Cargando…
Applying machine learning to investigate long‐term insect–plant interactions preserved on digitized herbarium specimens
PREMISE: Despite the economic significance of insect damage to plants (i.e., herbivory), long‐term data documenting changes in herbivory are limited. Millions of pressed plant specimens are now available online and can be used to collect big data on plant–insect interactions during the Anthropocene....
Autores principales: | Meineke, Emily K., Tomasi, Carlo, Yuan, Song, Pryer, Kathleen M. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7328658/ https://www.ncbi.nlm.nih.gov/pubmed/32626611 http://dx.doi.org/10.1002/aps3.11369 |
Ejemplares similares
-
Using computer vision on herbarium specimen images to discriminate among closely related horsetails (Equisetum)
por: Pryer, Kathleen M., et al.
Publicado: (2020) -
Machine Learning Using Digitized Herbarium Specimens to Advance Phenological Research
por: Pearson, Katelin D, et al.
Publicado: (2020) -
Biases in estimation of insect herbivory from herbarium specimens
por: Kozlov, Mikhail V., et al.
Publicado: (2020) -
Increasing the efficiency of digitization workflows for herbarium specimens
por: Tulig, Melissa, et al.
Publicado: (2012) -
Computer vision applied to herbarium specimens of German trees: testing the future utility of the millions of herbarium specimen images for automated identification
por: Unger, Jakob, et al.
Publicado: (2016)