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Synthetic fields, real gains

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dc.contributor.authorWachter, Paul
dc.contributor.authorKruse, Niklas
dc.contributor.authorSchöning, Julius
dc.date.accessioned2024-04-08T11:56:36Z
dc.date.available2024-04-08T11:56:36Z
dc.date.issued2024
dc.description.abstractArtificial intelligence (AI) promises transformative impacts on society, industry, and agriculture, while being heavily reliant on diverse, quality data. The resource-intensive “data problem” has initialized a shift to synthetic data. One downside of synthetic data is known as the “reality gap”, a lack of realism. Hybrid data, combining synthetic and real data, addresses this. The paper examines terminological inconsistencies and proposes a unified taxonomy for real, synthetic, augmented, and hybrid data. It aims to enhance AI training datasets in smart agriculture, addressing the challenges in the agricultural data landscape. Utilizing hybrid data in AI models offers improved prediction performance and adaptability.en
dc.identifier.isbn978-3-88579-738-8
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43916
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartof44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft
dc.relation.ispartofseriesLecture Notes in Informatics(LNI) - Proceedings, Volume P - 344
dc.subjecthybrid data
dc.subjectsynthetic data
dc.subjectaugmented data
dc.subjectsmart farming
dc.subjectreality gap
dc.titleSynthetic fields, real gainsen
dc.typeText/Conference Paper
gi.citation.endPage442
gi.citation.publisherPlaceBonn
gi.citation.startPage437
gi.conference.date27.-28. Februar 2080
gi.conference.locationStuttgart
gi.conference.reviewfull

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P344 - 44. GIL-Jahrestagung 2023 - Fokus: Biodiversität fördern durch digitale Landwirtschaft