Community-based mosquito surveillance: an automatic mosquito-on-human-skin recognition system with a deep learning algorithm

Ong, Song-Quan and Gomesh Nair and Umi Kalsom Yusof and Hamdan Ahmad (2022) Community-based mosquito surveillance: an automatic mosquito-on-human-skin recognition system with a deep learning algorithm. Pest Management Science: formerly Pesticide Science. ISSN 1526-498X

Text Abstract.pdf Download (37kB)

Abstract

Public community engagement is crucial for mosquito surveillance programs. To support community participation, one of the approaches is assisting the public in recognizing the mosquitoes that carry pathogens. Therefore, this study aims to build an automatic recognition system to identify mosquitos at the public community level. We construct a customized image dataset consisting of three mosquito species in either damaged or un-damaged body conditions. To distinguish the mosquito in harsh conditions, we explore two state-of-the-art deep learning (DL) architectures: (i) a freezing convolutional base, with partial trainable weights, and (ii) training the entire model with most of the trainable weights. We project a weighted feature map on different layers of the model to visualize the morphological region used by the model in classification and compared it with the morphological key used by the expert.

Item Type:	Article
Keyword:	Deep convolutional neural network , Expert system ,
Subjects:	Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science > QA76.75-76.765 Computer software
Department:	INSTITUTE > Institute for Tropical Biology and Conservation
Depositing User:	DG MASNIAH AHMAD -
Date Deposited:	26 Sep 2022 08:37
Last Modified:	26 Sep 2022 08:37
URI:	https://eprints.ums.edu.my/id/eprint/34158

Actions (login required)

View Item