An Image Segmentation Algorithm of Corn Disease Based on the Modified Bionic Pulse Coupled Neural Network

Abstract

The image segmentation of corn diseases is one of the critical technical aspects of digital image processing technology for disease recognition. Currently, aiming at solving the loss of colure texture of corn disease image segmentation, this paper proposes a modified bionic pulse coupled neural network. This proposed algorithm combined from pulse coupled neural network and a modified artificial bee colony algorithm. A revenue function is defined based on linear weighted function with maximum Shannon entropy and minimum cross-entropy. Through adaptive strategy of searching solutions, we optimized the parameters of pulse coupled neural network based on the modified ABC. The modified network is used to segment the color images of different kinds of corn disease in RGB color subspaces. Then combined with the results by color image merger strategy, we can get the terminal results of target area. The experimental results show that the proposed method could segment the disease regions better and set complexity parameters simplier.