Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/42841
metadata.artigo.dc.title: The use of machine learning in digital processing of satellite images applied to coffee crop.
metadata.artigo.dc.creator: Miranda, Jonathan da Rocha
Alves, Marcelo de Carvalho
metadata.artigo.dc.subject: Remote sensing
satellite imagery
Coffee
Image processing
Sensoriamento remoto
Processamento digital de imagens
Cafeicultura
Imagem de satélite
metadata.artigo.dc.publisher: CABI
metadata.artigo.dc.date.issued: 2020
metadata.artigo.dc.identifier.citation: MIRANDA, J. da R.; ALVES, M. de C. The use of machine learning in digital processing of satellite images applied to coffee crop. CAB Reviews, Wallingford, v. 15, n. 45, p. 1-10, 2020. DOI: 10.1079/PAVSNNR202015045.
metadata.artigo.dc.description.abstract: Remote sensing can be used to monitor and estimate, with reasonable correct answers, the yield, plant health, and coffee nutrition. Satellite-coupled sensors can obtain information about the spectral signature of the crop, on a time scale, in order to monitor and detect phenological changes. However, the accumulation of data obtained by orbital sensors makes it difficult to understand the relationship between the aspects of coffee. Thus, machine learning can perform data mining and meet the spectral signature patterns that constitute coffee behavior. This literature review sought the survey of research that used machine learning tools applied in digital image processing from satellites for coffee crop monitoring.
metadata.artigo.dc.identifier.uri: https://www.cabdirect.org/cabdirect/abstract/20203350450
http://repositorio.ufla.br/jspui/handle/1/42841
metadata.artigo.dc.language: en
Appears in Collections:DEA - Artigos publicados em periódicos
DEG - Artigos publicados em periódicos

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