，可以显着减少MR系统生成的植物根系数据的分析，并从长远来看实现可能具有重要全球重要性的大规模MR研究。该项目的重点是开发针对以人为本的设计方法来支持具有不同专业知识水平的植物科学家的交互式机器学习工具。为了实现这些目标，该项目从混合方法中进行了三角调节结果，包括对经验丰富的标签者的实验室研究，与来自潜水员背景的利益相关者的参与共同设计研讨会以及佛罗里达4-H合作伙伴计划的夏季参与科学经验。实验室研究对人类标记行为（例如注释数量和质量）如何影响机器学习算法性能，反之亦然。参与共同设计研讨会着重于设计互动机器学习数据可视化和标记工具，以人为以人为中心的理解为以各种专业知识的植物科学家的理解，包括科学家，新兴的科学家以及最终用户的非科学家，包括青年和成人。最后，夏季科学经历了如何将这种方法扩展到更广泛的领域和用户群体之外，而不是传统上从事STEM的人。该项目将促进植物科学中MR系统数据的分析，从而对农业和自然生态系统的生产力，可持续性和韧性产生影响。这也将影响总体上以人为中心的机器学习的吞吐量。该项目的方法还将推广到其他类似的标记域，例如人体解剖结构（血管，神经元）或水文学（河流三角洲，海岸线）。通过与4-H的合作伙伴关系涉及边缘化的年轻人也发展了该国的潜在STEM劳动力。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准通过评估来获得支持的。