Abstract Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens—preserved plant material curated in natural history collections—but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth.

中文翻译：

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使用数字化植物标本馆标本进行机器学习以推进物候研究

摘要 机器学习 (ML) 通过从植物标本馆（自然历史收藏中保存的植物材料）的图像中获取数据来推动科学发现具有巨大潜力，但 ML 技术直到最近才应用于这一丰富的资源。ML 在研究植物物候事件（如生长和繁殖）方面具有特别强的前景。作为气候变化的主要指标、生态过程的驱动因素和植物健康的关键决定因素，植物物候学是 ML 技术在科学和社会中应用的重要前沿。在本文中，我们描述了一种用于从植物标本馆标本图像中提取物候数据的通用模块化 ML 工作流程，并讨论了该工作流程的优势、局限性和潜在的未来改进。