Digital Image Segmentation of Chicks Flock Using Clustering Method on Fog Computing Network

Image segmentation is one of the most widely used techniques for correctly classifying image pixels in decision-oriented applications. There are various image segmentation methods such as threshold, edge, cluster, and neural networks. Among the various methods, the most effective is the clustering method. In this study, we describe image preprocessing, a segmentation process using clustering methods using K-Means and Fuzzy C-Means algorithms in a nebula network. The evaluation results show that the segmentation process of chicken flock images in the fog network uses a clustering method, and the Fuzzy C-Means algorithm can produce better segmentation than K-Means. The results of this step can be used in further studies, namely to monitor the activity of flocks of chickens in fog nets.