The use of sensors designed to identify acoustics (e.g., microphones) emitted by flying insects for classification dates back to 1945 where Kahn et al. \cite{kahn1945recording} recorded the sounds produced by mosquitoes and try to distinguish the species based on audio features. Since then, many researchers have tried to improve upon the idea throughout the decades \cite{belton1979flight,mankin2006field,raman2007detecting}. However, all these works are still faced with the limitation of sounds to attenuate according to an inverse squared law. This property poses a dilemma among researchers between selecting a more sensitive microphone, which then results to recording of unnecessary ambient noises, or opting to instead work with very unnatural environments to force insect collection \cite{belton1979flight,moore2002automated}, which then the results are hard to generalize to insects in natural conditions.

To address the issues posed by acoustic sensors, an approach was proposed by Batista et al. \cite{batista2011towards} to use inexpensive laser systems to perform the classification task. This approach has since then been studied extensively for potential improvements \cite{chen2014flying,mullen2016laser}. The sensor bases its classification of the insects on wing-beat frequencies, which are known to achieve high accuracies for identifying mosquitoes. Using lasers, the wing beat of an insect’s flight can be captured even from a few meters away. However, the catch is to ensure that the insect must pass through the laser exactly once to guard against multiplicity. Also with limited frequency ranges, it is not implausible to consider that wing-beat frequencies of different insects are not unique. Hence to address the latter issue, current laser systems being studied are now considering the potential significance of using circadian rhythm and time stamping the collection.