A distributed \(K\)-means segmentation algorithm applied to \textit{Lobesia botrana} recognition
Summary: Early detection of \textit{Lobesia botrana} is a primary issue for a proper control of this insect considered as the major pest in grapevine. In this article, we propose a novel method for \textit{L. botrana} recognition using image data mining based on clustering segmentation with descriptors which consider gray scale values and gradient in each segment. This system allows a 95 percent of \textit{L. botrana} recognition in non-fully controlled lighting, zoom, and orientation environments. Our image capture application is currently implemented in a mobile application and subsequent segmentation processing is done in the cloud.
scientific article; zbMATH DE number 6798528