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Hybrid Algorithm of Convolutional Neural Networks and Vector Support Machines in Classification

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Advanced Research in Technologies, Information, Innovation and Sustainability (ARTIIS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1485))

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Abstract

Looking for the improvement of the classification, we propose a hybrid algorithm to identify the corn plant and the weed. With the aim of improving the fertilization and herbicide application processes. An efficient process can avoid wasted fertilizers and decrease subsoil contamination. The purpose is to identify the corn plant to specify the fertilizer application in an automated and precise way. Whereas, the identification of the weed allows to apply herbicides directly. In this work we propose a hybrid method with Convolutional Neural Networks (CNN) to extract characteristics from images and Vector Support Machines (SVM) for classification. We obtained effectiveness results, a percentage of 98%, being higher than those compared to the state of the art.

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Author information

Authors and Affiliations

  1. Universidad Autónoma del Estado de México (UAEMEX), Jardín Zumpango s/n, Fraccionamiento El Tejocote, Texcoco, Estado de México, Mexico

    Marcos Yamir Gómez Ramos, José Sergio Ruíz Castilla & Farid García Lamont

Authors
  1. Marcos Yamir Gómez Ramos
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  2. José Sergio Ruíz Castilla
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  3. Farid García Lamont
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Corresponding author

Correspondence to Marcos Yamir Gómez Ramos .

Editor information

Editors and Affiliations

  1. Universidad Estatal Península de Santa Elena, La Libertad, Ecuador

    Teresa Guarda

  2. Universidade do Minho, Guimarães, Portugal

    Filipe Portela

  3. Universidade do Minho, Guimarães, Portugal

    Manuel Filipe Santos

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Ramos, M.Y.G., Castilla, J.S.R., Lamont, F.G. (2021). Hybrid Algorithm of Convolutional Neural Networks and Vector Support Machines in Classification. In: Guarda, T., Portela, F., Santos, M.F. (eds) Advanced Research in Technologies, Information, Innovation and Sustainability. ARTIIS 2021. Communications in Computer and Information Science, vol 1485. Springer, Cham. https://doi.org/10.1007/978-3-030-90241-4_21

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  • DOI: https://doi.org/10.1007/978-3-030-90241-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90240-7

  • Online ISBN: 978-3-030-90241-4

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