Design and implementation of an IoT-OL trap for community-based dengue early warning system
Paper Details
Design and implementation of an IoT-OL trap for community-based dengue early warning system
Abstract
Surveillance of mosquito populations is central to successful vector intervention and disease control. Conventional surveillance techniques of manual egg counts and field-based assessments are cumbersome, slow, and hard to scale for real-time monitoring applications. To overcome these limitations, the paper introduces the design, implementation, and deployment of an Internet of Things (IoT)-Based Orvicidal-Larvidal (OL) trap and Community Dengue Early Warning System (C-DEWS) to facilitate the automated detection, enumeration, and monitoring of Aedes aegypti eggs in Cauayan City, Isabela. A Raspberry Pi equipped with micro-camera lenses captures the images and in determining the object of interest. The Convolution Neural Network (CNN) associated with the device achieves an outstanding 99.5% success rate for detecting and counting Aedes aegypti eggs, ensuring a reliable system. In addition, all key indicators, such as environmental factors, sanitary practices, Aedes aegypti egg distribution per location, and integration of dengue cases from the City Health Office (CHO) determine and monitor the dengue outbreak. This work shows how smart mosquito surveillance systems emerge from integrating data and multiple networks with cloud management frameworks.
Akbaba CE, Polat A. 2022. Determination of appropriate thresholding method in segmentation stage in detecting breast cancer cells. Journal of Advanced Research in Natural and Applied Sciences 8(1), 54–62.
Aldosery A, Vasconcelos D, Ribeiro M, Nunes N, Kostkova P. 2022, October. Mosquito Ovitraps IoT Sensing System (MOISS): Internet of Things-based system for continuous, real-time and autonomous environment monitoring. In: 2022 IEEE 8th World Forum on Internet of Things (WF-IoT), IEEE, p. 1–8.
Balingit JC, Carvajal TM, Saito-Obata M, Gamboa M, Nicolasora AD, Sy AK, Watanabe K. 2020. Surveillance of dengue virus in individual Aedes aegypti mosquitoes collected concurrently with suspected human cases in Tarlac City, Philippines. Parasites & Vectors 13, 1–13.
Bellini P, Nesi P, Pantaleo G. 2022. IoT-enabled smart cities: A review of concepts, frameworks and key technologies. Applied Sciences 12(3), 1607.
Desiani A, Irmeilyana I, Cahyono ES, Yahdin S, Arhami M, Andrian I. 2022. Combination contrast stretching and adaptive thresholding for retinal blood vessel image. MATRIK: Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 22(1), 1–12.
Fajardo AC. 2023. Detecting and counting Aedes aegypti egg using IoT-ovitrap with computer vision approach. Journal of Biodiversity and Environmental Sciences 23(1), 62–67.
Gumiran CR, Fajardo AC, Medina RP, Dao MS, Aguinaldo BE. 2022. Aedes aegypti egg morphological property and attribute determination based on computer vision. In: 2022 7th International Conference on Signal and Image Processing (ICSIP), IEEE, p. 581–585.
Gumiran CR, Fajardo AC, Medina RP, Dao MS, Gumiran JM. 2023, January. Aedes aegypti egg classification model using support vector machine. In: 2023 15th International Conference on Computer Research and Development (ICCRD), IEEE, p. 111–116.
Isa I, Ishak AR, Dom NC, Mohamed Z, Anuar MA. 2019. An IoT-based ovitrap system applied for Aedes mosquito surveillance. International Journal of Engineering and Advanced Technology 9(1), 5752–5758.
Liu WL, Wang Y, Chen YX, Chen BY, Lin AYC, Dai ST, Liao LD. 2023. An IoT-based smart mosquito trap system embedded with real-time mosquito image processing by neural networks for mosquito surveillance. Frontiers in Bioengineering and Biotechnology 11, 1100968.
Malarvizhi B, Zehra A, Poonguzhali G. 2023. Dengue vector (Aedes aegypti) control in South Chennai using ovitraps through community participation.
Manoharan SN, Kumar KM, Vadivelan N. 2023. A novel CNN-TLSTM approach for dengue disease identification and prevention using IoT-fog cloud architecture. Neural Processing Letters 55(2), 1951–1973.
Mardiani E, Riswandi DI, Suprayitno D, Mudia H. 2024. Implementation of Internet of Things in the production process of MSMEs: Quality improvement and process control. Jurnal Informasi dan Teknologi, 310–316.
Naranjo-Alcazar J, Grau-Haro J, Zuccarello P, Almenar D, Lopez-Ballester J. 2024. Automatic counting and classification of mosquito eggs in field traps. arXiv preprint arXiv:2405.20656.
Ojo B, Nnajiofor C, Osawe E, Aghaunor CT. 2024. Smart urban infrastructure: Leveraging IoT for enhanced resilience to climate change. International Journal of Science and Research Archive 12(2), 1355–1364.
Prema K, Belinda CM. 2019. Smart farming: IoT-based plant leaf disease detection and prediction using deep neural network with image processing. International Journal of Innovative Technology and Exploring Engineering 8(9), 3081–3083.
Qureshi KN. 2018. New trends in Internet of Things, applications, challenges, and solutions. TELKOMNIKA (Telecommunication Computing Electronics and Control) 16(3), 1114–1119.
Sakata MK, Sato M, Sato MO, Watanabe T, Mitsuishi H, Hikitsuchi T, Minamoto T. 2022. Detection and persistence of environmental DNA (eDNA) of the different developmental stages of a vector mosquito, Culex pipiens pallens. PLOS One 17(8), e0272653.
Salam I, Arsin AA, Wahyu A, Birawida AB, Syam A, Mallongi A, Elisafitri R. 2021. Modeling dynamic system for prediction of dengue hemorrhagic fever in Maros district. Open Access Macedonian Journal of Medical Sciences 9(E), 901–905.
Salazar FV, Gimutao KA. 2018. The evolution of entomological research with focus on emerging and re-emerging mosquito-borne infections in the Philippines. Public Health: Emerging and Re-emerging Issues.
Tambo E, El Dessouky AG, Khater EI. 2019. Innovative preventive and resilience approaches against Aedes-linked vector-borne arboviral diseases threat and epidemics burden in Gulf Council countries. Oman Medical Journal 34(5), 391.
Vijayakumar V, Malathi D, Subramaniyaswamy V, Saravanan P, Logesh R. 2019. Fog computing-based intelligent healthcare system for the detection and prevention of mosquito-borne diseases. Computers in Human Behavior 100, 275–285.
Walther D, Kampen H. 2017. The citizen science project ‘Mueckenatlas’ helps monitor the distribution and spread of invasive mosquito species in Germany. Journal of Medical Entomology 54(6), 1790–1794.
Yao S, Wang T, Li J, Abdelzaher T. 2019, November. Stardust: A deep learning serving system in IoT: Demo abstract. In: Proceedings of the 17th Conference on Embedded Networked Sensor Systems, p. 402–403.
Betchie E. Aguinaldo, Alyssa Jeanne M. Sicam (2025), Design and implementation of an IoT-OL trap for community-based dengue early warning system; JBES, V26, N3, March, P81-87
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