Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study of Streptanthus tortuosus
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DOI: 10.3390/plants10112471
Abstract: 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… read more here.
Keywords: phenological data; streptanthus tortuosus; machine learning; herbarium specimens ... See more keywords