GinJinn: An object‐detection pipeline for automated feature extraction from herbarium specimens
Premise The 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 obj...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-06-01
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| Series: | Applications in Plant Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aps3.11351 |