Image-Based Automated Recognition of 31 Poaceae Species: The Most Relevant Perspectives.

Rzanny, Michael; Wittich, Hans Christian; Mäder, Patrick; Deggelmann, Alice; Boho, David; Wäldchen, Jana

Rzanny, Michael; Wittich, Hans Christian; Mäder, Patrick; Deggelmann, Alice; Boho, David; Wäldchen, Jana.

Afiliación

Rzanny M; Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany.
Wittich HC; Data-intensive Systems and Visualisation, Technische Universität Ilmenau, Ilmenau, Germany.
Mäder P; Data-intensive Systems and Visualisation, Technische Universität Ilmenau, Ilmenau, Germany.
Deggelmann A; Faculty of Biological Sciences, Friedrich Schiller University, Jena, Germany.
Boho D; Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany.
Wäldchen J; Data-intensive Systems and Visualisation, Technische Universität Ilmenau, Ilmenau, Germany.

Front Plant Sci ; 12: 804140, 2021.

Article en En | MEDLINE | ID: mdl-35154194

Palabras clave

Poaceae; accuracy; automated plant identification; deep learning; fine-grained image classification; image recognition; machine learning; plant perspective

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Front Plant Sci Año: 2021 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Suiza

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