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Journal of Insect Behavior

, Volume 27, Issue 5, pp 657–677 | Cite as

Flying Insect Classification with Inexpensive Sensors

  • Yanping ChenEmail author
  • Adena Why
  • Gustavo Batista
  • Agenor Mafra-Neto
  • Eamonn Keogh
Article

Abstract

The ability to use inexpensive, noninvasive sensors to accurately classify flying insects would have significant implications for entomological research, and allow for the development of many useful applications in vector control for both medical and agricultural entomology. Given this, the last sixty years have seen many research efforts on this task. To date, however, none of this research has had a lasting impact. In this work, we explain this lack of progress. We attribute the stagnation on this problem to several factors, including the use of acoustic sensing devices, the overreliance on the single feature of wingbeat frequency, and the attempts to learn complex models with relatively little data. In contrast, we show that pseudo-acoustic optical sensors can produce vastly superior data, that we can exploit additional features, both intrinsic and extrinsic to the insect’s flight behavior, and that a Bayesian classification approach allows us to efficiently learn classification models that are very robust to overfitting. We demonstrate our findings with large scale experiments, as measured both by the number of insects and the number of species considered.

Keywords

Automate insect classification insect flight sound insect wingbeat Bayesian classifier flight activity circadian rhythm 

Notes

Acknowledgments

We would like to thank the Vodafone Americas Foundation, the Bill and Melinda Gates Foundation and São Paulo Research Foundation (FAPESP) for funding this research, and the many faculties from the Department of Entomology at UCR that offered advice and expertise.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Yanping Chen
    • 1
    Email author
  • Adena Why
    • 2
  • Gustavo Batista
    • 3
  • Agenor Mafra-Neto
    • 4
  • Eamonn Keogh
    • 5
  1. 1.Department of Computer Science & EngineeringUniversity of CaliforniaRiversideUSA
  2. 2.Department of EntomologyUniversity of CaliforniaRiversideUSA
  3. 3.University of São Paulo - USPSão PauloBrazil
  4. 4.ISCA TechnologiesRiversideUSA
  5. 5.Department of Computer Science & EngineeringUniversity of CaliforniaRiversideUSA

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