ArticleApplications in plant sciences2020
Generating segmentation masks of herbarium specimens and a data set for training segmentation models using deep learning.
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 17 papers.
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Who cites it
17 citing papers in PubMed.
- Herbarium specimens in the age of artificial intelligence: from herbarium image identification to Integrative Taxonomic AI.Journal of plant research · 2026Article
- Opportunities and Challenges in Applying AI to Evolutionary Morphology.Integrative organismal biology (Oxford, England) · 2024Review
- Identification of herbarium specimen sheet components from high-resolution images using deep learning.Ecology and evolution · 2023Article
- Application of a deep learning image classifier for identification of Amazonian fishes.Ecology and evolution · 2023Article
- 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
- Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping.PloS one · 2021Article
- Harnessing Large-Scale Herbarium Image Datasets Through Representation Learning.Frontiers in plant science · 2021Article
- A large-scale optical microscopy image dataset of potato tuber for deep learning based plant cell assessment.Scientific data · 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
- Using computer vision on herbarium specimen images to discriminate among closely related horsetails (Applications in plant sciences · 2020Article
- Computer vision species identification of lichens and bryophytes from biocrusts in Australian drylands.Applications in plant sciencesArticle
- 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
- An image-based technique for automated root disease severity assessment using PlantCV.Applications in plant sciencesArticle
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5 authors.
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Abstract
premiseDigitized images of herbarium specimens are highly diverse with many potential sources of visual noise and bias. The systematic removal of noise and minimization of bias must be achieved in order to generate biological insights based on the plants rather than the digitization and mounting practices involved. Here, we develop a workflow and data set of high-resolution image masks to segment plant tissues in herbarium specimen images and remove background pixels using deep learning. METHODS AND
resultsWe generated 400 curated, high-resolution masks of ferns using a combination of automatic and manual tools for image manipulation. We used those images to train a U-Net-style deep learning model for image segmentation, achieving a final Sørensen-Dice coefficient of 0.96. The resulting model can automatically, efficiently, and accurately segment massive data sets of digitized herbarium specimens, particularly for ferns.
conclusionsThe application of deep learning in herbarium sciences requires transparent and systematic protocols for generating training data so that these labor-intensive resources can be generalized to other deep learning applications. Segmentation ground-truth masks are hard-won data, and we share these data and the model openly in the hopes of furthering model training and transfer learning opportunities for broader herbarium applications.
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