A Deep Learning-Based Automatic Mosquito Sensing and Control System for Urban Mosquito Habitats

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Mosquito control is important as mosquitoes are extremely harmful pests that spread various infectious diseases. In this research, we present the preliminary results of an automated system that detects the presence of mosquitoes via image processing using multiple deep learning networks. The Fully Convolutional Network (FCN) and neural network-based regression demonstrated an accuracy of 84%. Meanwhile, the single image classifier demonstrated an accuracy of only 52%. The overall processing time also decreased from 4.64 to 2.47 s compared to the conventional classifying network. After detection, a larvicide made from toxic protein crystals of the Bacillus thuringiensis serotype israelensis bacteria was injected into static water to stop the proliferation of mosquitoes. This system demonstrates a higher efficiency than hunting adult mosquitos while avoiding damage to other insects.
Publisher
MDPI
Issue Date
2019-06
Language
English
Article Type
Article
Citation

SENSORS, v.19, no.12, pp.2785

ISSN
1424-8220
DOI
10.3390/s19122785
URI
http://hdl.handle.net/10203/263676
Appears in Collection
EE-Journal Papers(저널논문)
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