Mosquito vector monitoring system based on optical wingbeat classification

• A mosquito monitoring system using the wingbeat pattern was proposed.
• A robust optical sensing device for wingbeat profiling was developed.
• Three algorithms for mosquito classification were assessed using three species of living mosquitoes.
• The average classification accuracy of the proposed algorithm is above 85%.

We developed an automatic mosquito classification system which consists of an infrared recording device for profiling the wingbeat of the in-flight mosquito species and a machine learning model for classifying the gender, genus, and species of the incoming mosquitoes by the signatures of their wingbeats.The recording device is a set of infrared emitters and receivers, which are attached to the wall of an apparatus. When the winged subject enters the apparatus, its flapping wings block the infrared beam from the emitters intermittently such that the receivers convert the wingbeat to the electrical waveform. To classify the incoming subjects, we proposed a machine learning method, which is the Gaussian mixture model trained using the expectation-maximization algorithm (EM-GMM), and compared it with the previously proposed algorithms, including the artificial neuron network model (ANN) and the nearest neighbor model.To assess the performance of the system, we used the living male and female Aedes albopictus, Aedes aegypti and Culex quinquefasciatus. The results show that the accuracies of the proposed system are above 80% on identifying the gender and genus of the mosquitoes, with the precisions above 80% and 70%, respectively. The results also suggest that the EM-GMM algorithm outperforms the other two algorithms on the accuracy and precision of the classification of the classes of mosquitoes. In addition to the evaluation of the performance of the system, we also found that certain classes of mosquitoes share similar wingbeat characteristics, which implies that the distinctive wingbeat characteristics should be considered for the optimal accuracy of the classification of the insects of interest.