TreeVibes: Modern Tools for Global Monitoring of Trees for Borers

Is there a wood-feeding insect inside a tree or wooden structure? We investigate severalways of how deep learning approaches can massively scan recordings of vibrations stemming fromprobed trees to infer their infestation state with wood-boring insects that feed and move insidewood. The recordings come from remotely controlled devices that sample the internal soundscapeof trees on a 24/7 basis and wirelessly transmit brief recordings of the registered vibrations to acloud server. We discuss the different sources of vibrations that can be picked up from trees inurban environments and how deep learning methods can focus on those originating from borers.Our goal is to match the problem of the accelerated—due to global trade and climate change—establishment of invasive xylophagus insects by increasing the capacity of inspection agencies. Weaim at introducing permanent, cost-effective, automatic monitoring of trees based on deep learningtechniques, in commodity entry points as well as in wild, urban and cultivated areas in order to effectlarge-scale, sustainable pest-risk analysis and management of wood boring insects such as thosefrom theCerambycidaefamily (longhorn beetles). Simple Summary:We demonstrate that remote, automatic monitoring of probed trees for borersat global scales is currently technologically feasible. Vibroacoustic recorders, one per tree, sampleon a prescheduled basis (e.g., 20 s per hour) short clips of the internal vibroacoustic soundscene oftrees. These clips are compressed and wirelessly transmitted on cloud services where deep learningnetworks screen those data and tag if a tree is infested or not. This approach allows us to integrateinformation over a large time span (from a single day to weeks) before reaching a decision on theinfestation state of the tree. We aim at automatizing inspection services against wood-boring insectsin commodity entry point, forests and tree cultivations and directing only ambiguous cases to ahuman observer.