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Deep learning and computer vision will transform entomology.
Høye, Toke T; Ärje, Johanna; Bjerge, Kim; Hansen, Oskar L P; Iosifidis, Alexandros; Leese, Florian; Mann, Hjalte M R; Meissner, Kristian; Melvad, Claus; Raitoharju, Jenni.
Afiliação
  • Høye TT; Department of Bioscience, Aarhus University, DK-8410 Rønde, Denmark; tth@bios.au.dk.
  • Ärje J; Arctic Research Centre, Aarhus University, DK-8410 Rønde, Denmark.
  • Bjerge K; Department of Bioscience, Aarhus University, DK-8410 Rønde, Denmark.
  • Hansen OLP; Arctic Research Centre, Aarhus University, DK-8410 Rønde, Denmark.
  • Iosifidis A; Unit of Computing Sciences, Tampere University, FI-33720 Tampere, Finland.
  • Leese F; School of Engineering, Aarhus University, DK-8200 Aarhus N, Denmark.
  • Mann HMR; Department of Bioscience, Aarhus University, DK-8410 Rønde, Denmark.
  • Meissner K; Arctic Research Centre, Aarhus University, DK-8410 Rønde, Denmark.
  • Melvad C; Natural History Museum Aarhus, DK-8000 Aarhus C, Denmark.
  • Raitoharju J; Department of Biology-Center for Biodiversity Dynamics in a Changing World, Aarhus University, DK-8000 Aarhus C, Denmark.
Proc Natl Acad Sci U S A ; 118(2)2021 01 12.
Article em En | MEDLINE | ID: mdl-33431561
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Entomologia / Monitorização de Parâmetros Ecológicos / Aprendizado Profundo / Insetos Limite: Animals Idioma: En Ano de publicação: 2021 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Entomologia / Monitorização de Parâmetros Ecológicos / Aprendizado Profundo / Insetos Limite: Animals Idioma: En Ano de publicação: 2021 Tipo de documento: Article