An autonomous system of detecting and attracting leafhopper males using species- and sex-specific substrate borne vibrational signals

Abstract

In leafhoppers that are among the most important vectors of plant diseases, mate recognition and location are mediated exclusively by species- and sex-specific vibrational signals exchanged in precisely coordinated duets. These pests are currently managed primarily by insecticide treatments, however, current legislation and consumers’ concerns and demands require that the risks and impacts of pesticides be reduced. We present a proof-of-concept low-cost autonomous digital processing system (AS), capable of recognizing the male calls of the leafhopper Aphrodes bicincta “Dragonja” and generating female replies. Such a device could be used as a vibrational trap. We chose this species since its duet structure is complex, with the female replies having to appear in short (47–175ms) intervals between continuously repeated elements in the male call in order to trigger male searching behaviour. The AS male call recognition algorithm is based on linear prediction cepstral coefficient (LPCC) feature vectors and a multilayer perceptron classifier (MLP). To prevent the noise-based feature vectors from feeding into the classifier, a bandwidth-limited linear prediction call activity detector based on spectrum peak tracking was designed. We tested the efficiency of the AS in behavioural experiments with live males. The MLP classification method successfully classified vibrational calls of male A. bicincta “Dragonja” from background noise. The fast real time identification enabled a synchronized playback of female vibrational reply with latencies as short as 130ms. This mimicking of a duetting female by autonomous system also attracted the males to the source of the female reply. The AS is also a useful tool to enable further studies of vibrational duets that are needed to develop effective alternative control strategies.