MM4Drone: A Multi-spectral Image and mmWave Radar Approach for Identifying Mosquito Breeding Grounds via Aerial Drones

Mosquitoes spread diseases such as Dengue and Zika that affect a significant portion of the world population. One approach to hamper the spread of the diseases is to identify the mosquitoes' breeding places. Recent studies use drones to detect breeding sites, due to their low cost and flexibility. In this paper, we investigate the applicability of drone-based multi-spectral imagery and mmWave radios to discover breeding habitats. Our approach is based on the detection of water bodies. We introduce our Faster R-CNN-MSWD, an extended version of the Faster R-CNN object detection network, which can be used to identify water retention areas in both urban and rural settings using multi-spectral images. We also show promising results for estimating extreme shallow water depth using drone-based multi-spectral images. Further, we present an approach to detect water with mmWave radios from drones. Finally, we emphasize the importance of fusing the data of the two sensors and outline future research directions

Field Effectiveness of Drones to Identify Potential Aedes aegypti Breeding Sites in Household Environments from Tapachula, a Dengue-Endemic City in Southern Mexico

Aedes aegypti control programs require more sensitive tools in order to survey domestic and peridomestic larval habitats for dengue and other arbovirus prevention areas. As a consequence of the COVID-19 pandemic, field technicians have faced a new occupational hazard during their work activities in dengue surveillance and control. Safer strategies to monitor larval populations, in addition to minimum householder contact, are undoubtedly urgently needed. Drones can be part of the solution in urban and rural areas that are dengue-endemic. Throughout this study, the proportion of larvae breeding sites found in the roofs and backyards of houses were assessed using drone images. Concurrently, the traditional ground field technician’s surveillance was utilized to sample the same house groups. The results were analyzed in order to compare the effectiveness of both field surveillance approaches. Aerial images of 216 houses from El Vergel village in Tapachula, Chiapas, Mexico, at a height of 30 m, were obtained using a drone. Each household was sampled indoors and outdoors by vector control personnel targeting all the containers that potentially served as Aedes aegypti breeding sites. The main results were that the drone could find 1 container per 2.8 found by ground surveillance; however, containers that were inaccessible by technicians in roofs and backyards, such as plastic buckets and tubs, disposable plastic containers and flowerpots were more often detected by drones than traditional ground surveillance. This new technological approach would undoubtedly improve the surveillance of Aedes aegypti in household environments, and better vector control activities would therefore be achieved in dengue-endemic countries

UP4DREAM CAPACITY BUILDING PROJECT: UAS BASED MAPPING IN DEVELOPING COUNTRIES

UP4DREAM (UAV Photogrammetry for Developing Resilience and Educational Activities in Malawi) is a cooperative project cofounded by ISPRS between the Polytechnic University of Turin and the United Nations Children Fund (UNICEF) Malawi, with the support of two local Universities (Lilongwe University of Agriculture and Natural Resources, and Mzuzu University), and Agisoft LLC (for the use of their photogrammetry and computer vision software suite). Malawi is a flood-prone landlocked country constantly facing natural and health challenges, which prevent the country's sustainable socio-economic development. Frequent naturals shocks leave vulnerable communities food insecure. Moreover, Malawi suffers from high rates of HIV, as well as it has endemic malaria. The UP4DREAM project focuses on one of the drone project's critical priorities in Malawi (Imagery). It aims to start a capacity-building initiative in line with other mapping missions in developing countries, focusing on the realization and management of large-scale cartography (using GIS - Geographic Information Systems) and on the generation of 3D products based on the UAV-acquired data. The principal aim of UP4DREAM is to ensure that local institutions, universities, researchers, service companies, and manufacturers operating in the humanitarian drone corridor, established by UNICEF in 2017, will have the proper knowledge and understanding of the photogrammetry and spatial information best practices, to perform large-scale aerial data acquisition, processing, share and manage in the most efficient, cost-effective and scientifically rigorous way

Conditional trust:Community perceptions of drone use in malaria control in Zanzibar

Background The potential of drones to support public health interventions, such as malaria vector control, is beginning to be realised. Although permissions from civil aviation authorities are often needed for drone operations, the communities over which they fly tend to be ignored: How do affected communities perceive drones? Is drone deployment accepted by communities? How should communities be engaged? Methods An initiative in Zanzibar, United Republic of Tanzania is using drones to map malarial mosqutio breeding sites for targeting larval source management interventions. A community engagement framework was developed, based on participatory research, across three communities where drones will be deployed, to map local perceptions of drone use. Costs associated with this exercise were collated. Results A total of 778 participants took part in the study spanning a range of community and stakeholder groups. Overall there was a high level of acceptance and trust in drone use for public health research purposes. Despite this level of trust for drone operations this support was conditional: There was a strong desire for pre-deployment information across all stakeholder groups and regular updates of this information to be given about drone activities, as well as consent from community level governance. The cost of the perception study and resulting engagement strategy was US$24,411. Conclusions Mapping and responding to community perceptions should be a pre-requisite for drone activity in all public health applications and requires funding. The findings made in this study were used to design a community engagement plan providing a simple but effective means of building and maintaining trust and acceptability. We recommend this an essential investment

The application of drones for mosquito larval habitat identification in rural environments: a practical approach for malaria control?

Background Spatio-temporal trends in mosquito-borne diseases are driven by the locations and seasonality of larval habitat. One method of disease control is to decrease the mosquito population by modifying larval habitat, known as larval source management (LSM). In malaria control, LSM is currently considered impractical in rural areas due to perceived difficulties in identifying target areas. High resolution drone mapping is being considered as a practical solution to address this barrier. In this paper, the authors’ experiences of drone-led larval habitat identification in Malawi were used to assess the feasibility of this approach. Methods Drone mapping and larval surveys were conducted in Kasungu district, Malawi between 2018 and 2020. Water bodies and aquatic vegetation were identified in the imagery using manual methods and geographical object-based image analysis (GeoOBIA) and the performances of the classifications were compared. Further, observations were documented on the practical aspects of capturing drone imagery for informing malaria control including cost, time, computing, and skills requirements. Larval sampling sites were characterized by biotic factors visible in drone imagery and generalized linear mixed models were used to determine their association with larval presence. Results Imagery covering an area of 8.9 km2 across eight sites was captured. Larval habitat characteristics were successfully identified using GeoOBIA on images captured by a standard camera (median accuracy = 98%) with no notable improvement observed after incorporating data from a near-infrared sensor. This approach however required greater processing time and technical skills compared to manual identification. Larval samples captured from 326 sites confirmed that drone-captured characteristics, including aquatic vegetation presence and type, were significantly associated with larval presence. Conclusions This study demonstrates the potential for drone-acquired imagery to support mosquito larval habitat identification in rural, malaria-endemic areas, although technical challenges were identified which may hinder the scale up of this approach. Potential solutions have however been identified, including strengthening linkages with the flourishing drone industry in countries such as Malawi. Further consultations are therefore needed between experts in the fields of drones, image analysis and vector control are needed to develop more detailed guidance on how this technology can be most effectively exploited in malaria control

Sustainable control of infestations using image processing and modelling

A sustainable pest control system integrates automated pest detection and recognition to evaluate the pest density using image samples taken from habitats. Novel predator/prey modelling algorithms assess control requirements for the UAV system, which is designed to deliver measured quantities of naturally beneficial predators to combat pest infestations within economically acceptable timeframes. The integrated system will reduce the damaging effect of pests in an infested habitat to an economically acceptable level without the use of chemical pesticides. Plant pest recognition and detection is vital for food security, quality of life and a stable agricultural economy. The research utilises a combination of the k-means clustering algorithm and the correspondence filter to achieve pest detection and recognition. The detection is achieved by partitioning the data space into Voronoi cells, which tends to find clusters of comparable spatial extents, thereby separating the objects (pests) from the background (pest habitat). The detection is established by extracting the variant and distinctive attributes between the pest and its habitat (leaf, stem) and using the correspondence filter to identify the plant pests to obtain correlation peak values for the different datasets. The correspondence filter can achieve rotationally invariant recognition of pests for a full 360 degrees, which proves the effectiveness of the algorithm and provides a count of the number of pests in the image. A series of models has been produced that will permit an assessment of common pest infestation problems and estimate the number of predators that are required to control the problem within a time schedule. A UAV predator deployment system has been designed. The system is offered as a replacement for chemical pesticides to improve peoples’ health opportunities and the quality of food products

IDENTIFICAÇÃO DE SÍTIOS DE REPRODUÇÃO DE AEDES AEGYPTI COM AERONAVE REMOTAMENTE PILOTADA (ARP)

A drone and its flight accessories are called Remotely Piloted Aircraft System (RPAS - Remotely Piloted Aircraft System), being a tool with a wide range of applications in several areas. The research explored new possibilities for the use of RPAS with a focus on the diagnosis and monitoring of breeding sites for Aedes aegypti. For this, objects considered as potential breeding grounds for mosquito larvae were distributed in environments that allowed greater or lesser visual detection of targets (packages / containers) in four environments: soil covered with dry grass, exposed soil, soil covered with low grass. and soil covered with tall grass. We use RPAS, Phantom 4 Pro with an Ipad Mini 4 mobile device and the DJI GO program for flights. We fly over targets for photographic recording at four heights from the ground (20m, 30m, 60m and 80m). The visual detection of the targets was carried out by a group of 10 people called a jury. The Jury assessed the greater or lesser probability of target detection, depending on three variables: type of target, type of environment and height of aerial photography. Photographs taken at a height of 30 meters represented the largest number of targets identified (30% of the targets). The most identified targets were tires, pet bottles, cans of beer and cans of paint. The least identified were colored plastic canisters and beer bottles. The research helped to improve operational procedures for controlling and combating endemics and epidemics, which may identify possible mosquito breeding sites through RPA, monitoring areas of difficult access that pose a risk to people's physical integrity.Um drone e seus complementos de voo são denominados Sistema de Aeronave Remotamente Pilotada (RPAS - Remotely Piloted Aircraft System), sendo uma ferramenta com ampla gama de aplicações em diversas áreas. A pesquisa prospectou novas possibilidades de uso de RPAS com enfoque no diagnóstico e monitoramento de locais de reprodução de Aedes aegypti. Para isso, objetos considerados como potenciais criadouros de larvas de mosquito foram distribuídos em ambientes que permitiam maior ou menor detecção visual dos alvos (embalagens/recipientes) em quatro ambientes: solo coberto com gramínea seca, solo exposto, solo coberto com gramínea de porte baixo e solo coberto com gramínea de porte alto. Foi utilizado RPAS, Phantom 4 Pro com dispositivo móvel e o programa nativo da RPA para os voos. Sobrevoamos alvos para registro fotográfico em quatro alturas do solo (20m, 30m, 60m e 80m). A detecção visual dos alvos foi realizada por um grupo de 10 pessoas denominado júri. O Júri aferiu a maior ou menor probabilidade de detecção de alvos, em função de três variáveis: tipo de alvo, tipo de ambiente e altura de tomada da fotografia aérea. Fotografias obtidas a 30 metros de altura representaram o maior número de alvos identificados (30% dos alvos). Os alvos mais identificados foram pneu, garrafa PET, latas de cerveja e latas de tinta. Os menos identificados foram vasilhas plásticas coloridas e garrafas de cerveja. A pesquisa colaborou para o aperfeiçoamento de procedimentos operacionais de controle e combate a endemias e epidemias, que poderão identificar possíveis criadouros do mosquito por meio de RPA, monitorando áreas de difícil acesso que ofereçam risco a integridade física das pessoas. Palavras-chave: drone; geotecnologias; arboviroses; dengue.   Identification of reproduction sites of Aedes aegypti with remote pilot aircraft (ARP)   ABSTRACT: A drone and its flight accessories are called Remotely Piloted Aircraft System (RPAS - Remotely Piloted Aircraft System), being a tool with a wide range of applications in several areas. The research explored new possibilities for the use of RPAS with a focus on the diagnosis and monitoring of breeding sites for Aedes aegypti. For this, objects considered as potential breeding grounds for mosquito larvae were distributed in environments that allowed greater or lesser visual detection of targets (packages / containers) in four environments: soil covered with dry grass, exposed soil, soil covered with low grass. and soil covered with tall grass. Was used RPAS, Phantom 4 Pro with an Ipad Mini 4 mobile device and the DJI GO program for flights. We fly over targets for photographic recording at four heights from the ground (20m, 30m, 60m and 80m). The visual detection of the targets was carried out by a group of 10 people called a jury. The Jury assessed the greater or lesser probability of target detection, depending on three variables: type of target, type of environment and height of aerial photography. Photographs taken at a height of 30 meters represented the largest number of targets identified (30% of the targets). The most identified targets were tires, pet bottles, cans of beer and cans of paint. The least identified were colored plastic canisters and beer bottles. The research helped to improve operational procedures for controlling and combating endemics and epidemics, which may identify possible mosquito breeding sites through RPA, monitoring areas of difficult access that pose a risk to people's physical integrity. Keywords: drone; geotecnologies; arbovírus; dengue

A Lack of "Environmental Earth Data" at the Microhabitat Scale Impacts Efforts to Control Invasive Arthropods That Vector Pathogens

We currently live in an era of major global change that has led to the introduction and range expansion of numerous invasive species worldwide. In addition to the ecological and economic consequences associated with most invasive species, invasive arthropods that vector pathogens (IAVPs) to humans and animals pose substantial health risks. Species distribution models that are informed using environmental Earth data are frequently employed to predict the distribution of invasive species, and to advise targeted mitigation strategies. However, there are currently substantial mismatches in the temporal and spatial resolution of these data and the environmental contexts which affect IAVPs. Consequently, targeted actions to control invasive species or to prepare the population for possible disease outbreaks may lack efficacy. Here, we identify and discuss how the currently available environmental Earth data are lacking with respect to their applications in species distribution modeling, particularly when predicting the potential distribution of IAVPs at meaningful space-time scales. For example, we examine the issues related to interpolation of weather station data and the lack of microclimatic data relevant to the environment experienced by IAVPs. In addition, we suggest how these data gaps can be filled, including through the possible development of a dedicated open access database, where data from both remotely- and proximally-sensed sources can be stored, shared, and accessed

Louisiana Agriculture Winter, 2018

Louisiana’s agricultural and natural resources continue to be under attack from a wide variety of invasive species. Some are new to the state, while others have been here for a long time. This issue of Louisiana Agriculture highlights the broad range of research activities being conducted by LSU AgCenter scientists to understand how invasive species survive and thrive and to identify effective means of controlling or limiting their damage in the state