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Evaluating synthetic vs. real data generation for AI-based selective weeding

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dc.contributor.authorIqbal, Naeem
dc.contributor.authorBracke, Justus
dc.contributor.authorElmiger, Anton
dc.contributor.authorHameed, Hunaid
dc.contributor.authorvon Szadkowski, Kai
dc.contributor.editorHoffmann, Christa
dc.contributor.editorStein, Anthony
dc.contributor.editorRuckelshausen, Arno
dc.contributor.editorMüller, Henning
dc.contributor.editorSteckel, Thilo
dc.contributor.editorFloto, Helga
dc.date.accessioned2023-02-21T15:14:29Z
dc.date.available2023-02-21T15:14:29Z
dc.date.issued2023
dc.description.abstractSynthetic data has the potential to reduce the cost for ML training in agriculture but poses its own set of problems compared to real data acquisition. In this work, we present two methods of training data acquisition for the application of machine vision algorithms in the use case of selective weeding. Results from ML experiments suggest that current methods for generating synthetic data in the field of agriculture cannot fully replace real data but may greatly reduce the quantity of real data required for model training.en
dc.identifier.isbn978-3-88579-724-1
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/40311
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartof43. GIL-Jahrestagung, Resiliente Agri-Food-Systeme
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-330
dc.subjectsynthetic images
dc.subjectplant detection
dc.subjectphenotyping
dc.subjectdeep learning
dc.subjectagriculture
dc.titleEvaluating synthetic vs. real data generation for AI-based selective weedingen
dc.typeText/Conference Paper
gi.citation.endPage135
gi.citation.publisherPlaceBonn
gi.citation.startPage125
gi.conference.date13.-14. Februar 2023
gi.conference.locationOsnabrück

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P330 - 43. GIL-Jahrestagung 2023 - Fokus: Resiliente Agri-Food-Systeme