Performance analysis of pest detection for agricultural field using clustering techniques

Abstract

In modern agricultural field, pest detection cause significant reduction in both quality and quantity of tomato plant cultivation. In order to increase the Production rate of Tomato plant, the presence of whitefly pests which cause leaf discoloration or leaf deformation is the major problem. Pest detection using clustering is a powerful technique reached in image segmentation. The performance of the image segmentation algorithm depends on its simplification of image. The clustering methods such as K-Means and Fuzzy c means (FCM) algorithms have been proposed. The purpose of these clustering is to identify the accuracy and required time consumes to segmented gray scale pest image. FCM clustering achieves better segmentation and provides flexibility for the pixels belongs to various classes. Also performance analysis is measured for quality of image such as structural content, peak signal to noise ratio, normalized correlation coefficient, average difference and normalized absolute error. The algorithm was developed and implemented using MATLAB 7.14 build 2012a.