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 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...

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Bibliographic Details
Main Authors: Natalie L. R. Love, Pierre Bonnet, Hervé Goëau, Alexis Joly, Susan J. Mazer
Format: Article
Language:English
Published: MDPI AG 2021-11-01
Series:Plants
Subjects:
regional convolutional neural network
object detection
deep learning
visual data classification
climate change
natural history collections
Online Access:https://www.mdpi.com/2223-7747/10/11/2471

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