Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study of <i>Streptanthus tortuosus</i>
Machine learning (ML) can accelerate the extraction of phenological data from herbarium specimens; however, no studies have assessed whether ML-derived phenological data can be used reliably to evaluate ecological patterns. In this study, 709 herbarium specimens representing a widespread annual herb...
Main Authors: | Natalie L. R. Love, Pierre Bonnet, Hervé Goëau, Alexis Joly, Susan J. Mazer |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-11-01
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Series: | Plants |
Subjects: | |
Online Access: | https://www.mdpi.com/2223-7747/10/11/2471 |
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