01844naa a2200265 a 450000100080000000500110000800800410001910000250006024501270008526000090021252010200022165000200124165000190126165000110128065000250129165300280131665300160134465300250136065300290138570000180141470000230143270000260145570000170148177300800149819241152012-05-08       2012    bl uuuu     u00u1 u    #d1 aLAMPARELLI, R. A. C.  aUse of data mining and spectral profiles to differentiate condition after harvest of coffee plants.h[electronic resource]  c2012  aThis study aimed at identifying different conditions of coffee plants after harvesting period, using data mining and spectral behavior profiles from Hyperion/EO1 sensor. The Hyperion image, with spatial resolution of 30 m, was acquired in August 28th, 2008, at the end of the coffee harvest season in the studied area. For pre-processing imaging, atmospheric and signal/noise effect corrections were carried out using Flaash and MNF (Minimum Noise Fraction Transform) algorithms, respectively. Spectral behavior profiles (38) of different coffee varieties were generated from 150 Hyperion bands. The spectral behavior profiles were analyzed by Expectation-Maximization (EM) algorithm considering 2; 3; 4 and 5 clusters. T-test with 5% of significance was used to verify the similarity among the wavelength cluster means. The results demonstrated that it is possible to separate five different clusters, which were comprised by different coffee crop conditions making possible to improve future intervention actions.  aCrop management  aRemote sensing  aManejo  aSensoriamento Remoto  aComportamento espectral  aData mining  aMineração de dados  aMonitoramento de cultura1 aJOHANN, J. A.1 aSANTOS, É. R. dos1 aESQUERDO, J. C. D. M.1 aROCHA, J. V.  tEngenharia Agrícola, Jaboticabalgv. 32, n. 1, p. 184-196, jan./fev. 2012.