Author(s)

Deny Lizbeth Hernández-Rabadán, Julian Guerrero, Fernando Ramos-Quintana

Tecnológico de Monterrey Campus Cuernavaca, Morelos, México.

Segmenting vegetation in color images is a complex task, especially when the background and lighting conditions of the environment are uncontrolled. This paper proposes a vegetation segmentation algorithm that combines a supervised and an unsupervised learning method to segment healthy and diseased plant images from the background. During the training stage, a Self-Organizing Map (SOM) neural network is applied to create different color groups from a set of images containing vegetation, acquired from a tomato greenhouse. The color groups are labeled as vegetation and non-vegetation and then used to create two color histogram models corresponding to vegetation and non-vegetation. In the online mode, input images are segmented by a Bayesian classifier using the two histogram models. This algorithm has provided a qualitatively better segmentation rate of images containing plants’ foliage in uncontrolled environments than the segmentation rate obtained by a color index technique, resulting in the elimination of the background and the preservation of important color information. This segmentation method will be applied in disease diagnosis of tomato plants in greenhouses as future work.

Image Segmentation; Color Images; Self-Organizing Maps; Bayesian Classifier

D. Hernández-Rabadán, J. Guerrero and F. Ramos-Quintana, "Method for Segmenting Tomato Plants in Uncontrolled Environments," Engineering, Vol. 4 No. 10, 2012, pp. 599-606. doi: 10.4236/eng.2012.410076.