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Ontology-Based Decision Support System for the Nitrogen Fertilization of Winter Wheat

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Metadata and Semantic Research (MTSR 2020)

Abstract

Digital technologies are already used in several aspects of agriculture. However, decision-making in crop production is still often a manual process that relies on various heterogeneous data sources. Small-scale farmers and their local consultants are particularly burdened by increasingly complex requirements. Regional circumstances and regulations play an essential role and need to be considered. This paper presents an ontology-based decision support system for the nitrogen fertilization of winter wheat in Bavaria, Germany. Semantic Web and Linked Data technologies were employed to both reuse and model new common semantic structures for interrelated knowledge. Many relevant general and regional data sources from multiple domains were not yet available in RDF. Hence, we used several tools to transform relevant data into corresponding OWL ontologies and combined them in a central knowledge base. The GUI application of the decision support system queries it to parameterize requests to external web services and to show relevant information in an integrated view. It further uses SPARQL queries to automatically generate recommendations for farmers and their consultants.

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Notes

  1. 1.

    https://www.eppo.int/RESOURCES/eppo_databases/eppo_codes.

  2. 2.

    https://www.ontotext.com/products/graphdb/.

  3. 3.

    https://www.qgis.org/en/site/.

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Acknowledgments

The research leading to these results has been funded by the Bavarian State Ministry of Food, Agriculture and Forestry (StMELF) under grant agreement no. D/17/02 in the project FarmExpert.

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Correspondence to Ingmar Kessler .

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Kessler, I., Perzylo, A., Rickert, M. (2021). Ontology-Based Decision Support System for the Nitrogen Fertilization of Winter Wheat. In: Garoufallou, E., Ovalle-Perandones, MA. (eds) Metadata and Semantic Research. MTSR 2020. Communications in Computer and Information Science, vol 1355. Springer, Cham. https://doi.org/10.1007/978-3-030-71903-6_24

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  • DOI: https://doi.org/10.1007/978-3-030-71903-6_24

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