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

Figure 1

Blocks diagram of the SEVUE methodology. Two phases are performed: training and testing phases. The main processes of SEVUE are the following: an image enhancement to adjust the color and brightness levels of the input images; a color clustering process using SOM1 and SOM2 (SOM2 refines the results of SOM1); a classification process by a Bayesian classifier which segments vegetation from input images; and finally a process for recovering areas incorrectly classified as nonvegetation, which at the output will yield the segmented image.