Detecting the tomato leaf disease using clustering based on fuzzy adaptive turbulence particle swarm optimization

Early blight is one of diseases that infects tomato leaves. The symptoms of this disease are characterized by the appearance of dark spots on tomato leaves. This disease causes a decrease in the production of tomato plants. The early detection of this diseases is very important to maintain the tomato production. Monitoring tomato leaves health manually in large area is very time-consuming and inefficient. The drones and computer vision technology is an alternative in solving this problem. One of the important steps in detecting the tomato leaf disease based on computer vision is the segmentation area of the tomato leaf into the healthy and diseased tomato leaf. The K-means clustering is an image segmentation method that is simple, fast and works unsupervised. However, the solutions of the K-means clustering often be trapped into the local optimum. The Particle Swarm Optimization (PSO) offers a solution of this problem. The performance of PSO depends on the particle velocity of the PSO, if the particle velocity is not determined precisely then the PSO will converge prematurely. Fuzzy Adaptive Turbulence Particle Swarm Optimization (FATPSO) offers an adaptive minimum velocity control of the PSO particles to overcome the premature convergence problem in PSO. For this reason, these papers propose the FATPSO to detect the tomato leaf disease. The fitness function of FATPSO uses an objective function of K-means. The experimental results show that the FATPSO has a better performance than the PSO algorithm in detecting the tomato leaf disease.