Your browser doesn't support javascript.
loading
Aphid cluster recognition and detection in the wild using deep learning models.
Zhang, Tianxiao; Li, Kaidong; Chen, Xiangyu; Zhong, Cuncong; Luo, Bo; Grijalva, Ivan; McCornack, Brian; Flippo, Daniel; Sharda, Ajay; Wang, Guanghui.
Afiliação
  • Zhang T; Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, 66045, USA.
  • Li K; Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, 66045, USA.
  • Chen X; Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, 66045, USA.
  • Zhong C; Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, 66045, USA.
  • Luo B; Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, 66045, USA.
  • Grijalva I; Department of Entomology, Kansas State University, Manhattan, KS, 66506, USA.
  • McCornack B; Department of Entomology, Kansas State University, Manhattan, KS, 66506, USA.
  • Flippo D; Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS, 66506, USA.
  • Sharda A; Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS, 66506, USA.
  • Wang G; Department of Computer Science, Toronto Metropolitan University, Toronto, ON, M5B 2K3, Canada. wangcs@torontomu.ca.
Sci Rep ; 13(1): 13410, 2023 08 17.
Article em En | MEDLINE | ID: mdl-37591898
Assuntos
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Afídeos / Aprendizado Profundo Tipo de estudo: Diagnostic_studies Limite: Animals Idioma: En Revista: Sci Rep Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Afídeos / Aprendizado Profundo Tipo de estudo: Diagnostic_studies Limite: Animals Idioma: En Revista: Sci Rep Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos