ArticleApplications in plant sciences2019
Toward a large-scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras.
Article in Applications in plant sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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29 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
- From Knowledge to Action: Next Steps for the Natural Science Collections Community.Bioscience · 2026Article
- Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process.PLoS computational biology · 2025Article
- Envisaging a global infrastructure to exploit the potential of digitised collections.Biodiversity data journal · 2023Article
- 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
- Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study ofPlants (Basel, Switzerland) · 2021Article
- Harnessing Large-Scale Herbarium Image Datasets Through Representation Learning.Frontiers in plant science · 2021Article
- Using Convolutional Neural Networks to Efficiently Extract Immense Phenological Data From Community Science Images.Frontiers in plant science · 2021Article
- The Herbarium 2021 Half-Earth Challenge Dataset and Machine Learning Competition.Frontiers in plant science · 2021Article
- Repositories for Taxonomic Data: Where We Are and What is Missing.Systematic biology · 2020Review
- Machine Learning Using Digitized Herbarium Specimens to Advance Phenological Research.Bioscience · 2020Article
- GinJinn: An object-detection pipeline for automated feature extraction from herbarium specimens.Applications in plant sciences · 2020Article
- Generating segmentation masks of herbarium specimens and a data set for training segmentation models using deep learning.Applications in plant sciences · 2020Article
- A new fine-grained method for automated visual analysis of herbarium specimens: A case study for phenological data extraction.Applications in plant sciences · 2020Article
- Applying machine learning to investigate long-term insect-plant interactions preserved on digitized herbarium specimens.Applications in plant sciences · 2020Article
- Maximizing human effort for analyzing scientific images: A case study using digitized herbarium sheets.Applications in plant sciences · 2020Article
- Enabling automated herbarium sheet image post-processing using neural network models for color reference chart detection.Applications in plant sciences · 2020Article
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13 authors.
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Abstract
premise of the studyPhenological annotation models computed on large-scale herbarium data sets were developed and tested in this study.
methodsHerbarium specimens represent a significant resource with which to study plant phenology. Nevertheless, phenological annotation of herbarium specimens is time-consuming, requires substantial human investment, and is difficult to mobilize at large taxonomic scales. We created and evaluated new methods based on deep learning techniques to automate annotation of phenological stages and tested these methods on four herbarium data sets representing temperate, tropical, and equatorial American floras.
resultsDeep learning allowed correct detection of fertile material with an accuracy of 96.3%. Accuracy was slightly decreased for finer-scale information (84.3% for flower and 80.5% for fruit detection). DISCUSSION: The method described has the potential to allow fine-grained phenological annotation of herbarium specimens at large ecological scales. Deeper investigation regarding the taxonomic scalability of this approach is needed.
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