Journal of
Public Health and Epidemiology

  • Abbreviation: J. Public Health Epidemiol.
  • Language: English
  • ISSN: 2141-2316
  • DOI: 10.5897/JPHE
  • Start Year: 2009
  • Published Articles: 539

Full Length Research Paper

High-accuracy detection of malaria mosquito habitats using drone-based multispectral imagery and Artificial Intelligence (AI) algorithms in an agro-village peri-urban pastureland intervention site (Akonyibedo) in Unyama Sub–County, Gulu District, Northern Uganda

Mona Minakshi
  • Mona Minakshi
  • Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA.
  • Google Scholar
Tanvir Bhuiyan
  • Tanvir Bhuiyan
  • Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA.
  • Google Scholar
Sherzod Kariev
  • Sherzod Kariev
  • Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA.
  • Google Scholar
Martha Kaddumukasa
  • Martha Kaddumukasa
  • Department of Parasitology and Entomology, Makerere University P. O. Box 7062, Kampala, Uganda.
  • Google Scholar
Denis Loum
  • Denis Loum
  • Nwoya District, Local Government, Nwoya, Uganda.
  • Google Scholar
Nathanael B. Stanley
  • Nathanael B. Stanley
  • College of Public Health, University of South Florida, Tampa, FL, USA.
  • Google Scholar
Sriram Chellappan
  • Sriram Chellappan
  • Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA.
  • Google Scholar
Peace Habomugisha
  • Peace Habomugisha
  • The Carter Center, Plot 15 Bombo Road, Kampala, Uganda.
  • Google Scholar
David W. Oguttu
  • David W. Oguttu
  • Vector Control Division, Plot 6, Loudrel Road, Kampala Ministry of Health, Uganda.
  • Google Scholar
Benjamin G. Jacob
  • Benjamin G. Jacob
  • Nwoya District, Local Government, Nwoya, Uganda.
  • Google Scholar


  •  Received: 12 February 2020
  •  Accepted: 27 May 2020
  •  Published: 31 July 2020

Abstract

The purpose of this research is to design, deploy and validate artificial intelligence (AI) algorithms operating on drone videos to enable a real time methodology for optimizing predictive mapping  unknown, geographic locations (henceforth, geolocation)  of potential, seasonal, Anopheles (gambiae, and funestus)  larval habitats in an agro-village, epi-entomological, intervention site (Akonyibedo village) in Gulu District, Northern Uganda. Formulae are developed for classifying the drone swath, capture point, land cover in Akonyibedo agro-village. An AI algorithm is designed for constructing a smartphone application (app) in order to enable automatic detection of potential larval habitats from drone videos. The aim of this work is to enable scaling up to larger intervention sites (e.g., district level, sub-county) and then throughout entire Uganda. We demonstrate how capture point, stratifiable, drone swath coverage in Akonyibedo village can be accomplished employing temporal series of re-centered, real time, imaged, Anopheline, larval habitat, seasonal, map projections. We also define a remote methodology for detecting unknown, georeferenceable, capture point geolocations of potential, seasonal, breeding sites employing multispectral, wavelength, signature, reflux emissivities in a drone spectral library. Our results show that high-resolution  drone  imagery when processed employing state of the art AI algorithms can discriminate a profile of water bodies where Anopheles mosquitoes are most likely to breed (overall ground truth accuracy of 100%). Live, high definition, Anopheline larval habitat signature maps can be generated in real-time drone AI app on a smartphone or Apple device while the image is being captured or larvicidal application is taking place.

Key words: Anopheles, larval habitat, drones, artificial intelligence (AI), malaria, Uganda.