Much of the damage caused by stem-boring larvae, specifically the "red palm weevil (Rhynchophorus ferrugineus)", could be mitigated through "early detection and treatment" of infestations. "Acoustic technology" has the potential to enable this early detection by identifying the short, high-frequency sound impulses produced by larvae as they feed and move within palm tree trunks. However, distinguishing these signals from background noise and wind-induced tapping remains a significant challenge. This paper explores a multidisciplinary approach combining "entomological behavioural analysis", "advanced signal processing", and "edge-aided automated machine learning" to provide reliable detection in agricultural environments. By processing signals at the source, these systems can overcome traditional barriers like instrumentation costs and training needs.
P et al. (2026) studied this question.