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Advances in Self-Organizing Maps and Learning Vector Quantization pp 329–339Cite as

Prototype-Based Classification for Image Analysis and Its Application to Crop Disease Diagnosis

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 428))

Abstract

In this paper, provide an application of Learning Vector Quantization (LVQ)-based techniques for solving a real-world problem. We apply LVQ for automated diagnosis of crop disease in cassava plants using features extracted from images of plants’ leaves. The problem reduces to a five class problem in which we attempt to distinguish between a leaf from a health plant and leaves representing four different viral and bacterial diseases in cassava. We discuss the problem under additional constraints that the solution must easily be deployable on a mobile device with limited processing power. In this study we explore the right configuration of type of algorithm and type of features extracted from the leaves that optimally solves the problem. We apply different variations of LVQ and compare them with standard classification techniques (Naïve Bayes, SVM and KNN). Results point to a preference of color feature representations and LVQ-based algorithms.

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Notes

  1. 1.

    http://www.air.ug/mcrops/.

  2. 2.

    http://www.cs.rug.nl/~biehl/gmlvq.html.

  3. 3.

    http://scikit-learn.org/.

References

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Acknowledgments

The authors would like to thank the Dr. Titus Alicai and Dr. Chris Omongo of the Uganda National Crop Resources Research Institute (NaCRRI), for granting us permission to access disease and pest surveillance data and for supporting the annotation of the data. This work is carried out with support from the Bill and Melinda Gates Foundation under the PEARL 1: Automated survey technology and spatial modeling of viral crop disease in cassava project.

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Authors and Affiliations

  1. School of Computing & Informatics Technology, Makerere University, P.O. Box 7062, Kampala, Uganda

    Ernest Mwebaze

  2. Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, P.O. Box 407, 9700, Groningen, AK, The Netherlands

    Michael Biehl

Authors
  1. Ernest Mwebaze
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  2. Michael Biehl
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Corresponding author

Correspondence to Ernest Mwebaze .

Editor information

Editors and Affiliations

  1. Department of Statistics, Rice University, Houston, Texas, USA

    Erzsébet Merényi

  2. Department of Electrical and Computer En, Air Force Institute of Technology, Wright-Patterson AFB, Ohio, USA

    Michael J. Mendenhall

  3. Applied Physics, Rice University, Houston, Texas, USA

    Patrick O'Driscoll

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Mwebaze, E., Biehl, M. (2016). Prototype-Based Classification for Image Analysis and Its Application to Crop Disease Diagnosis. In: Merényi, E., Mendenhall, M., O'Driscoll, P. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 428. Springer, Cham. https://doi.org/10.1007/978-3-319-28518-4_29

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  • DOI: https://doi.org/10.1007/978-3-319-28518-4_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28517-7

  • Online ISBN: 978-3-319-28518-4

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