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An Artificial Intelligence of Things based Method for Early Detection of Bark Beetle Infested Trees

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dc.contributor.authorKnebel, Peter
dc.contributor.authorAppold, Christian
dc.contributor.authorGuldner, Achim
dc.contributor.authorHorbach, Marius
dc.contributor.authorJuncker, Yasmin
dc.contributor.authorMüller, Simon
dc.contributor.authorMatheis, Alfons
dc.contributor.editorWohlgemuth, Volker
dc.contributor.editorNaumann, Stefan
dc.contributor.editorArndt, Hans-Knud
dc.contributor.editorBehrens, Grit
dc.contributor.editorHöb, Maximilian
dc.date.accessioned2022-09-19T09:20:51Z
dc.date.available2022-09-19T09:20:51Z
dc.date.issued2022
dc.description.abstractBark 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.en
dc.identifier.isbn978-3-88579-722-7
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/39407
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofEnviroInfo 2022
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-328
dc.subjectSoundscape Ecology
dc.subjectBark beetle detection
dc.subjectIoT sensors
dc.subjectAIoT-based evaluation
dc.titleAn Artificial Intelligence of Things based Method for Early Detection of Bark Beetle Infested Treesen
dc.typeText/Conference Paper
gi.citation.publisherPlaceBonn
gi.citation.startPage111
gi.conference.date26.-30- September 2022
gi.conference.locationHamburg

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P328 - EnviroInfo 2022
Environmental Informatics 2022