LAUSR

Nematodes are a serious issue for coffee cultivation in Brazil. Because root infection by nematodes induces spectral variation in leaves and defines a unique spatial configuration in the cultivation field, the aim of this study was to use biophysical parameters and remote sensing data to discriminate and map healthy, moderately infected, and severely infected coffee plants. An experimental area in southern Minas Gerais State, in which the occurrence of nematodes was certified, was selected, and biophysical and spectral measurements of the leaves were made. Hyperspectral data were also used in a band simulation of the RapidEye sensor to identify the most sensitive spectral ranges for pathogen discrimination in coffee plants. These bands, plus a normalized difference vegetation index image, were used for a multispectral classification of healthy and nematode-infected areas. None of the biophysical parameters efficiently discriminated the leaves of healthy and infected plants, but the band simulation indicated that red, red edge, and near infrared spectral ranges were complementary to the discrimination of healthy coffee plants and the two levels of infection. The multispectral classification defined the spatial distribution of healthy, moderately infected, and severely infected coffee plants, with an overall accuracy of 78% and Kappa coefficient of 0.71. Considering the degree of uncertainty and high cost involved in conventional detection of soil parasites, the levels of accuracy achieved were adequate.