Segmentation of Leaf Spots Disease in Apple Plants Using Particle Swarm Optimization and K-means Algorithm

Abstract

Leaf spot disease is one of the causes of a decrease in apple production. Early detection of this disease will increase the quality and amount of apple production. Monitoring the health of apple plants in larger area is traditionally a job that requires a lot of time and effort. The use of drones to detect the leaf spot disease is an alternative technology to monitor the health of apple plants in large areas. The image of an apple leaf taken by a drone needs to be processed by segmenting the apple leaves into the infected and the healthy leaves to detect apple leaf spot disease. K-means clustering offers an algorithm that is simple, fast and works unsupervised to segment images compared to level set algorithms. Random selection of centroid will cause K-means to be trapped at the local optimum point and result in unsatisfactory image segmentation. To solve this problem, Particle Swarm Optimization offers a good solution to optimization problems to avoid convergence problems at the local optimum. Therefore, in this paper, we study how to segment leaf spot disease on apple leaves using the Particle Swarm Optimization and K-means algorithm. The objective function of K-means algorithm optimized by Particle Swarm Optimization is used for segmenting the leaf spot disease. The first step in this proposed method is to convert the leaf image from RGB to CIE L * a * b format. The a component’s of L * a * b format are taken and clustered by using the Particle Swarm Optimization. The global best of the Particle Swarm Optimization becomes an initial centroid of K-means algorithm. The experimental results show that the Particle Swarm Optimization-K-means (PSOK) has a better performance than the K-means algorithm in segmenting leaf spot disease.