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Methods and techniques for plant and weed detection creating a database for future computer vision systems in weed control and practical implementations: Insights from the KIdetect project, funded by the BMEL

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dc.contributor.authorNoori, Faryal
dc.contributor.authorHopf, Lucas
dc.contributor.authorFlierl, Philipp
dc.contributor.authorZimmermann, Alexander
dc.contributor.authorNiedermeier, Michael
dc.contributor.authorHolst, Gerhard
dc.contributor.authorSchmailzl, Anton
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.abstractThis paper explores weed detection methodologies in vertical farming systems using Short Wave Infrared (SWIR) and Visual (VIS) camera technology, alongside computer vision techniques and Artificial Intelligence (AI). It investigates pixel-matching techniques for stereo-image processing to enhance imaging accuracy and reliability in agriculture. Classical methods like SIFT FLANN Matcher, Epiline Matcher, and Partial Epiline Matcher are evaluated. The paper also examines the integration of AI with classical pixel-matching methods to streamline pixel pair identification. Real-world accuracy assessments demonstrate promising results, facilitating practical applications. Additionally, it covers camera calibration and image rectification tasks on VIS cameras to support 3D reconstruction for plant structure analysis, alongside Stereo pixel matching. Overall, it provides valuable insights into stereo image analysis in agriculture, fostering future research and practical implementations in precision agriculture and computer vision systems.en
dc.identifier.doi10.18420/inf2024_114
dc.identifier.isbn978-3-88579-746-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45086
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.subjectVertical-Farming-System
dc.subjectSWIR-Technology
dc.subjectAI-weed detection
dc.subjectImage data generation
dc.subjectLaboratory environment
dc.subject3D reconstruction
dc.subjectAI-based
dc.subjectCNN disparity map
dc.subjectMachine Learning
dc.titleMethods and techniques for plant and weed detection creating a database for future computer vision systems in weed control and practical implementations: Insights from the KIdetect project, funded by the BMELen
dc.typeText/Conference Paper
gi.citation.endPage1293
gi.citation.publisherPlaceBonn
gi.citation.startPage1287
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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