ArticleApplications in plant sciences2020
A new fine-grained method for automated visual analysis of herbarium specimens: A case study for phenological data extraction.
Article in Applications in plant sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 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
- Herbarium specimens in the age of artificial intelligence: from herbarium image identification to Integrative Taxonomic AI.Journal of plant research · 2026Article
- The digitisation workflow of the herbarium of the State Museum of Natural History of the NAS of Ukraine (LWS).Biodiversity data journal · 2025Article
- 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
- 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
- Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots.Applications in plant sciences · 2020Article
- GinJinn: An object-detection pipeline for automated feature extraction from herbarium specimens.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
- A New Method for Counting Reproductive Structures in Digitized Herbarium Specimens Using Mask R-CNN.Frontiers in plant science · 2020Article
- 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
premiseHerbarium specimens represent an outstanding source of material with which to study plant phenological changes in response to climate change. The fine-scale phenological annotation of such specimens is nevertheless highly time consuming and requires substantial human investment and expertise, which are difficult to rapidly mobilize.
methodsWe trained and evaluated new deep learning models to automate the detection, segmentation, and classification of four reproductive structures of
resultsThe main outcome of our study is that the performance of detection and segmentation can vary significantly with: (i) the type of annotations used for training, (ii) the type of reproductive structures, and (iii) the size of the reproductive structures. In the case of DISCUSSION: This method has great potential for automating the analysis of reproductive structures in high-resolution images of herbarium sheets. Deeper investigations regarding the taxonomic scalability of this approach and its potential improvement will be conducted in future work.
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