Tomato Leaf Disease Segmentation Using Clustering Method Based on FATPSO with Multi Features

Early blight is one of diseases that infects 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 give 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 offers 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. However, 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) is able to control minimum velocity the PSO particles adaptively for overcoming the premature convergence problem in PSO. The good features from image will increase the accuracy of machine learning algorithm. For this reason, these papers the tomato leaf segmentation based on the FATPSO clustering algorithm with multi features. The fitness function of FATPSO uses an objective function of K-means. The experiments use the image taken manually from garden tomatoes. The images have good quality but they have many varieties in size and color. The next research should be considered to use the image taken by drone to guarantee a robust method of image quality produced by drones. The experimental results show that the FATPSO clustering algorithm with multi features has a better performance than the PSO algorithm with multi feature in the tomato leaf disease segmentation