Towards Automatic Classification on Flying Insects using Inexpensive Sensors

Abstract—Insects are intimately connected to human life and wellbeing, in both positive and negative senses. While it is estimated that insects pollinate at least two-thirds of the all food consumed by humans, malaria, a disease transmitted by the female mosquito of the Anopheles genus, kills approximately one million people per year. Due to the importance of insects to humans, researchers have developed an arsenal of mechanical, chemical, biological and educational tools to help mitigate insects harmful effects, and to enhance their beneficial effects. However, the efficiency of such tools depends on knowing the time and location of migrations/infestations/population as early as possible. Insect detection and counting is typically performed by means of traps, usually “sticky traps”, which are regularly collected and manually analyzed. The main problem is that this procedure is expensive in terms of materials and human time, and creates a lag between the time the trap is placed and inspected. This lag may only be a week, but in the case of say, mosquitoes or sand flies, this can be more than half their adult life span. We are developing an inexpensive optical sensor that uses a laser beam to detect, count and ultimately classify flying insects from distance. Our objective is to use classification techniques to provide accurate real-time counts of disease vectors down to the species/sex level. This information can be used by public health workers, government and non-government organizations to plan the optimal intervention strategies in the face of limited resources. In this work, we present some preliminary results of our research, conducted with three insect species. We show that using our simple sensor we can accurately classify these species using their wing-beat frequency as feature. We further discuss how we can augment the sensor with other sources of information in order to scale our ideas to classify a larger number of species. Keywords-Mosquito classification, inexpensive optical sen-sors, time series data I