skip to main content
10.1145/2425836.2425891acmotherconferencesArticle/Chapter ViewAbstractPublication PagesivcnzConference Proceedingsconference-collections
poster

Counting insects in flight using image processing techniques

Authors Info & Claims
Published:26 November 2012Publication History

ABSTRACT

A method to monitor New Zealand's native bees using image processing technology is presented. Since most species are solitary ground nesting bees the number of active nests within an area can give a good estimate of the population of a community. The number of native bees in flight around plants can also provide valuable information about the overall health of a community and help to quantify their value in the ecosystem as keystone pollinators. On this basis, images of insects in flight have been collected across one season and the results compared with previous results of active nest counts. Open source software FIJI was used to pre-process and classify images. Accuracies were verified using data mining software WEKA. Performance evaluations showed the fast random forest classifier consistently returned fast, accurate results. Fine differences in images were discriminated that were otherwise impossible to identify with the naked eye and even when training data were unevenly distributed the classifier returned accuracies above 98%. The results are promising and while there are few alternatives to traditional methods, image processing for ecology can provide cost effective, standardized tools to help monitor the population and diversity of native bees in New Zealand.

References

  1. T. O'Toole, "Those Other Bees: Changing the Funding Culture," in Pollinating Bees - The Conservation Link Between Agriculture and Nature, P. Kevan and V. L. Imperatriz Fonseca, Eds., ed: Ministry of Environment, 2002, pp. 37--40.Google ScholarGoogle Scholar
  2. S. Shavit, A. Dafni and G. Ne'eman, "Competition between honeybees (Apis mellifera) and native solitary bees in the Mediterranean region of Israel - Implications for conservation," Israel Journal of Plant Sciences, vol. 57, pp. 171--183, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  3. Watson and Son. (2008). Manuka has multi-million dollar potential for the Far North. Available: http://www.watsonandson.co.nz/Google ScholarGoogle Scholar
  4. D. W. Roubik, "Ups and Downs in Pollinator Populations: When is there a Decline?," Ecology and Society, vol. 5(1), 2001.Google ScholarGoogle Scholar
  5. B. J. Donovan, Apoidea, Fauna of New Zealand vol. 57, 2007.Google ScholarGoogle Scholar
  6. P. J. D. Weeks and K. J. Gaston, "Image analysis, neural networks, and the taxonomic impediment to biodiversity studies," Biodiversity and Conservation, vol. 6, pp. 263--274, 1997.Google ScholarGoogle ScholarCross RefCross Ref
  7. S. Schroder, D. Wittmann, W. Drescher, V. Roth, V. Steinhage and A. B. Cremers, "The New Key to Bees: Automated Identification by Image Analysis of Wings," in Pollinating Bees - The Conservation Link Between Agriculture and Nature - Ministry of Environment, ed Brasília: Kevan P & Imperatriz Fonseca VL, 2002, pp. 209--216.Google ScholarGoogle Scholar
  8. K. J. Gaston and M. A. O'Neill, "Automated species identification-why not?," Philosophical Transactions of the Royal Society of London, vol. B, pp. 655--667, 2004.Google ScholarGoogle Scholar
  9. K. N. Russell, M. T. Do, J. C. Huff, N. I. Platnick and N. MacLeod, "Introducing Spida-Web: Wavelets, Neural Networks and Internet Accessibility in an Image-Based Automated Identification System"," in Automated Taxon Identification in Systematics: Theory, Approaches and Applications, N. MacLeod, Ed., ed: CRC Press, 2008, pp. 131--152.Google ScholarGoogle Scholar
  10. N. Nikolaou, P. Sampaziotis, M. Aplikioti, A. Drakos, I. Kirmitzoglou, N. Papamarkos and V. J. Promponas, "VeSTIS: A Versatile Semi- Automatic Taxon Identification System from Digital Images," in Proceedings of the International Congress, ed. Paris, 2010, pp. 231--236.Google ScholarGoogle Scholar
  11. M. Mayo and A. T. Watson, "Automatic species identification of live moths," Knowledge-Based Systems, vol. 20, pp. 195--202 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. M. Hall, F. Eibe, G. Holmes, B. Pfahringer, P. Reutemann and I. H. Witten, "The WEKA Data Mining Software: An Update," SIGKDD Explorations, vol. 11, pp. 10--18, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. N. H. Hart and L. Huang, "An image based approach to monitor New Zealand native bees," presented at the IEEE Conference on Robotics, Automation and Mechatronics (RAM), Qingdao, China, 2011.Google ScholarGoogle Scholar
  14. J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J.-Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak and A. Cardona, "Fiji: an open-source platform for biological-image analysis," Nature Methods, vol. 9, pp. 676--682, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  15. L. Breiman, "Random forests," Machine Learning, vol. 45, pp. 5--32, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. P. Johansson, "Small vessel detection in high quality optical satellite imagery -- using component tree image representation and random forest classification," Course TMS106, Statistical Image Analysis, Chalmers University of Technology, Gothenburg, 2011.Google ScholarGoogle Scholar
  17. F. Livingston, "Implementation of Breiman's random forest machine learning algorithm," in ECE591Q Machine Learning Conference, 2005.Google ScholarGoogle Scholar
  18. C. Strobl, A.-L. Boulesteix, A. Zeileis and T. Hothorn, "Bias in random forest variable importance measures: Illustrations, sources and a solution," BMC Bioinformatics, vol. 8, pp. 1--21, 2007/01/25 2007.Google ScholarGoogle ScholarCross RefCross Ref
  19. D. J. Flaspohler, "A technique for sampling flying insects," vol. v. 69, no. 2, p. 201--208, 1998.Google ScholarGoogle Scholar

Index Terms

  1. Counting insects in flight using image processing techniques

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      IVCNZ '12: Proceedings of the 27th Conference on Image and Vision Computing New Zealand
      November 2012
      547 pages
      ISBN:9781450314732
      DOI:10.1145/2425836

      Copyright © 2012 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 26 November 2012

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • poster

      Acceptance Rates

      Overall Acceptance Rate55of74submissions,74%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader