Gaia-AgStream: An Explainable AI Platform for Mining Complex Data Streams in Agriculture
Autor(en): | Schoenke, J. Aschenbruck, N. Interdonato, R. Kanawati, R. Meisener, A.-C. Thierart, F. Vial, G. Atzmueller, M. |
Herausgeber: | Boumerdassi, S. Ghogho, M. Renault, E. |
Stichwörter: | Agriculture; Agroecology; Anomaly detection; Biodiversity; Carbon farming; Complex networks; Data fusion; Data integration; Data quality; Data reduction; Data streaming; Distributed systems; Explainable AI; Explainable artificial intelligence; Information management; Knowledge graph; Knowledge graphs; Machine learning; Open Data; Quality control; Root cause analysis; Semantic Web; Sensor data fusion; Sensor networks; Sensors network; Uncertainty analysis, Agro ecologies; Uncertainty management; Uncertainty management, Machine learning | Erscheinungsdatum: | 2021 | Herausgeber: | Springer Science and Business Media Deutschland GmbH | Enthalten in: | Communications in Computer and Information Science | Band: | 1470 CCIS | Startseite: | 71 | Seitenende: | 83 | Beschreibung: | Conference of 1st International Conference on Smart and Sustainable Agriculture, SSA 2021 ; Conference Date: 21 June 2021 Through 22 June 2021; Conference Code:268539 |
ISBN: | 9783030882587 | ISSN: | 18650929 | DOI: | 10.1007/978-3-030-88259-4_6 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119875445&doi=10.1007%2f978-3-030-88259-4_6&partnerID=40&md5=3ee852e65c1f129b82bc022ceead0f53 |
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