Acoustic detection of the wood borer, Semanotus bifasciatus, as an early monitoring technology

BACKGROUND Semanotus bifasciatus Motschulsky (Coleoptera: Cerambycidae) is one of the most destructive wood-boring pests of Platycladus trees in East Asia, threatening the protection of antique cypresses and urban ecological safety. Early identification of Semanotus bifasciatus attacks can help forest managers mitigate the infestation before it turns into an outbreak. Acoustic detection technology is a non-destructive and continuous monitoring method with the potential to early identify and accurately evaluate the wood-boring damage. However, few studies have focused on the detection timing and corresponding acoustic features. In this study, we employed a manipulated insect infestation experiment to identify time windows in which early instar Semanotus bifasciatus larvae are most actively boring and feeding within logs and to identify acoustic features that distinguish larval sounds from typical background noise. RESULTS The Semanotus bifasciatus larvae produced sounds most frequently between 13:00 and 20:00 while sounds were detectable from the first to the third instar during the larval growth stage, indicating a suitable time window for early detection. The stepwise regression (SR) model was optimal for detecting the larval instar [coefficient of determination (R-2) = 0.71, root mean squared error of prediction (RMSEp) = 0.42, and relative percent deviation (RPD) = 3.38] while the best model for predicting larval population size was the partial least squares regression (PLSR) model (R-2 = 0.97, RMSEp = 61.96, and RPD = 28.87). CONCLUSION This study developed an acoustic method for identifying the early attack of Semanotus bifasciatus (including detection time window, feature variables and models for larval instar prediction and population size estimation). This technology integrated with internet of things (IoT) framework can be of value in developing an automated monitoring system for forest wood borer, and provide necessary guidance for integrated pest management (IPM). (c) 2022 Society of Chemical Industry.

Automated summary

This study developed an acoustic method for identifying the early attack of Semanotus bifasciatus, including detection time window, feature variables, and models for larval instar prediction and population size estimation. The results showed that the larvae produced sounds most frequently between 13:00 and 20:00, indicating a suitable time window for early detection. The stepwise regression model was optimal for detecting the larval instar, while the partial least squares regression model was the best for predicting larval population size.