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The Scientific World Journal
Volume 2014 (2014), Article ID 214674, 13 pages
Research Article

Integrating SOMs and a Bayesian Classifier for Segmenting Diseased Plants in Uncontrolled Environments

1ITESM, Autopista del Sol, 62790 Xochitepec, MOR, Mexico
2Centro de Investigación en Biotecnología/Laboratorio de Investigaciones Ambientales, Universidad Autónoma del Estado de Morelos, Avenida Universidad 1001, 62209 Cuernavaca, MOR, Mexico

Received 18 July 2014; Revised 5 October 2014; Accepted 6 October 2014; Published 4 November 2014

Academic Editor: Gonzalo Pajares

Copyright © 2014 Deny Lizbeth Hernández-Rabadán et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This work presents a methodology that integrates a nonsupervised learning approach (self-organizing map (SOM)) and a supervised one (a Bayesian classifier) for segmenting diseased plants that grow in uncontrolled environments such as greenhouses, wherein the lack of control of illumination and presence of background bring about serious drawbacks. During the training phase two SOMs are used: one that creates color groups of images, which are classified into two groups using -means and labeled as vegetation and nonvegetation by using rules, and a second SOM that corrects classification errors made by the first SOM. Two color histograms are generated from the two color classes and used to estimate the conditional probabilities of the Bayesian classifier. During the testing phase an input image is segmented by the Bayesian classifier and then it is converted into a binary image, wherein contours are extracted and analyzed to recover diseased areas that were incorrectly classified as nonvegetation. The experimental results using the proposed methodology showed better performance than two of the most used color index methods.