ArticleApplications in plant sciences2020
Enabling automated herbarium sheet image post-processing using neural network models for color reference chart detection.
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 2 papers.
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Who cites it
2 citing papers in PubMed, 8 citations in OpenAlex.
- Performant barcode decoding for herbarium specimen images using vector-assisted region proposals (VARP).Applications in plant sciences · 2021Article
- Enabling automated herbarium sheet image post-processing using neural network models for color reference chart detection.Applications in plant sciences · 2020Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
premiseLarge-scale efforts to digitize herbaria have resulted in more than 18 million publicly available Plantae images on sites such as iDigBio. The automation of image post-processing will lead to time savings in the digitization of biological specimens, as well as improvements in data quality. Here, new and modified neural network methodologies were developed to automatically detect color reference charts (CRC), enabling the future automation of various post-processing tasks. METHODS AND
resultsWe used 1000 herbarium specimen images from 52 herbaria to test our novel neural network model, ColorNet, which was developed to identify CRCs smaller than 4 cm
conclusionsOur proposed neural networks detect a range of CRCs, which may enable the automation of post-processing tasks found in herbarium digitization workflows, such as image orientation or white balance correction.
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