ArticleApplications in plant sciences2020
GinJinn: An object-detection pipeline for automated feature extraction from herbarium specimens.
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 12 papers.
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Who cites it
12 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
- Delayed flowering phenology of red-flowering plants in response to hummingbird migration.Current biology : CB · 2025Article
- Identification of herbarium specimen sheet components from high-resolution images using deep learning.Ecology and evolution · 2023Article
- A General-Purpose Machine Learning R Library for Sparse Kernels Methods With an Application for Genome-Based Prediction.Frontiers in genetics · 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
- Is color data from citizen science photographs reliable for biodiversity research?Ecology and evolution · 2021Article
- Not that kind of tree: Assessing the potential for decision tree-based plant identification using trait databases.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
- Detection and annotation of plant organs from digitised herbarium scans using deep learning.Biodiversity data journal · 2020Article
- From leaves to labels: Building modular machine learning networks for rapid herbarium specimen analysis with LeafMachine2.Applications in plant sciencesArticle
- PlantSAM: An object detection-driven segmentation pipeline for herbarium specimens.Applications in plant sciencesArticle
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
premiseThe generation of morphological data in evolutionary, taxonomic, and ecological studies of plants using herbarium material has traditionally been a labor-intensive task. Recent progress in machine learning using deep artificial neural networks (deep learning) for image classification and object detection has facilitated the establishment of a pipeline for the automatic recognition and extraction of relevant structures in images of herbarium specimens. METHODS AND
resultsWe implemented an extendable pipeline based on state-of-the-art deep-learning object-detection methods to collect leaf images from herbarium specimens of two species of the genus
conclusionsWe establish GinJinn as a deep-learning object-detection tool for the automatic recognition and extraction of individual leaves or other structures from herbarium specimens. Our pipeline offers greater flexibility and a lower entrance barrier than previous image-processing approaches based on hand-crafted features.
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