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Appl. Sci. 2017, 7(1), 51; doi:10.3390/app7010051

Vision-Based Perception and Classification of Mosquitoes Using Support Vector Machine

1
Department of Robotics and Mechatronics, Tokyo Denki University, Tokyo 120-8551, Japan
2
SUTD-JTC I 3 Centre, Singapore University of Technology and Design, Singapore 487372, Singapore
3
Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore 487372, Singapore
4
Department of Biomedical Engineering, National University of Singapore, Singapore 117583, Singapore
*
Author to whom correspondence should be addressed.
Academic Editor: Antonio Fern√°ndez-Caballero
Received: 3 October 2016 / Revised: 14 December 2016 / Accepted: 27 December 2016 / Published: 5 January 2017
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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%. View Full-Text
Keywords: mosquito classification; support vector machine; feature extraction; computer vision; automated mosquito surveillance mosquito classification; support vector machine; feature extraction; computer vision; automated mosquito surveillance
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Fuchida, M.; Pathmakumar, T.; Mohan, R.E.; Tan, N.; Nakamura, A. Vision-Based Perception and Classification of Mosquitoes Using Support Vector Machine. Appl. Sci. 2017, 7, 51.

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