Title: Image of plant disease segmentation model based on improved pulse-coupled neural network

Authors: Xiaoyan Guo; Ming Zhang

Addresses: Information and Science Technology College, Gansu Agriculture University, Lanzhou, 730070, China ' School of Electronics and Information Engineering, Lanzhou City University, Lanzhou, 730070, China

Abstract: Image segmentation is a key step in feature extraction and disease recognition of plant diseases images. To avoid subjectivity while using a pulse-coupled neural network (PCNN) which realises parameter configuration through artificial exploration to segment plant disease images, an improved image segmentation model called SFLA-PCNN is proposed in this paper. The shuffled frog-leaping algorithm (SFLA) is used to optimise the parameters (β, α_θ, V_θ) of PCNN to improve PCNN performance. A series of plant disease images are taken as segmentation experiments, and the results reveal that SFLA-PCNN is more accurate than other methods mentioned in this paper and can extract lesion images from the background area effectively, providing a foundation for subsequent disease diagnosis.

Keywords: shuffled frog leap algorithm; pulse-coupled neural network; PCNN; plant disease.

DOI: 10.1504/IJCSE.2020.110198

International Journal of Computational Science and Engineering, 2020 Vol.23 No.1, pp.1 - 9

Received: 05 Oct 2019
Accepted: 08 Mar 2020
Published online: 08 Oct 2020 *

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