Mosquitoes Species Classification Using Acoustic Features of Wing Beats

Bilal, Muhammad; ur-Rehman, Ata; Razzaq, Saqlain

doi:10.1109/comtech57708.2023.10165480

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…Mosquitoes produce unique wing beat frequencies, which can be used to identify different mosquito species 8,9 . The use of acoustic monitoring has the potential to be a cost-effective and non-invasive method of mosquito control 10 . The use of acoustic wing beats for mosquito species classification has several advantages over traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Accurate Acoustic Based Deep Learning Model for Mosquitoes Species Classification

Bilal,

Rehman,

Rafique

et al. 2023

Preprint

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Despite global health advancements, mosquito borne diseases are still life threating and prevalent, with dengue and malaria contributing to thousands of deaths each year. However, the responsibility for transmitting these specific diseases does not lie with all mosquito species globally. Detecting mosquito species from their wingbeats acoustic data can be very effective, however, it is a challenging task to accurately distinguish specific mosquito species based on their wingbeat patterns. Our acoustic-based deep learning model for mosquito species classification uses the combination of sinc layer, one dimensional convolutional layer and long short-term memory (LSTM) layer to learn features from raw audio signals and capture the temporal dependencies between them strikingly outperforming existing methods. We achieve test accuracy of 93.59% precision of 0.9363, Recall of 0.9311 and F1 Score of 0.9335. Our model has significant potential for low-cost, non-invasive, and high-throughput identification of mosquito species, which can play a role on the prevention and control of mosquito-borne diseases. By utilizing the unique wingbeat patterns of mosquitoes, our model enables early identification and monitoring of disease-carrying species. This early detection allows for timely implementation of targeted interventions, focusing resources on the specific mosquito species responsible for disease transmission in a particular region. Additionally, acoustic-based models enhance surveillance systems by providing real-time tracking and monitoring of mosquito populations. This data-driven approach facilitates rapid response to potential disease outbreaks, enabling public health authorities to implement effective control measures and reduce the burden of mosquito-borne diseases on communities.

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Section: Introductionmentioning

confidence: 99%