Machine Learning for the Study of Plankton and Marine Snow from Images.
Ann Rev Mar Sci
; 14: 277-301, 2022 01 03.
Article
em En
| MEDLINE
| ID: mdl-34460314
ABSTRACT
Quantitative imaging instruments produce a large number of images of plankton and marine snow, acquired in a controlled manner, from which the visual characteristics of individual objects and their in situ concentrations can be computed. To exploit this wealth of information, machine learning is necessary to automate tasks such as taxonomic classification. Through a review of the literature, we highlight the progress of those machine classifiers and what they can and still cannot be trusted for. Several examples showcase how the combination of quantitative imaging with machine learning has brought insights on pelagic ecology. They also highlight what is still missing and how images could be exploited further through trait-based approaches. In the future, we suggest deeper interactions with the computer sciences community, the adoption of data standards, and the more systematic sharing of databases to build a global community of pelagic image providers and users.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Plâncton
/
Aprendizado de Máquina
Idioma:
En
Revista:
Ann Rev Mar Sci
Assunto da revista:
BIOLOGIA
Ano de publicação:
2022
Tipo de documento:
Article