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A data quality assessment tool for agricultural structured data as support for smart farming

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dc.contributor.authorSchroth, Christof
dc.contributor.authorKelbert, Patricia
dc.contributor.authorVollmer, Anna Maria
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.accessioned2023-02-21T15:14:19Z
dc.date.available2023-02-21T15:14:19Z
dc.date.issued2023
dc.description.abstractIn the field of precision farming or smart farming, more and more sensors are used and produce a massive amount of data. Examples are machinery, weather stations, or georeferenced data, which can be used, among other things, by Artificial Intelligence decision support systems to improve or facilitate farmers’ daily work tasks. Even if there are no issues in transferring (Internet of Things) sensor data from machines to farm management information systems, data still contain errors such as missing, implausible, or incorrect data values. In this paper, we present an automated data quality assessment (DQA) tool based on the ISO25012 standard. We describe the process of how we developed this tool with support from practitioners who produce agricultural data in the context of the EU Horizon 2020 project DEMETER. Additionally, we highlight some of the requirements we collected for such a tool and briefly discuss how we addressed them. For example, we learned that in the context of developing smart farming services, the data quality dimensions Accuracy, Completeness, Consistency, and Credibility are the most important ones for practitioners such as farmers, digital service providers, or machine suppliers. Therefore, we included them in the DQA tool and implemented it in Python. It is released under the open-source Apache 2 license. Individual parameters can be provided as input for calculations (e.g., thresholds or time lengths) to meet different users’ needs. The output of the DQA is provided in machine-readable JSON format and can be used for further analysis, e.g., to improve the quality of the data collection or the follow-up data analysis. This can help practitioners develop more valuable smart farming services.en
dc.identifier.isbn978-3-88579-724-1
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/40299
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.subjectprecision farming
dc.subjectsmart farming
dc.subjectdata quality
dc.subjectopen source
dc.subjectstructured data
dc.titleA data quality assessment tool for agricultural structured data as support for smart farmingen
dc.typeText/Conference Paper
gi.citation.endPage506
gi.citation.publisherPlaceBonn
gi.citation.startPage501
gi.conference.date13.-14. Februar 2023
gi.conference.locationOsnabrück

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