Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/33348
metadata.artigo.dc.title: Multispectral radiometric monitoring of bacterial blight of coffee
metadata.artigo.dc.creator: Marin, Diego Bedin
Alves, Marcelo de Carvalho
Pozza, Edson Ampélio
Belan, Leônidas Leoni
Freitas, Marcelo Loran de Oliveira
metadata.artigo.dc.subject: Pseudomonas syringae pv. garcae
Remote sensing
Vegetation indices
Coffe - Bacterial blight
Sensoriamento remoto
Índices de vegetação
Café - Ferrugem bacteriana
metadata.artigo.dc.publisher: Springer
metadata.artigo.dc.date.issued: 2018
metadata.artigo.dc.identifier.citation: MARIN, D. B. et al. Multispectral radiometric monitoring of bacterial blight of coffee. Precision Agriculture, Dordrecht, p. 1-24, 2018. DOI: 10.1007/s11119-018-09623-9.
metadata.artigo.dc.description.abstract: Bacterial blight of coffee caused by Pseudomonas syringae pv. garcae shows great destructive potential in the main coffee producing regions in Brazil and worldwide. Remote sensing technologies can be used as an inexpensive and effective method to identify and monitor the disease. This study evaluated the potential of the Landsat 8 OLI/TIRS multispectral sensor for the spatial and temporal monitoring of coffee (Coffea arabica) affected by the bacterial blight. In a commercial coffee field in Minas Gerais State, Brazil, samples were collected from a grid of 85 points spaced from 35 to 50 m apart. Each sampling point consisted of five plants, being four plants distributed surrounding a central plant. The analyzes of the plant foliage, disease incidence, and disease severity were performed from January to December 2014 and correlated with 15 vegetation indices derived from a time series of 11 multispectral images. The brightness temperature of these images was calculated in order to indicate the area of the field more favorable to the occurrence of the bacterial blight of coffee. Vegetation indices were highly correlated with the incidence (r = 0.76) and severity (r = 0.52) of the disease. The brightness temperature aided in the mapping of areas with optimal temperature conditions for the occurrence of the disease. In general, the study demonstrated the potential of Landsat 8 OLI/TIRS images to identify and monitor crops affected by the bacterial blight of coffee.
metadata.artigo.dc.identifier.uri: https://link.springer.com/article/10.1007/s11119-018-09623-9
http://repositorio.ufla.br/jspui/handle/1/33348
metadata.artigo.dc.language: en_US
Appears in Collections:DFP - Artigos publicados em periódicos

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