Detecting and counting Aedes aegypti egg using iot-ovitrap with computer vision approach

Paper Details

Research Paper 11/07/2023
Views (692) Download (73)
current_issue_feature_image
publication_file

Detecting and counting Aedes aegypti egg using iot-ovitrap with computer vision approach

Arnel C. Fajardo
J. Bio. Env. Sci.23( 1), 62-67, July 2023.
Certificate: JBES 2023 [Generate Certificate]

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.

VIEWS 142

A. Joshi, C. Miller. 2021. Review of Machine Learning Techniques For Mosquito Control In Urban Environments, Ecol. Inform., Vol. 61, No.1, P. 101241, Doi: 10.1016/J.Ecoinf.2021.101241.

Al. Rapid Surveillance For Vector Presence (RSVP): Development of a Novel System For Detecting Aedes aegypti And Aedes Albopictus, Plos Negl. Borne Dis., 2020, Vol. 1, No. 1, P. 100014, DOI: 10.1016/J.Crpvbd.2021.100014.

Bandong S., Joelianto E. 2019. Counting of Aedes aegypti Eggs using Image Processing with Grid Search Parameter Optimization. ICSECC- International Conference on Sustainable Engineering and Creative Computing: New Idea, New Innovation, Proceedings, 293–298. https://doi.org/10.1109/ICSECC.2019.8907232

Cuevas E., Osuna V., Oliva, D. 2017). Template matching. Studies in Computational Intelligence. https://doi.org/10.1007/978-3-319-51109-2_4

Chaves, 2017. Modeling The Association Between Aedes aegypti Ovitrap Egg Counts, Multi-Scale Remotely Sensed Environmental Data And Arboviral Cases At Puntarenas, Costa Rica (2017–2018),” Curr. Res. Parasitol. Vector-“Doh Observes National Dengue Awareness Month, Leads The 2021 Asean Dengue Day Regional Forum, Department Of Health. Https://Doh.Gov.Ph/Press-Release/Dohobserves-National-Dengue-Awareness-Monthleads-The-2021-Asean-Dengue-Day-Regional-Forum

Dehshibi D., Masip A. 2021. Deep Convolutional Neural Network For Classification Of Aedes Albopictus Mosquitoes, IEEE Access, Vol. 9, Pp. 72681–72690. DOI: 10.1109/ACCESS.2021.3079700

Gumiran AC., Fajardo RP., Medina MS., Dao, BE. Aguinaldo. 2022. Aedes aegypti Egg Morphological Property And Attribute Determination Based On Computer Vision, Pp. 581–585, Sep. DOI: 10.1109/ICSIP55141.2022.9887255

Ghoshal A., Aspat A., Lemos E. 2021. OpenCV Image Processing for AI Pet Robot. International Journal of Applied Sciences and Smart Technologies. https://doi.org/10.24071/ijasst.v3i1.2765

Han Y. 2021. Reliable template matching for image detection in vision sensor systems. Sensors. https://doi.org/10.3390/s21248176

Scavuzzo M. 2018. Modeling Dengue Vector Population Using Remotely Sensed Data And Machine Learning, Acta Trop., Vol. 185, Pp. 167–175. Doi: 10.1016/J.Actatropica.2018.05.003

Santana C., Firmo A., Oliveira R., Buarque P., Alves, G., Lima R. 2019. Albopictus Eggs in Paddles from Ovitraps Using Deep Learning. 17(12), 1987–1994.

Shubham Mishra, Mrs. Versha Verma, Dr. Nikhat Akhtar, Shivam Chaturvedi, & Dr. Yusuf Perwej. 2022. An Intelligent Motion Detection Using OpenCV. International Journal of Scientific Research in Science, Engineering and Technology. https://doi.org/10.32628/ijsrset22925

Wijaya MC. 2022. Template Matching Using Improved Rotations Fourier Transform Method. International Journal of Electronics and Telecommunications. https://doi.org/10.24425/ijet.2022.143898

Santana LFM., Pedra GM., Pires MP. 2019. Using Computer Vision for Aedes aegypti Egg Detection. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2726-2731. https://doi.org/10.1109/IROS40897.2019.8968270

Yamashita R., Nishio M., Do RKG., Togashi K. 2018. Convolutional neural networks: An overview and application in radiology. In Insights into Imaging. https://doi.org/10.1007/s13244-018-0639-9