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A Distributed -Means Segmentation Algorithm Applied to Lobesia botrana Recognition

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  • José García
  • Christopher Pope
  • Francisco Altimiras

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.

Suggested Citation

  • José García & Christopher Pope & Francisco Altimiras, 2017. "A Distributed -Means Segmentation Algorithm Applied to Lobesia botrana Recognition," Complexity, Hindawi, vol. 2017, pages 1-14, August.
    • Handle: RePEc:hin:complx:5137317
    DOI: 10.1155/2017/5137317
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    1. Clifford Lynch, 2008. "How do your data grow?," Nature, Nature, vol. 455(7209), pages 28-29, September.
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    Cited by:

    1. José García & Paola Moraga & Matias Valenzuela & Hernan Pinto, 2020. "A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem," Mathematics, MDPI, vol. 8(4), pages 1-22, April.
    2. José García & Victor Yepes & José V. Martí, 2020. "A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem," Mathematics, MDPI, vol. 8(4), pages 1-22, April.

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