A Deep Learning-Based Automatic Mosquito Sensing and Control System for Urban Mosquito Habitats.

Kim, Kyukwang; Hyun, Jieum; Kim, Hyeongkeun; Lim, Hwijoon; Myung, Hyun

Kim, Kyukwang; Hyun, Jieum; Kim, Hyeongkeun; Lim, Hwijoon; Myung, Hyun.

Afiliação

Kim K; Department of Biological Sciences, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon 34141, Korea. kkim0214@kaist.ac.kr.
Hyun J; Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon 34141, Korea. jimi.hyun@kaist.ac.kr.
Kim H; Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon 34141, Korea. hkkim1227@kaist.ac.kr.
Lim H; School of Electrical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon 34141, Korea. wjuni@kaist.ac.kr.
Myung H; School of Electrical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon 34141, Korea. hmyung@kaist.ac.kr.

Sensors (Basel) ; 19(12)2019 Jun 21.

Article em En | MEDLINE | ID: mdl-31234294

Assuntos

Proteínas de Bactérias/química; Aprendizado Profundo; Ecossistema; Controle de Mosquitos/métodos; Animais; Bacillus thuringiensis/química; Técnicas Biossensoriais; Processamento de Imagem Assistida por Computador/métodos; Redes Neurais de Computação

Palavras-chave

deep learning; drug spray; mosquito; urban habitat; vector control

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Proteínas de Bactérias / Controle de Mosquitos / Ecossistema / Aprendizado Profundo Limite: Animals Idioma: En Ano de publicação: 2019 Tipo de documento: Article

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