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Canola seed or not? Autoencoder-based Anomaly Detection in AgriculturalSeedProduction

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dc.contributor.authorKukushkin, Maksim
dc.contributor.authorEnders, Matthias
dc.contributor.authorKaschuba, Reinhard
dc.contributor.authorBogdan, Martin
dc.contributor.authorSchmid, Thomas
dc.contributor.editorKlein, Maike
dc.contributor.editorKrupka, Daniel
dc.contributor.editorWinter, Cornelia
dc.contributor.editorWohlgemuth, Volker
dc.date.accessioned2023-11-29T14:50:22Z
dc.date.available2023-11-29T14:50:22Z
dc.date.issued2023
dc.description.abstractAnalysing harvested seeds is a time-consuming task in the seed-producing industry. Automating this process has the potential to enhance and expedite agricultural seed production. In our study, we focus on differentiating Canola seeds from visually similar non-Canola seeds using computer vision techniques. Our approach utilises both RGB and hyperspectral images, captured by a specialised camera, to train separate autoencoder neural networks. By leveraging the high spatial resolution of RGB data and the high spectral resolution of hyperspectral data, we develop distinct models for Canola seed analysis, ensuring a comprehensive and robust assessment. The autoencoder networks are trained on a dataset of Canola seeds, allowing for the extraction of latent representations from both RGB and hyperspectral data. This enables efficient compression of input data and effective discrimination between Canola and non-Canola seeds. Our proposed approach demonstrates promising results in detecting non-Canola seeds in unseen test data.de
dc.identifier.doi10.18420/inf2023_169
dc.identifier.isbn978-3-88579-731-9
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43095
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2023 - Designing Futures: Zukünfte gestalten
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-337
dc.subjectanomaly detection
dc.subjectseed production
dc.subjecthyperspectral imaging
dc.subjectautoencoder
dc.titleCanola seed or not? Autoencoder-based Anomaly Detection in AgriculturalSeedProductionde
dc.typeText/Conference Paper
gi.citation.endPage1652
gi.citation.publisherPlaceBonn
gi.citation.startPage1645
gi.conference.date26.-29. September 2023
gi.conference.locationBerlin
gi.conference.sessiontitleÖkologische Nachhaltigkeit - Kolloquium Landwirtschaft der Zukunft - Ist KI ein wesentlicher Schlüssel zur nachhaltigeren Landwirtschaft?

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P337 - INFORMATIK 2023 - Designing Futures: Zukünfte gestalten