A distributed \(K\)-means segmentation algorithm applied to \textit{Lobesia botrana} recognition
type: Item
instance of: scholarly article
title: A distributed \(K\)-means segmentation algorithm applied to \textit{Lobesia botrana} recognition
zbMATH Open document ID: 1373.93238
DOI: 10.1155/2017/5137317
author: Peng Zhang
published in: Complexity
publication date: 2017-10-26T00:00:00Z
review text: 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.
Mathematics Subject Classification ID: 93C95
Mathematics Subject Classification ID: 94A08
Mathematics Subject Classification ID: 68U10
zbMATH DE Number: 6798528
zbMATH Keywords: proper control
zbMATH Keywords: insect
zbMATH Keywords: major pest in grapevine
zbMATH Keywords: image data mining
zbMATH Keywords: clustering segmentation
describes a project that uses: MetaSpark
describes a project that uses: MapReduce
describes a project that uses: ImageNet
describes a project that uses: AlexNet
describes a project that uses: Spark
describes a project that uses: Apache Spark
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/5137317
OpenAlex ID: W2743843277
description: scientific article; zbMATH DE number 6798528