A distributed K-means segmentation algorithm applied to Lobesia botrana recognition

JOSE ANTONIO GARCIA CONEJEROS, Christopher Pope, Francisco Altimiras

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

Early detection of 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 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 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.

Original languageEnglish
Article number5137317
JournalComplexity
Volume2017
DOIs
StatePublished - 2017
Externally publishedYes

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