Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study of Streptanthus tortuosus

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Love, Natalie L. R., Bonnet, Pierre, Goëau, Hervé, Joly, Alexis, Mazer, Susan J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8623300/
https://www.ncbi.nlm.nih.gov/pubmed/34834835
http://dx.doi.org/10.3390/plants10112471

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8623300/
https://www.ncbi.nlm.nih.gov/pubmed/34834835
http://dx.doi.org/10.3390/plants10112471

Ejemplares similares