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Learning to Count Mosquitoes for the Sterile Insect Technique

Authors:

Dilip Krishnan,

Daniel Newburger,

D. SculleyAuthors Info & Claims

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 1943 - 1949

https://doi.org/10.1145/3097983.3098204

Published: 13 August 2017 Publication History

Abstract

Mosquito-borne illnesses such as dengue, chikungunya, and Zika are major global health problems, which are not yet addressable with vaccines and must be countered by reducing mosquito populations. The Sterile Insect Technique (SIT) is a promising alternative to pesticides; however, effective SIT relies on minimal releases of female insects. This paper describes a multi-objective convolutional neural net to significantly streamline the process of counting male and female mosquitoes released from a SIT factory and provides a statistical basis for verifying strict contamination rate limits from these counts despite measurement noise. These results are a promising indication that such methods may dramatically reduce the cost of effective SIT methods in practice.

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Cited By

Saradopoulos IPotamitis IKonstantaras AEliopoulos PNtalampiras SRigakis I(2023)Image-Based Insect Counting Embedded in E-Traps That Learn without Manual Image Annotation and Self-Dispose Captured InsectsInformation10.3390/info1405026714:5(267)Online publication date: 30-Apr-2023
https://doi.org/10.3390/info14050267
Knolhoff LOvercash J(2023)Resistance to genetic controlInsect Resistance Management10.1016/B978-0-12-823787-8.00009-X(299-327)Online publication date: 2023
https://doi.org/10.1016/B978-0-12-823787-8.00009-X
Saradopoulos IPotamitis INtalampiras SKonstantaras AAntonidakis E(2022)Edge Computing for Vision-Based, Urban-Insects Traps in the Context of Smart CitiesSensors10.3390/s2205200622:5(2006)Online publication date: 4-Mar-2022
https://doi.org/10.3390/s22052006
Show More Cited By

Index Terms

Learning to Count Mosquitoes for the Sterile Insect Technique
1. Applied computing
  1. Life and medical sciences
    1. Computational biology
      1. Imaging
  2. Physical sciences and engineering
    1. Mathematics and statistics

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Published In

cover image ACM Conferences

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2017

2240 pages

ISBN:9781450348874

DOI:10.1145/3097983

General Chairs:
Stan Matwin
Dalhousie University
,
Shipeng Yu
LinkedIn
,
Faisal Farooq
IBM

Copyright © 2017 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 13 August 2017

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KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 13 - 17, 2017

NS, Halifax, Canada

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KDD '17 Paper Acceptance Rate 64 of 748 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Saradopoulos IPotamitis IKonstantaras AEliopoulos PNtalampiras SRigakis I(2023)Image-Based Insect Counting Embedded in E-Traps That Learn without Manual Image Annotation and Self-Dispose Captured InsectsInformation10.3390/info1405026714:5(267)Online publication date: 30-Apr-2023
https://doi.org/10.3390/info14050267
Knolhoff LOvercash J(2023)Resistance to genetic controlInsect Resistance Management10.1016/B978-0-12-823787-8.00009-X(299-327)Online publication date: 2023
https://doi.org/10.1016/B978-0-12-823787-8.00009-X
Saradopoulos IPotamitis INtalampiras SKonstantaras AAntonidakis E(2022)Edge Computing for Vision-Based, Urban-Insects Traps in the Context of Smart CitiesSensors10.3390/s2205200622:5(2006)Online publication date: 4-Mar-2022
https://doi.org/10.3390/s22052006
Vorsino AXi Z(2022)A mass rearing cost calculator for the control of Culex quinquefasciatus in Hawaiʻi using the incompatible insect techniqueParasites & Vectors10.1186/s13071-022-05522-115:1Online publication date: 5-Dec-2022
https://doi.org/10.1186/s13071-022-05522-1

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