Use of synthetic images for training a deep learning model for weed detection and biomass estimation in cotton.

Sapkota, Bishwa B; Popescu, Sorin; Rajan, Nithya; Leon, Ramon G; Reberg-Horton, Chris; Mirsky, Steven; Bagavathiannan, Muthukumar V

Sapkota, Bishwa B; Popescu, Sorin; Rajan, Nithya; Leon, Ramon G; Reberg-Horton, Chris; Mirsky, Steven; Bagavathiannan, Muthukumar V.

Afiliación

Sapkota BB; Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, 77843, USA.
Popescu S; Department of Ecosystem Science and Management, Texas A&M University, College Station, TX, 77843, USA.
Rajan N; Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, 77843, USA.
Leon RG; Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, 27695, USA.
Reberg-Horton C; Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, 27695, USA.
Mirsky S; Sustainable Agricultural Systems Laboratory, USDA-ARS, Beltsville, MD, 20705, USA.
Bagavathiannan MV; Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, 77843, USA. muthu@tamu.edu.

Sci Rep ; 12(1): 19580, 2022 11 15.

Article en En | MEDLINE | ID: mdl-36379963

Asunto(s)

Aprendizaje Profundo; Biomasa; Redes Neurales de la Computación; Malezas; Productos Agrícolas; Poaceae; Gossypium; Procesamiento de Imagen Asistido por Computador/métodos

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Sci Rep Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido

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