A Review of Successes and Impeding Challenges of IoT-Based Insect Pest Detection Systems for Estimating Agroecosystem Health and Productivity of Cotton.

Kiobia, Denis O; Mwitta, Canicius J; Fue, Kadeghe G; Schmidt, Jason M; Riley, David G; Rains, Glen C

Kiobia, Denis O; Mwitta, Canicius J; Fue, Kadeghe G; Schmidt, Jason M; Riley, David G; Rains, Glen C.

Afiliação

Kiobia DO; College of Engineering, University of Georgia, Tifton, GA 31793, USA.
Mwitta CJ; College of Engineering, University of Georgia, Tifton, GA 31793, USA.
Fue KG; Department of Agricultural Engineering, School of Engineering Science and Technology, Sokoine University of Agriculture, Morogoro P.O. Box 3003, Tanzania.
Schmidt JM; Department of Entomology, University of Georgia, Tifton, GA 31793, USA.
Riley DG; Department of Entomology, University of Georgia, Tifton, GA 31793, USA.
Rains GC; College of Engineering, University of Georgia, Tifton, GA 31793, USA.

Sensors (Basel) ; 23(8)2023 Apr 20.

Article em En | MEDLINE | ID: mdl-37112469

Assuntos

Inteligência Artificial; Gossypium; Animais; Insetos; Agricultura; Algoritmos

Palavras-chave

computer vision; cotton pests; machine learning; pest control; pest detection; precision agriculture

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Gossypium Tipo de estudo: Diagnostic_studies Limite: Animals Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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