Please use this identifier to cite or link to this item: https://doi.org/10.3390/app7010051
Title: Vision-based perception and classification of mosquitoes using support vector machine
Authors: Fuchida, M
Pathmakumar, T
Mohan, R.E
Tan, N 
Nakamura, A
Issue Date: 2017
Citation: Fuchida, M, Pathmakumar, T, Mohan, R.E, Tan, N, Nakamura, A (2017). Vision-based perception and classification of mosquitoes using support vector machine. Applied Sciences (Switzerland) 7 (1) : 51. ScholarBank@NUS Repository. https://doi.org/10.3390/app7010051
Rights: Attribution 4.0 International
Abstract: The need for a novel automated mosquito perception and classification method is becoming increasingly essential in recent years, with steeply increasing number of mosquito-borne diseases and associated casualties. There exist remote sensing and GIS-based methods for mapping potential mosquito inhabitants and locations that are prone to mosquito-borne diseases, but these methods generally do not account for species-wise identification of mosquitoes in closed-perimeter regions. Traditional methods for mosquito classification involve highly manual processes requiring tedious sample collection and supervised laboratory analysis. In this research work, we present the design and experimental validation of an automated vision-based mosquito classification module that can deploy in closed-perimeter mosquito inhabitants. The module is capable of identifying mosquitoes from other bugs such as bees and flies by extracting the morphological features, followed by support vector machine-based classification. In addition, this paper presents the results of three variants of support vector machine classifier in the context of mosquito classification problem. This vision-based approach to the mosquito classification problem presents an efficient alternative to the conventional methods for mosquito surveillance, mapping and sample image collection. Experimental results involving classification between mosquitoes and a predefined set of other bugs using multiple classification strategies demonstrate the efficacy and validity of the proposed approach with a maximum recall of 98%. © 2016 by the authors.
Source Title: Applied Sciences (Switzerland)
URI: https://scholarbank.nus.edu.sg/handle/10635/178737
ISSN: 20763417
DOI: 10.3390/app7010051
Rights: Attribution 4.0 International
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