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PhenoTruckAI: On-Site Hyperspectral Measurement for Distinction of Quarantine Grapevine Disease “flavescence dorée” and non-Quarantine Disease “bois noir” in a Mobile Laboratory

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dc.contributor.authorThielert, Bonito
dc.contributor.authorMenz, Patrick
dc.contributor.authorGötte, Gesa
dc.contributor.authorRunne, Miriam
dc.contributor.authorMichel, Markus
dc.contributor.authorWagner, Sylvia
dc.contributor.authorJarausch, Wolfgang
dc.contributor.authorWarnemünde, Sebastian
dc.contributor.editorKlein, Maike
dc.contributor.editorKrupka, Daniel
dc.contributor.editorWinter, Cornelia
dc.contributor.editorGergeleit, Martin
dc.contributor.editorMartin, Ludger
dc.date.accessioned2024-10-21T18:24:12Z
dc.date.available2024-10-21T18:24:12Z
dc.date.issued2024
dc.description.abstractGerman wine growing regions are threatened by the expected occurrence of the quarantine phytoplasma disease “flavescence dorée (FD)”. As a fast and reliable extension for FD monitoring in the field, hyperspectral imaging using machine learning (ML) based data processing has been assessed for its potential to detect FD and to distinguish it from the less damaging phytoplasma disease “bois noir (BN)”. As FD is not yet present in Germany, the study has been conducted in Northern Italy in a mobile lab. The best models reached a high phytoplasma detection accuracy of 94.9% and 97.8% for the visible to near-infrared (VNIR) and the short-wavelength spectral range (SWIR), respectively. The distinction accuracy to BN reached 79.9% (VNIR) and 79.3% (SWIR). Both, the practicability performing hyperspectral measurements in a sovereign mobile lab and the applicability of hyperspectral sensor systems using ML for detection and distinction of FD and BN phytoplasmas has been shown.en
dc.identifier.doi10.18420/inf2024_110
dc.identifier.isbn978-3-88579-746-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45082
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2024
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-352
dc.subjecthyperspectral
dc.subjectmobile laboratory
dc.subjectphytoplasma
dc.subjectflavescence dorée
dc.subjectmachine learning
dc.subjectvine
dc.subjectbois noir
dc.subjectdisease detection
dc.titlePhenoTruckAI: On-Site Hyperspectral Measurement for Distinction of Quarantine Grapevine Disease “flavescence dorée” and non-Quarantine Disease “bois noir” in a Mobile Laboratoryen
dc.typeText/Conference Paper
gi.citation.endPage1260
gi.citation.publisherPlaceBonn
gi.citation.startPage1253
gi.conference.date24.-26. September 2024
gi.conference.locationWiesbaden
gi.conference.sessiontitleKoLaZ-24-Kolloquium Landwirtschaft der Zukunft 2024: Digitale Souveränität in der Landwirtschaft, der Lebensmittelkette und dem ländlichen Raum: Trotz, mit oder durch KI?

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