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International Workshop on Soft Computing Models in Industrial and Environmental Applications

SOCO 2020: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020) pp 199–206Cite as

Machine Learning in Classification of the Wax Structure of Breathing Openings on Leaves Affected by Air Pollution

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1268))

Abstract

Texture analysis and classification of image components belong to common problems of the interdisciplinary area of digital signal and image processing. The paper is devoted to the pattern matrix construction using features evaluated by the discrete Fourier transform (DFT) or the discrete wavelet transform (DWT) using the relative power in selected frequency bands or scale levels, respectively. Image features are then used to recognize groups of similar pattern vectors by self-organizing neural networks forming a mathematical tool for cluster analysis. Further classification methods including the decision tree, support vector machine, nearest neighbour method and neural networks are then applied for construction of specific models and evaluation of their accuracy and cross validation errors. The proposed algorithm is applied for analysis of given microscopic images representing wax structures covering breathing openings on leaves affected by environmental pollution in different locations. The classification accuracy depends upon the method used and it is higher than 92% for all experiments.

The work has been supported by the research grant No. LTAIN19007 Development of Advanced Computational Algorithms for Evaluating Post-surgery Rehabilitation.

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Author information

Authors and Affiliations

  1. University of Chemistry and Technology, 166 28, Prague 6, Czech Republic

    Aleš Procházka, Martina Mudrová, Pavel Cejnar & Jan Mareš

  2. Czech Technical University, 160 00, Prague 6, Czech Republic

    Aleš Procházka

Authors
  1. Aleš Procházka
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  2. Martina Mudrová
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  3. Pavel Cejnar
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  4. Jan Mareš
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Corresponding author

Correspondence to Aleš Procházka .

Editor information

Editors and Affiliations

  1. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Álvaro Herrero

  2. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Carlos Cambra

  3. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Daniel Urda

  4. Technological Institute of Castilla y León, Burgos, Spain

    Javier Sedano

  5. Department of Industrial Engineering, University of A Coruña, La Coruña, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Procházka, A., Mudrová, M., Cejnar, P., Mareš, J. (2021). Machine Learning in Classification of the Wax Structure of Breathing Openings on Leaves Affected by Air Pollution. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_19

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