Plant Disease Image Segmentation applying the K-Means Algorithm, Firefly, and Particle Swarm Optimisation

Abstract

Abstract: The process of dividing an image to several pieces with comparable features is known as segmentation of images. A difficult phase in the image segmentation process is to extract the information from the image. Clustering is used to segment photos of a similar type or to assess the relevance of data. In essence, clustering is an unsupervised learning process. The data elements that make up one cluster share the same kinds of characteristics. In other words, each cluster has a minimum difference between its points and a maximum difference from the data points of other clusters. The suggested approach clusters image pixels by combining Particle Swarm Optimization with K-Means. Since combined techniques produce the best results, Kmeans should be thanked. K-means combined with Particle Swarm Optimization performs better than firefly combined with Kmeans. The firefly algorithm is used to tackle optimization issues and has a variety of uses. The Firefly method has been utilized in numerous research and optimization fields. Firefly algorithm and Firefly have been effectively utilized to solve a variety of issues. The firefly algorithm needs to be altered or combined with other algorithms in order to be used to a wide range of problems. Due to their metaheuristic nature, contemporary algorithms are crucial for solving NP-Hard problems