Skip to main content
Log in

Machine vision for low-cost remote control of mosquitoes by power laser

  • Original Research Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

In this paper, we present an innovative and effective method for remote monitoring of mosquitoes and their neutralization. We explain in detail how we leverage modern advances in neural networks to use a powerful laser to neutralize mosquitoes. The paper presented the experimental low-cost prototype for mosquito control, which uses a powerful laser to thermally neutralize the mosquitoes. The developed device is controlled by a single-board computer based on the neural network. The paper demonstrated experimental research for mosquito neutralization during which, to maximize approximation to natural conditions, simulation of various working conditions was conducted. The manuscript showed that a low-cost device can be used to kill mosquitoes with a powerful laser.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Fernandes, J., Moise, I., Maranto, G., Beier, J.: Revamping mosquito-borne disease control to tackle future threats. Trends Parasitol. 34(5), 359–368 (2018)

    Article  Google Scholar 

  2. Fouet, C., Kamdem, C.: Integrated mosquito management: is precision control a luxury or necessity? Trends Parasitol. 35(1), 85–95 (2019)

    Article  Google Scholar 

  3. Schwab, S., Stone, C., Fonseca, D., Fefferman, N.: The importance of being urgent: the impact of surveillance target and scale on mosquito-borne disease control. Epidemics 23, 55–63 (2018)

    Article  Google Scholar 

  4. Vijayakumar, V., Malathi, D., Subramaniyaswamy, V., Saravanan, P., Logesh, R.: Fog computing-based intelligent healthcare system for the detection and prevention of mosquito-borne diseases. Comput. Hum. Behav. (2018). https://doi.org/10.1016/j.chb.2018.12.009

    Article  Google Scholar 

  5. Orozco, B., Windebank, T.: Mosquito detection with neural networks: the buzz of deep learning. (2017). arXiv:1705.05180v1 [stat.ML]

  6. Alam, J., Guoqing, H., Chen, C.: Characteristics analysis and detection algorithm of mosquitoes. TelkomnikaIndones. J. Electr. Eng. 12(7), 5368–5378 (2014)

    Google Scholar 

  7. Li, Y., Chan, H., Sinka, M.: Mosquito detection with low-cost smartphones: data acquisition for malaria research. (2017). arXiv:1711.06346v3 [stat.ML]

  8. Fuchida, M., Pathmakumar, T., Mohan, R.: Vision-based perception and classification of mosquitoes using support vector machine. Appl. Sci. 7(1), 51 (2017). https://doi.org/10.3390/app7010051

    Article  Google Scholar 

  9. Alam, J., Guoqing, H., Mojahidul, I.: Study of mosquito detection and position tracking algorithm. Automatic moth detection from trap images for pest. (2016). arXiv:1602.07383v1 [cs.CV]

  10. Khalifa, A., Alouani, I., Mahjoub, M., Amara, N.: Pedestrian detection using a moving camera: a novel framework for foreground detection. Cogn. Syst. Res. 60, 77–96 (2020)

    Article  Google Scholar 

  11. Sezer, B., Apaydin, H., Bilge, G., Boyaci, I.: Coffee arabica adulteration: detection of wheat, corn and chickpea. Food Chem. 264, 142–148 (2018)

    Article  Google Scholar 

  12. Wang, L., Geng, X., Ma, X.: Ridesharing car detection by transfer learning. Artif. Intell. 273, 1–18 (2019)

    Article  Google Scholar 

  13. Deng, Y., Liu, F., Chen, J., Su, G.: Mean shift tracker with chaotic artificial bee colony and space variant resolution. Optik 125(16), 4572–4577 (2014)

    Article  Google Scholar 

  14. Mullen, E.R., Rutschman, P., Pegram, N., Patt, J.M., John, J., Adamczyk, J.J.: Laser system for identification, tracking, and control of flying insects. Opt. Express 24, 11828–11838 (2016)

    Article  Google Scholar 

  15. Keller, M.D., Norton, B.J., Farrar, D.J., et al.: Optical tracking and laser-induced mortality of insects during flight. Sci. Rep. 10, 14795 (2020). https://doi.org/10.1038/s41598-020-71824-y

    Article  Google Scholar 

  16. Floreano, D., Zufferey, J.: Insect vision: a few tricks to regulate flight altitude. Curr. Biol. 20(19), 847–849 (2010)

    Article  Google Scholar 

  17. Nemec, D., Hrubos, M., Gregor, M., Bubenikova, E.: Visual localization and identification of vehicles inside a parking house. Proced. Eng. 192, 632–637 (2017)

    Article  Google Scholar 

  18. Bowen, M.: The sensory physiology of host seeking behavior in mosquitoes. Annu. Rev. Entomol. 36, 139–158 (1991)

    Article  Google Scholar 

  19. Killeen, F., Smith, A.: Exploring the contributions of bed nets, cattle, insecticides and excitorepellency to malaria control: a deterministic model of mosquito host-seeking behavior and mortality. Trans. R. Soc. Trop. Med. Hyg. 101, 867–880 (2007)

    Article  Google Scholar 

  20. Service, M.: Effects of wind on the behavior and distribution of mosquitoes and blackies. Int. J. Biometeorol. 24, 347–353 (1980)

    Article  Google Scholar 

  21. Cortez, R., Foppa, I.: a spatial model of mosquito host-seeking behavior. PLoSComput. Biol. (2012). https://doi.org/10.1371/journal.pcbi.1002500

    Article  Google Scholar 

  22. Banga, K., Kotwaliwale, N., Mohapatra, M.: Techniques for insect detection in stored food grains: an overview. Food Control 94, 167–176 (2018)

    Article  Google Scholar 

  23. Liu, H., Chahl, J.: A multispectral machine vision system for invertebrate detection on green leaves. Comput. Electron. Agric. 150, 279–288 (2018)

    Article  Google Scholar 

  24. Okamoto, H., Murakami, M., Kataoka, T., Shibata, Y.: Machine vision for detecting insects in hole of raspberry fruit. IFAC Proc. 46(4), 350–354 (2013)

    Article  Google Scholar 

  25. Gibson, G., Warren, B., Ian, J.: Humming in tune: sex and species recognition by mosquitoes on the wing. J. Assoc. Res. Otolaryngol. 11(4), 527–540 (2010)

    Article  Google Scholar 

  26. Raman, D., Gerhardt, R., Wilkerson, J.: Detecting insect flight sounds in the field: implications for acoustical counting of mosquitoes. Trans. ASABE 50(4), 1481–1485 (2007). https://doi.org/10.13031/2013.23606

    Article  Google Scholar 

  27. Fernandes, M., Cordeiro, W., Recamonde-Mendoza, M.: Detecting Aedes aegypti mosquitoes through audio classification with convolutional neural networks. Comput. Biol. Med. (2020). https://doi.org/10.1016/j.compbiomed.2020.104152

    Article  Google Scholar 

  28. Mukundarajan, H., Hol, F., Castillo, E., Newby, C., Prakash, M.: Using mobile phones as acoustic sensors for high-throughput mosquito surveillance. eLife 2017(6), e27854 (2017). https://doi.org/10.7554/eLife.27854

    Article  Google Scholar 

  29. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. (2017). arXiv:1703.06870 [cs.CV]

  30. He, K., Gkioxari, H., Dollar, P., Girshick, R.: Mask R-CNN. (2018). arXiv:1703.06870v3 [cs.CV]

Download references

Acknowledgements

Sergei Petrovskii (University of Leicester) is appreciated for his comments.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rakhmatulin Ildar.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ildar, R. Machine vision for low-cost remote control of mosquitoes by power laser. J Real-Time Image Proc 18, 2027–2036 (2021). https://doi.org/10.1007/s11554-021-01079-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-021-01079-x

Keywords

Navigation