Mosquito vector-borne diseases such as malaria and dengue constitute some of the most serious public health burdens in tropical and sub-tropical countries. Effective targeting of disease control efforts requires accurate estimates of mosquito vector population density. The traditional, and still most common, approach to this involves the use of traps along with manual counting and classification of mosquito species. This process is costly and labor-intensive, which hinders its widespread use. In this paper we present a software pipeline for detection and classification of mosquito wingbeat sounds. Since our target platform is low-cost IoT devices, we explore the tradeoff between accuracy and efficiency. When a fast binary mosquito detector precedes the classifier, we can reduce the computational demand compared with use of the classifier alone by a factor of 10. While the accuracy of traditional machine learning model drops from 90% to 64% when reducing the sample rate from 96 kHz to 8 kHz, our deep-learning models maintain an accuracy of almost 83%, even when additionally reducing the bit depth from 24 to 16 bits. We conclude that the combination of an efficient mosquito detector with a convolutional neural network provides for an excellent trade-off between accuracy and efficiency to detect, classify and count mosquitoes.

Mosquitoes are vectors of pathogens that cause diseases like malaria, dengue fever, yellow fever, chikungunya and Zika. For mosquito control it is crucial to understand their hearing system, as mosquitoes’ courting behavior is mostly auditory. Many nonlinear characteristics of the mosquito hearing organ have been observed through behavioral studies and neural measurements. These enable mosquitoes to detect and synchronize to other mosquitoes. Many hypotheses concerning the role of the flagellum and the fibrillae of the antenna in mosquito hearing have been made, and neural processes have been considered as the origin of the nonlinearities. In this study we introduce a geometric model based on the morphology of the mosquito antenna base. The model produces many of the observed nonlinear characteristics, providing evidence that the base of the antenna plays a crucial role in mosquito hearing. Even without neural processing, the antenna response to sound produces behaviorally relevant cues that can inform about the presence, location, and sex of other mosquitoes.

Counting mosquitoes in the wild is a crucial capability for monitoring, prediction, and control of vector-borne diseases. Current approaches are mainly manual, where specially designed mosquito traps or ovitraps are placed in areas of interest and recovered the next day. The counting itself is performed in an entomological laboratory, where individual mosquitoes are classified into species and counted. This process is costly, slow and inefficient. At the same time, mosquito counting is most relevant in tropical and sub-tropical countries, where mosquitoes spread deadly diseases like malaria, yellow fever and dengue fever. Many countries in these regions have relatively weak public health systems and so cannot support large-scale vector counting efforts. In this paper, we present a system architecture and a prototype to count mosquitoes in the wild with an Internet of Things approach. A sensor board is developed to gather audio data, and models are developed to detect, classify, and count mosquito species. Here, we present our prototype and an extensive background study of classifying mosquitoes based on sound recordings and some preliminary results and discussion.