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Probabilistic Neural Network for the Automated Identification of the Harlequin Ladybird (Harmonia Axyridis)

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Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8271))

Abstract

This paper describes recent work in the UK to automate the identification of Harlequin ladybird species (Harmonia axyridis) using color images. The automation process involves image processing and the use of probabilistic neural network (PNN) as classifier, with an aim to reduce the number of color images to be examined by entomologists through pre-sorting the images into correct, questionable and incorrect species. Two major sets of features have been extracted: color and geometrical measurements. Experimental results revealed more than 75% class match for the identification of taxa with similar-colored spots.

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References

  1. UK Ladybird Survey, http://www.coleoptera.org.uk

  2. Ware, R.L., Majerus, M.E.: Intraguild Predation of Immature Stages of British and Japanese Coccinellids by the Invasive Ladybird Harmonia Axyridis. BioControl 53, 169–188 (2008)

    Article  Google Scholar 

  3. Majerus, M.E.N., Strawson, V., Roy, H.: The Potential Impacts of the Arrival of the Harlequin Ladybird, Harmonia Axyridis (Pallas) (Coleoptera:Coccinellidae). Ecological Entomology 31, 207–215 (2006) (in Britain)

    Google Scholar 

  4. UK Harlequin Survey, http://www.harlequin-survey.org/recognition_and_distinction.htm

  5. Hopkins, G.W., Freckleton, R.P.: Decline In the Numbers of Amateur and Professional Taxonomists: Implications for Conservation. Animal Conservation 5, 245–249 (2002)

    Article  Google Scholar 

  6. GIMP2, http://www.gimp.org/

  7. WEKA, http://www.cs.waikato.ac.nz/ml/weka/

  8. Mabbott, P. (ed.): Community Heritage Initiative. A colour key for identifying ladybirds in Leicester & Rutland, http://www.leics.gov.uk/celebrating_wildlife

  9. Southampton Natural History Society, Ladybirds of Southampton, http://sotonnhs.org/docs/LadybirdAll.pdf

  10. CIELAB colour models – Technical Guides, http://www.dba.med.sc.edu/price/irf/Adobe_tg/models/cielab.html

  11. Torres, L., Reutter, J.Y., Lorente, L.: The Importance of the Color Information in Face Recognition. In: International Conference on Image Procesing (ICIP 19999), vol. 3, pp. 627–631 (October 1999)

    Google Scholar 

  12. Yip, A., Sinha, P.: Role of Color in Face Recognition. In: MIT tech report (ai.mit.com) AI Memo 2001-035, pp. 2001–2035. Massachusetts Institute of Technology, Cambridge, USA (2001)

    Google Scholar 

  13. Vízhányó, T., Felföldí, J.: Enhancing Colour Differences in Images of Diseased Mushrooms. Computers and Electronics in Agriculture 26, 187–198 (2000)

    Article  Google Scholar 

  14. Bradley, A.: The Use of the Area Under the ROC Curve in the Evaluation of Machine Learning Algorithms. Pattern Recognition 30, 1145–1159 (1997)

    Article  Google Scholar 

  15. Fawcett, T.: An Introduction to ROC Analysis. Pattern Recognition Letters 27(8), 861–874 (2006)

    Article  MathSciNet  Google Scholar 

  16. Omid, M.: Design of an Expert System for Sorting Pistachio Nuts Through Decision Tree and Fuzzy Logic Classifier. Expert Systems with Applications 38, 4339–4347 (2011)

    Article  Google Scholar 

  17. Wolpert, D.H.: Stacked Generalization. Neural Networks 5, 241–259 (1992)

    Article  Google Scholar 

  18. Prechelt, L.: Automatic Early Stopping Using Cross Validation: Quantifying the Criteria. Neural Networks 11(4), 761–767 (1998)

    Article  Google Scholar 

  19. Kohavi, R.: A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1137–1143. Morgan Kaufmann, San Francisco (1995)

    Google Scholar 

  20. Wu, S.G., Bao, F.S., Xu, E.Y., Wang, Y.X., Chang, Y.F., Xiang, Q.L.: A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network. In: 2007 IEEE International Symposium on Signal Processing and Information Technology, Giza, December 15-18, pp. 11–16 (2007)

    Google Scholar 

  21. MathWorks documentation, http://www.mathworks.com/help/nnet/ug/probabilistic-neural-networks.html

  22. Hagan, M.T., Demuth, H.B., Beale, M.: Neural Network Design. PWS Publishing Company, Beijing (2002) ISBN 7-111-10841-8

    Google Scholar 

  23. Foody, G.M.: Thematic Mapping from Remotely Sensed Data with Neural Networks: MLP, RBF and PNN based Approaches. Journal of Geographical Systems 3, 217–232 (2001)

    Article  Google Scholar 

  24. Hong, X.: Probabilistic neural network (PNN), http://www.personal.reading.ac.uk/~sis01xh/teaching/CY2D2/Pattern3.pdf

  25. Ayob, M.Z., Chesmore, E.D.: Hybrid Feature Extractor for Harlequin Ladybird Identification Using Color Images. In: 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), San Diego, pp. 214–221 (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Universiti Kuala Lumpur British Malaysian Institute, Gombak, Selangor, Malaysia

    Mohd Zaki Ayob

  2. Department of Electronics, University of York, YO10 5DD, York, UK

    E. D. Chesmore

Authors
  1. Mohd Zaki Ayob
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  2. E. D. Chesmore
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Editor information

Editors and Affiliations

  1. Department of Applied Computer Science, University of Winnipeg, R3B 2E9, Winnipeg, Manitoba, Canada

    Sheela Ramanna

  2. Department of Mathematics and Computing Science, Saint Mary’s University, B3H 3C3, Halifax, Nova Scotia, Canada

    Pawan Lingras

  3. Faculty of Informatics, Mahasarakham University, Khamreang, 44150, Kantarawichai, Mahasarakham, Thailand

    Chattrakul Sombattheera

  4. Department of Computing, Curtin University, Perth, WA, Australia

    Aneesh Krishna

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© 2013 Springer-Verlag Berlin Heidelberg

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Ayob, M.Z., Chesmore, E.D. (2013). Probabilistic Neural Network for the Automated Identification of the Harlequin Ladybird (Harmonia Axyridis). In: Ramanna, S., Lingras, P., Sombattheera, C., Krishna, A. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2013. Lecture Notes in Computer Science(), vol 8271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44949-9_3

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  • DOI: https://doi.org/10.1007/978-3-642-44949-9_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-44948-2

  • Online ISBN: 978-3-642-44949-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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