Image segmentation method for maize diseases based on pulse coupled neural network with modified artificial bee algorithm

Abstract: The image segmentation of crop diseases is one of the critical technical aspects of digital image processing technology for disease recognition. However, because of background information complexity of crop disease images, boundary area vagueness and noise effect of light and vein texture, there is no robust easy and practical method. At the same time, the color texture feature is one of the important criteria for identifying diseases, but there are serious influences on feature extraction and disease recognition because of the color texture information ignorance of most of the methods at present. The main contribution of this paper is that the segmentation appearance is more subtle and the color texture information is better when kept in the target area of crop diseases based on the proposed method--a pulse coupled neural network based on a modified artificial bee algorithm (MABC-PCNN). The basic idea of the color disease image segmentation is that the method of MABC-PCNN was used to segment the disease regions in RGB subspaces, then the results in three subspaces were merged in reference to a selective large probability merge strategy, and finally the final merger result was obtained. The concrete realization is as follows. Firstly, a method of MABC-OCNN was proposed in this paper, and in this method the parameters of PCNN (β is the linking strength, Vθ is an amplitude coefficient and aθ is a an incentive pulse attenuation coefficient, Vθ and aθ set the operation of neuromine) were automatically optimized through an improved ABC (MABC). In more detail, the above mentioned coefficient was described as the components of the feasible solution corresponding to the nectar source. By introducing scale adjustment factor ?, the solution search strategy of leader and follower had been adjusted, then through the evaluation principle of a weighted linear combination of maximum Shannon entropy and minimum cross-entropy, the results of segmentation with PCNN were evaluated and in the iteration of MABC, the optimal solution was set as the coefficients of PCNN. Secondly, in the iteration of the method, we got the optimal parameters of PCNN, and meanwhile we got the segmentation results in RGB subspaces. According to the selective large probability merge strategy, the results were merged and the final result of the color disease image segmentation was gotten. Further details are as follows that the pixel value variances of segmentation results in RGB subspaces were calculated, and then with the above variances the contributions of the pixel values were calculated. Finally, a mask template was obtained with the components of pixel value contribution. By masking operation with the mask template and an original color disease image, the final segmentation result was gotten.In a group of color maize disease images, the experimental results show that no matter whether subjective evaluation or objective evaluation was compared with V values, the segmentation appearance is more subtle and the color texture information is better remained in the target area of maize diseases based on the proposed method in contrast to GA-PCNN in Ref18. However, because of stochastic optimal control parameters with a swarm intelligent algorithm, the algorithm in this paper is of relatively high time complexity. Along with the continuous improvement of hardware performance, this problem will be solved.