ArticleEcology and evolution2023
Identification of herbarium specimen sheet components from high-resolution images using deep learning.
Article in Ecology and evolution, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- herbUA Collectors: An open-source framework for online publication of the herbarium collector-centric data.Biodiversity data journal · 2026Article
- Hespi: a pipeline for automatically detecting information from herbarium specimen sheets.Bioscience · 2025Article
- SpeciMate: Improving metadata extraction from digitised biological specimens.Biodiversity data journal · 2025Article
- Herbarium collections remain essential in the age of community science.Nature communications · 2024Article
- Enhancing computer image recognition with improved image algorithms.Scientific reports · 2024Article
- A novel automated label data extraction and data base generation system from herbarium specimen images using OCR and NER.Scientific reports · 2024Article
- Identification of herbarium specimen sheet components from high-resolution images using deep learning.Ecology and evolution · 2023Article
- Enhancing plant morphological trait identification in herbarium collections through deep learning-based segmentation.Applications in plant sciencesArticle
- PlantSAM: An object detection-driven segmentation pipeline for herbarium specimens.Applications in plant sciencesArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advanced computer vision techniques hold the potential to mobilise vast quantities of biodiversity data by facilitating the rapid extraction of text- and trait-based data from herbarium specimen digital images, and to increase the efficiency and accuracy of downstream data capture during digitisation. This investigation developed an object detection model using YOLOv5 and digitised collection images from the University of Melbourne Herbarium (MELU). The MELU-trained 'sheet-component' model-trained on 3371 annotated images, validated on 1000 annotated images, run using 'large' model type, at 640 pixels, for 200 epochs-successfully identified most of the 11 component types of the digital specimen images, with an overall model precision measure of 0.983, recall of 0.969 and moving average precision (mAP0.5-0.95) of 0.847. Specifically, 'institutional' and 'annotation' labels were predicted with mAP0.5-0.95 of 0.970 and 0.878 respectively. It was found that annotating at least 2000 images was required to train an adequate model, likely due to the heterogeneity of specimen sheets. The full model was then applied to selected specimens from nine global herbaria (
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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.