Recognition and Quantification of Area Damaged by Oligonychus Perseae in Avocado Leaves

  • Gloria Díaz
  • Eduardo Romero
  • Juan R. Boyero
  • Norberto Malpica
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)

Abstract

The measure of leaf damage is a basic tool in plant epidemiology research. Measuring the area of a great number of leaves is subjective and time consuming. We investigate the use of machine learning approaches for the objective segmentation and quantification of leaf area damaged by mites in avocado leaves. After extraction of the leaf veins, pixels are labeled with a look-up table generated using a Support Vector Machine with a polynomial kernel of degree 3, on the chrominance components of YCrCb color space. Spatial information is included in the segmentation process by rating the degree of membership to a certain class and the homogeneity of the classified region. Results are presented on real images with different degrees of damage.

Keywords

Leaf damage segmentation quantification machine learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Gloria Díaz
    • 1
  • Eduardo Romero
    • 1
  • Juan R. Boyero
    • 2
  • Norberto Malpica
    • 3
  1. 1.Universidad Nacional de ColombiaColombia
  2. 2.Centro de Investigación y Formación Agraria Cortijo de la CruzSpain
  3. 3.Universidad Rey Juan CarlosSpain

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