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
Journal: Communications in Computer and Information Science
Volumen: 1470 CCIS
Startseite: 71
Seitenende: 83
Zusammenfassung: 
We 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.
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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