SpringerLink
Log in

Artificial neural network trained to identify mosquitoes in flight

  • Short Communication
  • Published:
Journal of Insect Behavior Aims and scope Submit manuscript

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Thailand)

Instant access to the full article PDF.

Institutional subscriptions

References

  • Cooper, J. W. (1981).Introduction to Pascal for Scientists, Wiley, New York.

    Google Scholar 

  • Farmery, M. J. (1982). The effect of air temperature on wingbeat frequency of naturally flying armyworm moth (Spodoptera exempta).Entomol. Exp. Appl. 32: 193–194.

    Google Scholar 

  • Greenbank, D. O., Schaefer, G. W., and Rainey, R. C. (1980). Spruce budworm (Lepidoptera: Tortricidae) moth flight and dispersal: new understanding from canopy observations, radar, and aircraft.Mem. Entomol. Soc. Can. 110.

  • Moore, A., Miller, J. R., Tabashnik, B. E., and Gage, S. H. (1986). Automated identification of flying insects by analysis of wingbeat frequencies.J. Econ. Entomol. 79: 1703–1706.

    Google Scholar 

  • Reed, S. C., Williams, C. M., and Chadwick, L. E. (1942). Frequency of wing-beat as a character for separating species races and geographic varieties ofDrosophila.Genetics 27: 349–361.

    Google Scholar 

  • Riley, J. R., Reynolds, D. R., and Farmery, M. J. (1983). Observations of the flight behavior of the armyworm moth,Spodoptera exempta, at an emergence site using radar and infra-red optical techniques.Ecol. Entomol. 8: 395–418.

    Google Scholar 

  • Rose, D. J. W., Page, W. W., Dewhurst, C. F., Riley, J. R., Reynolds, D. R., Pedgley, D. E., and Tucker, M. R. (1985). Downwind migration of the African armyworm moth,Spodoptera exempta, studied by mark-and-capture and by radar.Ecol. Entomol. 10: 299–313.

    Google Scholar 

  • Sawedal, L., and Hall, R. (1979). Flight tone as a taxonomic character in Chironomidae (Diptera).Entomol. Scand. Suppl. 10: 139–143.

    Google Scholar 

  • Schaefer, G. W., and Bent, G. A. (1984). An infra-red remote sensing system for the active detection and automatic determination of insect flight trajectories (IRADIT).Bull. Entomol. Res. 74: 261–278.

    Google Scholar 

  • Sotavalta, O. (1947). The flight-tone (wing-stroke frequency) of insects (Contributions to the problem of insect flight 1.).Acta Entomol. Fenn. 4: 1–114.

    Google Scholar 

  • Stanley, J. (1989).Introduction to Neural Networks, California Scientific Software, Sierra Madre.

    Google Scholar 

  • Unwin, D. M., and Corbet, S. A. (1984). Wingbeat frequency, temperature, and body size in bees and flies.Physiol. Entomol. 9: 115–121.

    Google Scholar 

  • Unwin, D. M., and Ellington, C. P. (1979). An optical tachometer for measurement of the wingbeat frequency of free-flying insects.J. Exp. Biol. 82: 377–378.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Entomology, CTAHR-Maui Research, University of Hawaii, P.O. Box 269, 96790, Hawaii

    Aubrey Moore

Authors
  1. Aubrey Moore
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Moore, A. Artificial neural network trained to identify mosquitoes in flight. J Insect Behav 4, 391–396 (1991). https://doi.org/10.1007/BF01048285

Download citation

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01048285

Key words

Search

Navigation