Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants

This 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.

[1]  P. S. Sastry,et al.  A survey of temporal data mining , 2006 .

[2]  Gladimir V. G. Baranoski,et al.  The Application of Photoacoustic Absorption Spectral Data to the Modeling of Leaf Optical Properties in the Visible Range , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Padhraic Smyth,et al.  Knowledge Discovery and Data Mining: Towards a Unifying Framework , 1996, KDD.

[4]  Lênio Soares Galvão,et al.  Eficácia de dados Hyperion/EO-1 para identificação de alvos agrícolas: comparação com dados ETM+/Landsat-7 , 2007 .

[5]  Vera Beatriz Köhler.  SENSORIAMENTO REMOTO NO ESTUDO DA VEGETAÇÃO BREVE REVISÃO , 1998 .

[6]  Waterloo Pereira Filho,et al.  Análise derivativa de dados hiperespectrais medidos em nível de campo e orbital para caracterizar a composição de águas opticamente complexas na Amazônia , 2007 .

[7]  J. Six,et al.  Object-based crop identification using multiple vegetation indices, textural features and crop phenology , 2011 .

[8]  Carlos H. C. Ribeiro,et al.  Utilização da Biblioteca TerraLib para Algoritmos de Agrupamento em Sistemas de Informações Geográficas , 2006, GEOINFO.

[9]  Mauricio Alves Moreira,et al.  Geotecnologias para mapear lavouras de café nos estados de Minas Gerais e São Paulo , 2010 .

[10]  Weili Wu,et al.  Effective Spatio-temporal Analysis of Remote Sensing Data , 2008, APWeb.

[11]  D. M. Gates,et al.  Spectral Properties of Plants , 1965 .

[12]  Rie Honda,et al.  Temporal Rule Discovery for Time-Series Satellite Images and Integration with RDB , 2001, PKDD.