Acoustic Sensor Module for Mosquito Detection and Classification

• Main Authors: Bacabac, John Sebastian, Ramos, Kim, Guico, Ma. Leonora, Galicia, Jan Kevin A
• Format: text
• Published: Archīum Ateneo 2023
• Subjects: acoustic sensor; FFT; mosquito classification; Electrical and Computer Engineering; Engineering
• Online Access: https://archium.ateneo.edu/ecce-faculty-pubs/145; https://doi.org/10.1109/ICCCE58854.2023.10246103
• Institution: Ateneo De Manila University

Although there have been multiple studies related to the tracking of mosquito wingbeat frequencies, not much has been done yet on Philippine mosquitoes. Additionally, most of the current methods for tracking mosquitoes involve actively trapping mosquitoes and evaluating them in a separate laboratory. As a response, an acoustic sensor module was developed using an Arduino microprocessor to identify and classify mosquitoes. Mosquitoes were lured and zapped. The module took sound input using an omnidirectional microphone and a parabolic dish housed in a pipe. The Arduino used digital signal processing to decrease background noise, identify probable mosquito wingbeats, and categorize different mosquito species according to the frequency of the wingbeats. Audacity was used to create recordings for reference, along with manual checking. ThingSpeak, an internet platform, received classified data and provided real-time display and analysis. Real-time classifications from the module were shown, and a histogram showed how frequently identified mosquito wingbeat frequencies were distributed. This made it possible for users to keep an eye on and track the mosquito population in the region where the module was placed. The findings showed that UV light attracts mosquitoes more potently than yeast. However, it was mentioned that combining yeast and UV light as luring techniques would be interesting for further study. The monitoring of mosquito populations is made easier by the interface with ThingSpeak, which offers real-time data display.