ArticleBiodiversity data journal2020
Detection and annotation of plant organs from digitised herbarium scans using deep learning.
Article in Biodiversity data journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 44 citations in OpenAlex.
- Petal to the metal: The slow road to automating large-scale phenology labeling for herbarium specimens.Applications in plant sciences · 2026Article
- Ethical collectomics: A guide to the responsible and fair mobilization of natural history collections.Bioscience · 2026Article
- Herbarium specimens in the age of artificial intelligence: from herbarium image identification to Integrative Taxonomic AI.Journal of plant research · 2026Article
- Soy-RepViT: a lightweight deep learning framework for fine-grained recognition of soybean growth habits.Frontiers in plant science · 2026Article
- Genes, shells, and AI: using computer vision to detect cryptic morphological divergence between genetically distinct populations of limpets.Scientific reports · 2025Article
- Estimating Rooting Depth From Herbarium Specimens Might Be More Accurate Than Using Large Trait Databases.Ecology and evolution · 2025Article
- Evaluating the method reproducibility of deep learning models in biodiversity research.PeerJ. Computer science · 2025Article
- Identification of herbarium specimen sheet components from high-resolution images using deep learning.Ecology and evolution · 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
- Automated Extraction of Phenotypic Leaf Traits of Individual Intact Herbarium Leaves from Herbarium Specimen Images Using Deep Learning Based Semantic Segmentation.Sensors (Basel, Switzerland) · 2021Article
- Comprehensive leaf size traits dataset for seven plant species from digitised herbarium specimen images covering more than two centuries.Biodiversity data journal · 2021Article
- Detection and annotation of plant organs from digitised herbarium scans using deep learning.Biodiversity data journal · 2020Article
- PlantSAM: An object detection-driven segmentation pipeline for herbarium specimens.Applications in plant sciencesArticle
- Enhancing plant morphological trait identification in herbarium collections through deep learning-based segmentation.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
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As herbarium specimens are increasingly becoming digitised and accessible in online repositories, advanced computer vision techniques are being used to extract information from them. The presence of certain plant organs on herbarium sheets is useful information in various scientific contexts and automatic recognition of these organs will help mobilise such information. In our study, we use deep learning to detect plant organs on digitised herbarium specimens with Faster R-CNN. For our experiment, we manually annotated hundreds of herbarium scans with thousands of bounding boxes for six types of plant organs and used them for training and evaluating the plant organ detection model. The model worked particularly well on leaves and stems, while flowers were also present in large numbers in the sheets, but were not equally well recognised.
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