期刊
BIOSCIENCE
卷 70, 期 7, 页码 610-620出版社
OXFORD UNIV PRESS
DOI: 10.1093/biosci/biaa044
关键词
phenology; machine learning; biodiversity; climate change; deep learning
类别
资金
- iDigBio - National Science Foundation's Advancing Digitization of Biodiversity Collections Program [DBI-1547229]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据