DiSCount: computer vision for automated quantification of <i>Striga</i> seed germination.

Masteling, Raul; Voorhoeve, Lodewijk; IJsselmuiden, Joris; Dini-Andreote, Francisco; de Boer, Wietse; Raaijmakers, Jos M

DiSCount: computer vision for automated quantification of Striga seed germination.

Masteling, Raul; Voorhoeve, Lodewijk; IJsselmuiden, Joris; Dini-Andreote, Francisco; de Boer, Wietse; Raaijmakers, Jos M.

Afiliación

Masteling R; 1Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), PO BOX 50, 6708 PB Wageningen, The Netherlands.
Voorhoeve L; 2Institute of Biology, Leiden University, Leiden, The Netherlands.
IJsselmuiden J; Track32, Wageningen, The Netherlands.
Dini-Andreote F; 4School of Engineering and Applied Science, Rotterdam University of Applied Sciences, Rotterdam, The Netherlands.
de Boer W; Track32, Wageningen, The Netherlands.
Raaijmakers JM; 1Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), PO BOX 50, 6708 PB Wageningen, The Netherlands.

Plant Methods ; 16: 60, 2020.

Article en En | MEDLINE | ID: mdl-32377220

Palabras clave

Computer vision; Deep learning; High-throughput assays; Machine learning; Parasitic weeds; Striga hermonthica

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Plant Methods Año: 2020 Tipo del documento: Article País de afiliación: Países Bajos

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