Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery.

Khan, Haseeb Rehman; Gillani, Zeeshan; Jamal, Muhammad Hasan; Athar, Atifa; Chaudhry, Muhammad Tayyab; Chao, Haoyu; He, Yong; Chen, Ming

Khan, Haseeb Rehman; Gillani, Zeeshan; Jamal, Muhammad Hasan; Athar, Atifa; Chaudhry, Muhammad Tayyab; Chao, Haoyu; He, Yong; Chen, Ming.

Afiliación

Khan HR; Department of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan.
Gillani Z; Department of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan.
Jamal MH; Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.
Athar A; Department of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan.
Chaudhry MT; Department of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan.
Chao H; Department of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan.
He Y; Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.
Chen M; College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.

Sensors (Basel) ; 23(4)2023 Feb 05.

Article en En | MEDLINE | ID: mdl-36850377

Asunto(s)

COVID-19; Aprendizaje Profundo; Humanos; Granjas; Pandemias; Agricultura; Productos Agrícolas

Palabras clave

Sentinel-2; crop classification; crop-type mapping; deep learning

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo / COVID-19 Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: Sensors (Basel) Año: 2023 Tipo del documento: Article País de afiliación: Pakistán Pais de publicación: Suiza

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