Knebel, PeterAppold, ChristianGuldner, AchimHorbach, MariusJuncker, YasminMüller, SimonMatheis, AlfonsWohlgemuth, VolkerNaumann, StefanArndt, Hans-KnudBehrens, GritHöb, Maximilian2022-09-192022-09-192022978-3-88579-722-7https://dl.gi.de/handle/20.500.12116/39407Bark beetles, like the European Spruce Bark Beetle (Ips typographus), are inherent partsof a forest ecosystem. However, with favorable conditions, they can multiply quickly and infest vastamounts of trees and cause their extinction. Therefore, it is important for forest officials and rangers ofe. g. a national park, to monitor the population of the beetles and the infested trees. There are severalways to approach this, but they are often costly and time-consuming. Therefore, we design and test abark beetle early warning system with AI-based data analysis: Audio data, data on pheromones andinformation for a drought stress assessment of the affected trees are to be collected and used as a basisfor the analysis. The aim is to devise a micro-controller-based sensor system that detects the infestationof a tree as early as possible and warns the forest officials, e. g. via a message on their cell phone.enSoundscape EcologyBark beetle detectionIoT sensorsAIoT-based evaluationText/Conference Paper1617-5468