Thielert, BonitoMenz, PatrickGötte, GesaRunne, MiriamMichel, MarkusWagner, SylviaJarausch, WolfgangWarnemünde, SebastianKlein, MaikeKrupka, DanielWinter, CorneliaGergeleit, MartinMartin, Ludger2024-10-212024-10-212024978-3-88579-746-3https://dl.gi.de/handle/20.500.12116/45082German 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.enhyperspectralmobile laboratoryphytoplasmaflavescence doréemachine learningvinebois noirdisease detectionText/Conference Paper10.18420/inf2024_1101617-5468