Deep learning-based high-throughput detection of in vitro germination to assess pollen viability from microscopic images.

Zhang, Mengwei; Zhao, Jianxiang; Hoshino, Yoichiro

Zhang, Mengwei; Zhao, Jianxiang; Hoshino, Yoichiro.

Afiliação

Zhang M; Division of Biosphere Science, Graduate School of Environmental Science, Hokkaido University, Kita 11, Nishi 10, Kita-ku, Sapporo 060-0811, Japan.
Zhao J; Division of Biosphere Science, Graduate School of Environmental Science, Hokkaido University, Kita 11, Nishi 10, Kita-ku, Sapporo 060-0811, Japan.
Hoshino Y; Division of Biosphere Science, Graduate School of Environmental Science, Hokkaido University, Kita 11, Nishi 10, Kita-ku, Sapporo 060-0811, Japan.

J Exp Bot ; 74(21): 6551-6562, 2023 11 21.

Article em En | MEDLINE | ID: mdl-37584205

Assuntos

Aprendizado Profundo; Germinação; Pólen; Tubo Polínico

Palavras-chave

Paeonia suffruticosa; Deep learning; Mask R-CNN; pollen germination frequency; pollen tube length; pollen viability

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Germinação / Aprendizado Profundo Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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