Gaia-AgStream: An Explainable AI Platform for Mining Complex Data Streams in Agriculture

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dc.contributor.authorSchoenke, J.
dc.contributor.authorAschenbruck, N.
dc.contributor.authorInterdonato, R.
dc.contributor.authorKanawati, R.
dc.contributor.authorMeisener, A.-C.
dc.contributor.authorThierart, F.
dc.contributor.authorVial, G.
dc.contributor.authorAtzmueller, M.
dc.contributor.editorBoumerdassi, S.
dc.contributor.editorGhogho, M.
dc.contributor.editorRenault, E.
dc.date.accessioned2021-12-23T16:34:47Z-
dc.date.available2021-12-23T16:34:47Z-
dc.date.issued2021
dc.identifier.isbn9783030882587
dc.identifier.issn18650929
dc.identifier.urihttps://osnascholar.ub.uni-osnabrueck.de/handle/unios/18200-
dc.descriptionConference of 1st International Conference on Smart and Sustainable Agriculture, SSA 2021 ; Conference Date: 21 June 2021 Through 22 June 2021; Conference Code:268539
dc.description.abstractWe present a position paper about our concept for an artificial intelligence (AI) and data streaming platform for the agricultural sector. The goal of our project is to support agroecology in terms of carbon farming and biodiversity protection by providing an AI and data streaming platform called Gaia-AgStream that accelerates the adoption of AI in agriculture and is directly usable by farmers as well as agricultural companies in general. The technical innovations we propose focus on smart sensor networks, unified uncertainty management, explainable AI, root cause analysis and hybrid AI approaches. Our AI and data streaming platform concept contributes to the European open data infrastructure project Gaia-X in terms of interoperability for data and AI models as well as data sovereignty and AI infrastructure. Our envisioned platform and the developed AI components for carbon farming and biodiversity will enable farmers to adopt sustainable and resilient production methods while establishing new and diverse revenue streams by monetizing carbon sequestration and AI ready data streams. The open and federated platform concept allows to bring together research, industry, agricultural start-ups and farmers in order to form sustainable innovation networks. We describe core concepts and architecture of our proposed approach in these contexts, outline practical use cases for our platform and finally outline challenges and future prospects. © 2021, Springer Nature Switzerland AG.
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofCommunications in Computer and Information Science
dc.subjectAgriculture
dc.subjectAgroecology
dc.subjectAnomaly detection
dc.subjectBiodiversity
dc.subjectCarbon farming
dc.subjectComplex networks
dc.subjectData fusion
dc.subjectData integration
dc.subjectData quality
dc.subjectData reduction
dc.subjectData streaming
dc.subjectDistributed systems
dc.subjectExplainable AI
dc.subjectExplainable artificial intelligence
dc.subjectInformation management
dc.subjectKnowledge graph
dc.subjectKnowledge graphs
dc.subjectMachine learning
dc.subjectOpen Data
dc.subjectQuality control
dc.subjectRoot cause analysis
dc.subjectSemantic Web
dc.subjectSensor data fusion
dc.subjectSensor networks
dc.subjectSensors network
dc.subjectUncertainty analysis, Agro ecologies
dc.subjectUncertainty management
dc.subjectUncertainty management, Machine learning
dc.titleGaia-AgStream: An Explainable AI Platform for Mining Complex Data Streams in Agriculture
dc.typeconference paper
dc.identifier.doi10.1007/978-3-030-88259-4_6
dc.identifier.scopus2-s2.0-85119875445
dc.identifier.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85119875445&doi=10.1007%2f978-3-030-88259-4_6&partnerID=40&md5=3ee852e65c1f129b82bc022ceead0f53
dc.description.volume1470 CCIS
dc.description.startpage71
dc.description.endpage83
dcterms.isPartOf.abbreviationCommun. Comput. Info. Sci.
crisitem.author.orcid0000-0002-5861-8896-
crisitem.author.netidAsNi712-
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