Application of insect songs in monitoring population density level of Locusta migratoria migratoria (Orthoptera: Acrididae)

Abstract

Background: The locusts Locusta migratoria migratoria (Orthoptera: Acrididae) is the most destructive agricultural pests worldwide, the population and distribution of L. migratoria migratoria growing rapidly in recent years. It is crucial to find a green, economical way to monitor this insect's population for effective control tactics. In this study, acoustic samples were recorded and analyzed under three different density levels of Asian migratory locust L. migratoria migratoria. Results: The results showed that the songs of L. migratoria migratoria had a very stable acoustic feature in time domains; then, we used duration of pulse as a tool for identifying and counting the numbers of pulse to classify the population size. After removing the background noises, an automatic density classification and monitoring system was established based on the backpropagation (BP) neural network. The field sample test showed that the accuracy of the density level recognition reached 96.67%. Conclusions: The results indicated that the calling songs of insects could be an effective character to distinguish population density level of locust plagues, and it could be potentially used as a green and environmental protection solution in monitoring the dynamics of locust plagues and other acoustic agriculture pests.