TY - JOUR
T1 - Mobile sensing in aedes aegypti larva detection with biological feature extraction
AU - Yuana, Dia Bitari Mei
AU - Sesulihatien, Wahjoe Tjatur
AU - Basuki, Achmad
AU - Harsono, Tri
AU - Alimudin, Akhmad
AU - Rohmah, Etik Ainun
N1 - Funding Information:
The authors would like to thank Ministry of Education Indonesia for supporting this research through University Flagship Research (PTUPT) funding.
Publisher Copyright:
© 2020, Institute of Advanced Engineering and Science. All rights reserved.
PY - 2020/8
Y1 - 2020/8
N2 - According to WHO, Dengue fever is the most critical and most rapidly mosquito-borne disease in the world over 50 years. Currently, the presence and detection of Aedes aegypti larvae (dengue-mosquitoes vector’s) are only quantified by human perception. In large-scale data, we need to automate the process of larvae detection and classification as much as possible. This paper introduces the new method to automate Aedes larvae. We use Culex larva for comparison. This method consists of data acquisition of recorded motion video, spatial movement patterns, and image statistical classification. The results show a significant difference between the biological movements of Aedes aegypti and Culex under the same environmental conditions. In 50 videos consisting of 25 Aedes larvae videos and 25 Culex larvae videos, the accuracy was 84%.
AB - According to WHO, Dengue fever is the most critical and most rapidly mosquito-borne disease in the world over 50 years. Currently, the presence and detection of Aedes aegypti larvae (dengue-mosquitoes vector’s) are only quantified by human perception. In large-scale data, we need to automate the process of larvae detection and classification as much as possible. This paper introduces the new method to automate Aedes larvae. We use Culex larva for comparison. This method consists of data acquisition of recorded motion video, spatial movement patterns, and image statistical classification. The results show a significant difference between the biological movements of Aedes aegypti and Culex under the same environmental conditions. In 50 videos consisting of 25 Aedes larvae videos and 25 Culex larvae videos, the accuracy was 84%.
KW - Aedes aegypti larva
KW - Biological feature extraction
KW - Detection
KW - Mobile sensing
UR - http://www.scopus.com/inward/record.url?scp=85085607977&partnerID=8YFLogxK
U2 - 10.11591/eei.v9i4.1993
DO - 10.11591/eei.v9i4.1993
M3 - Article
AN - SCOPUS:85085607977
SN - 2089-3191
VL - 9
SP - 1454
EP - 1460
JO - Bulletin of Electrical Engineering and Informatics
JF - Bulletin of Electrical Engineering and Informatics
IS - 4
ER -
