Smart Traps, Smarter Surveillance: Using IoT and Computer Vision to Detect Aedes Eggs | InformativeBD

Arnel C. Fajardo, from the different institute of Philippines. wrote a Research Article about, Smart Traps, Smarter Surveillance: Using IoT and Computer Vision to Detect Aedes Eggs. Entitled, Detecting and counting Aedes aegypti egg using iot-ovitrap with computer vision approach. This research paper published by the Journal of Biodiversity and Environmental Sciences | JBES. an open access scholarly research journal on Biodiversity. under the affiliation of the International Network For Natural Sciences| INNSpub. an open access multidisciplinary research journal publisher.

Abstract

This study focuses on the critical investigation of the propagation of the Aedes aegypti mosquito, a vector responsible for transmitting various diseases. The significance lies in understanding its spread due to its potential to disseminate illnesses. Employing laboratory-engineered traps called IoT-Ovitraps, the research aims to construct maps illustrating egg deposition within a community. To achieve this, images featuring the objects of interest, namely Aedes aegypti eggs, are captured using a Raspberry Pi equipped with a micro lens. The primary objective centers on the detection and enumeration of Aedes aegypti eggs within the confines of Cauayan City. To ascertain the most effective methodology for achieving accurate egg quantification, the study employs three distinct models. These models are subsequently compared for their precision in estimating egg quantities present in the ovitraps. Among the models assessed, the convolutional neural network (CNN) emerges as the superior option in terms of efficiency and dependability. Remarkably, the CNN model attains an impressive accuracy rate of 99.5% in accurately detecting and enumerating Aedes aegypti eggs. This outcome underscores the potential of advanced machine learning techniques in contributing to effective disease vector monitoring and control strategies, highlighting the promising role of neural networks in tackling the challenges posed by disease-carrying mosquitoes.

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Introduction

An epidemic of dengue disease is spread by infected Aedes aegypti. When eggs were clumped with comparable things, disease carriers were difficult to locate. This illness has frequently endangered public health and even resulted in fatalities. To identify disease carriers at an early stage, several approaches and technologies, including computer vision and deep learning, had been explored. Aedes aegypti eggs can also be branded and mixed in with similar objects while still being recognized by a computer vision algorithm. Some studies (Bandong & Joelianto, 2019) (Santana et al., 2019) includes in their investigations are determined the size, shape, and color are the most important characteristics of eggs.

In the Philippines, the threat of dengue fever remains a significant public health concern due to the active transmission of the disease by the infected Aedes aegypti mosquito. The country has experienced recurrent outbreaks of dengue, posing substantial health risks and mortality rates. In response to the challenges posed by these disease carriers, innovative strategies and technological advancements have been explored to enhance early detection and control.

In order to effectively detect and count Aedes aegypti eggs in a particular location in Cauyan City, Isabela Province, Philippines. The researcher created a hardware which is called IoT-OviTrap that composed of Raspberry pi with micro lenses that is place over the black container with paddle or so called “DOST OL trap”. The hardware was being set with a time interval for capturing and automatically processed the capture images and then sends the result into the webserver.

The motivation of this study is to perform different computer vision models such as (OpenCV, Template Matching and Neural Network) to be package into the hardware and compare its results.

This study lies in its potential to address a pressing public health concern related to the spread of dengue disease through infected Aedes aegypti mosquitoes.

Dengue outbreaks have posed a significant threat to public health, leading to substantial morbidity and mortality. The challenge of detecting disease carriers becomes more complex when Aedes aegypti eggs are clustered among similar objects, making their identification and control arduous. Early detection and monitoring of these disease vectors are critical to implementing timely control measures.

By exploring innovative approaches such as computer vision and deep learning, this study aims to contribute to the development of effective tools and techniques for the detection and counting of Aedes aegypti eggs. The creation of the IoT-OviTrap hardware, which integrates Raspberry Pi with micro lenses, offers a practical solution for real-time image capture and analysis. The utilization of computer vision models like OpenCV, Template Matching, and Neural Network within the hardware holds the potential to enhance the accuracy and efficiency of egg detection.

Ultimately, the study's findings have the potential to inform and guide public health interventions, aiding in the early identification and management of Aedes aegypti populations. This research could contribute significantly to the field of disease vector control and monitoring, offering insights into innovative technological solutions that can be applied in other regions facing similar challenges.

The emergence and propagation of dengue disease carried by Aedes aegypti mosquitoes have engendered substantial health risks, necessitating effective control measures. However, the challenge of identifying these disease vectors is compounded when their eggs are clustered alongside similar objects. Traditional detection methods often fall short in accurately locating and quantifying these eggs. In response, this study seeks to address the problem of efficient and reliable Aedes aegypti egg detection within a specific location in Cauayan City, Isabela Province, Philippines.

Despite previous research efforts, there is a need for advanced technological solutions that combine hardware and computer vision techniques to enhance the accuracy and speed of egg detection. The development of the IoT-OviTrap, encompassing Raspberry Pi with micro lenses and integrated computer vision models, seeks to provide a holistic solution to this problem. The comparative analysis of computer vision models—OpenCV, Template Matching, and Neural Network—within the hardware framework further enhances the potential for accurate and early identification of Aedes aegypti eggs.

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