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

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, αθ, Vθ) 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.