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

Engenharia Agrícola

Rubens A. C. Lamparelli¹  Jerry A. Johann¹  Éder R. Dos Santos²  Julio C. D. M. Esquerdo¹  Jansle V. Rocha¹ 

[1] ,CooxupéMonte Santo de Minas MG

关键词: crop monitoring;    spectral behavior;    management;    orbital remote sensing;    monitoramento de cultura;    comportamento espectral;    manejo;    sensoriamento remoto;   

DOI  :  10.1590/S0100-69162012000100019

来源: SciELO

【 摘 要 】

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.

【 授权许可】

CC BY   
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