ArticleFrontiers in plant science2020
A New Method for Counting Reproductive Structures in Digitized Herbarium Specimens Using Mask R-CNN.
Article in Frontiers in plant science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 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
- 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
- Climate change intensifies plant-pollinator mismatch and increases secondary extinction risk for plants in northern latitudes.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A quartet-based approach for inferring phylogenetically informative features from genomic and phenomic data.Computational and structural biotechnology journal · 2025Article
- Sex-dependent phenological responses to climate vary across species' ranges.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Envisaging a global infrastructure to exploit the potential of digitised collections.Biodiversity data journal · 2023Article
- Batch-Mask: Automated Image Segmentation for Organisms with Limbless or Non-Standard Body Forms.Integrative and comparative biology · 2022Article
- The ecological implications of intra- and inter-species variation in phenological sensitivity.The New phytologist · 2022Article
- Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits.Plant methods · 2022Article
- Can Artificial Intelligence Help in the Study of Vegetative Growth Patterns from Herbarium Collections? An Evaluation of the Tropical Flora of the French Guiana Forest.Plants (Basel, Switzerland) · 2022Article
- Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning.Frontiers in plant science · 2022Article
- Can flowers affect land surface albedo and soil microclimates?International journal of biometeorology · 2021Article
- Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study ofPlants (Basel, Switzerland) · 2021Article
- The Herbarium 2021 Half-Earth Challenge Dataset and Machine Learning Competition.Frontiers in plant science · 2021Article
- Using Convolutional Neural Networks to Efficiently Extract Immense Phenological Data From Community Science Images.Frontiers in plant science · 2021Article
- mvh: An R tool to assemble and organize virtual herbaria from openly available specimen images.Applications in plant sciencesArticle
- From leaves to labels: Building modular machine learning networks for rapid herbarium specimen analysis with LeafMachine2.Applications in plant sciencesArticle
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phenology-the timing of life-history events-is a key trait for understanding responses of organisms to climate. The digitization and online mobilization of herbarium specimens is rapidly advancing our understanding of plant phenological response to climate and climatic change. The current practice of manually harvesting data from individual specimens, however, greatly restricts our ability to scale-up data collection. Recent investigations have demonstrated that machine-learning approaches can facilitate this effort. However, present attempts have focused largely on simplistic binary coding of reproductive phenology (e.g., presence/absence of flowers). Here, we use crowd-sourced phenological data of buds, flowers, and fruits from >3,000 specimens of six common wildflower species of the eastern United States (
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