ArticleBioscience2020
Machine Learning Using Digitized Herbarium Specimens to Advance Phenological Research.
Article in Bioscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep Learning in Plant Phenological Research: A Systematic Literature Review.Frontiers in plant science · 2022Pooled it
- Petal to the metal: The slow road to automating large-scale phenology labeling for herbarium specimens.Applications in plant sciences · 2026Article
- Manage your image database with computer vision-Lessons learned at Encyclopedia of Life.Bioscience · 2026Article
- Herbarium specimens in the age of artificial intelligence: from herbarium image identification to Integrative Taxonomic AI.Journal of plant research · 2026Article
- The promise of digital herbarium specimens in large-scale phenology research.The New phytologist · 2026Review
- Bridging data silos to holistically model plant macrophenology.The New phytologist · 2026Review
- From Knowledge to Action: Next Steps for the Natural Science Collections Community.Bioscience · 2026Article
- Global sampling decline erodes science potential of natural history collections.Nature communications · 2025Article
- Hespi: a pipeline for automatically detecting information from herbarium specimen sheets.Bioscience · 2025Article
- A vision of human-AI collaboration for enhanced biological collection curation and research.Bioscience · 2025Article
- National biodiversity data infrastructures: ten essential functions for science, policy, and practice.Bioscience · 2025Article
- The digitisation workflow of the herbarium of the State Museum of Natural History of the NAS of Ukraine (LWS).Biodiversity data journal · 2025Article
- Article
- Ten best practices for effective phenological research.International journal of biometeorology · 2023Review
- Envisaging a global infrastructure to exploit the potential of digitised collections.Biodiversity data journal · 2023Article
- Article
- Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study ofPlants (Basel, Switzerland) · 2021Article
- Analyzing a phenological anomaly in Yucca of the southwestern United States.Scientific reports · 2021Article
- The Herbarium 2021 Half-Earth Challenge Dataset and Machine Learning Competition.Frontiers in plant science · 2021Article
- Towards Sustainable North American Wood Product Value Chains, Part I: Computer Vision Identification of Diffuse Porous Hardwoods.Frontiers in plant science · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.