English

Minakshi Mona,Habomugisha Peace,W Oguttu David,Chellappan Sriram,Loum Denis,Kaddumukasa Martha,Kariev Sherzod,G Jacob Benjamin,Bhuiyan Tanvir,B Stanley Nathanael

doi:10.5897/jphe2020.1213

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.  

Highlights

Real-time, geospatial, predictive mapping from remote sensing can identify mosquito larval habitats in a large area that is difficult or near impossible to survey using conventional ground-based techniques
Capture point, wavelength, color, surface reflux as a combination of fully specified RGB values in ArcGIS, color fringing artifacts may be avoided while preserving sharp edges of georeferenced, gridded, land use land cover (LULC), habitat boundaries and their eco-geographic, seasonal classified, feature attributes (Jacob et al, 2011) .This real time, cartographic methodology can aid in forecasting unknown, hyper-productive, aquatic and dry, Anopheles, larval, breeding sites using archived, time series, Unmanned Aerial Vehicle (UAV), frequency-oriented, sample datasets
The overall goal of this project is to develop a customized smartphone app that could identify the LULC geolocation of unknown, eco-georeferenceable, Anopheles, larval habitat capture points in agro-village, pasturelands from processed, real time, RGB, video images employing RGB signatures obtained from a drone aircraft seasonally flown in Akonyibedo village in Gulu District, Northern Uganda

Summary

Introduction

Real-time, geospatial, predictive mapping from remote sensing can identify mosquito larval habitats in a large area that is difficult or near impossible to survey using conventional ground-based techniques. RGB datasets constructed in ArcGIS provides an index case for how the reflectivity of vector arthropod, larval, habitat changes through seasons (Jacob et al, 2015). Capture point, wavelength, color, surface reflux as a combination of fully specified RGB values in ArcGIS, color fringing artifacts may be avoided while preserving sharp edges of georeferenced, gridded, land use land cover (LULC), habitat boundaries and their eco-geographic, seasonal classified, feature attributes (Jacob et al, 2011) .This real time, cartographic methodology can aid in forecasting unknown, hyper-productive, aquatic and dry, Anopheles, larval, breeding sites using archived, time series, UAV, frequency-oriented, sample datasets. For remote identification of mosquito, vector arthropod, larval habitat and their respective, RGB, time series, capture point LULC signatures, the first step is often to construct a discrete tessellation of the region (Jacob et al, 2019)

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