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，特别是根随着时间的推移而变化，对于了解植物如何适应气候变化并确保可持续生产至关重要。这一理解的前沿进展受益于传感器提供的数据的多样性和绝对数量的巨大增加。例如，植物科学使用被称为微电子（MR）系统的传感器技术来研究根系发育。这些传感器通过放置在透明管中的相机捕获植物根部的彩色图像。准备这些图像用于科学研究需要大量的时间，劳动力和精力，因为需要人工解释数据。机器学习算法可以自动化一些准备工作，但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好地协同工作。该项目开发方法和工具，以支持植物科学家（不同背景和专业知识水平，包括青年和其他新手）与机器学习协同工作，以更好地利用MR系统。成果将包括人类图像标记者的交互式机器学习体验的进步，以及参与标记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领域基于传感器的数据科学产生广泛影响，解决具有全球重要性的多个问题。 美国继续面临培训科学家短缺的问题，该项目将推动和评估吸引更多学生进入科学领域的努力。该项目中的科学教育项目让青年参与设计人机系统，可以帮助来自边缘化背景的青年学习科学如何运作，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，以显着减少MR系统生成的植物根系数据的分析瓶颈，并在长期内，使大规模的基于MR的研究，可能具有显着的全球重要性。该项目的重点是开发交互式机器学习工具，以支持不同专业水平的植物科学家使用以人为本的设计方法。为了实现这些目标，该项目从混合方法，包括实验室研究经验丰富的标签，参与式共同设计研讨会与利益相关者来自不同背景，夏季参与科学经验与佛罗里达4-H合作伙伴计划的三角调查结果。实验室研究为人类标签行为（如注释数量和质量）如何影响机器学习算法性能提供了新的理解，反之亦然。参与式共同设计研讨会的重点是设计交互式机器学习数据可视化和标记工具，基于以人为本的理解，包括科学家，新兴科学家和非科学家，包括青年和成年人，作为最终用户。最后，夏季科学经验告诉我们如何将这种方法扩展到更广泛的领域和用户群体，而不仅仅是那些传统上从事STEM的年轻人。该项目将促进植物科学中MR系统数据分析的更高吞吐量，从而对农业和自然生态系统的生产力，可持续性和恢复力产生未来影响。 它还将影响以人为中心的机器学习在科学中的吞吐量。该项目的方法也将推广到其他类似的标记领域，如人体解剖学（血管，神经元）或水文学（河流三角洲，海岸线）。通过与4-H的合作关系，让边缘化的青年参与进来，也增加了国家未来的STEM劳动力。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。