Advancing precision livestock farming through ontology-driven interoperable health data management: Extending the Livestock Health Ontology (LHO) for enhanced disease surveillance
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2024
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| _version_ | 1866902337188003840 |
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| author | Noor, Saba |
| author_facet | Noor, Saba |
| contents | <p>Precision Livestock Farming (PLF) is a form of smart farming that advances agriculture by prioritizing animal care through real-time monitoring of individual animal health. Despite its potential, some challenges need attention. PLF faces challenges in integrating diverse data from different locations and formats, ensuring consistency and understanding for better animal health and welfare. To address these challenges, ontologies can play a central role. Ontologies are formal and explicit representations of knowledge that define concepts, relationships, and properties within a specific domain. In the context of PLF, ontologies serve as a structured framework, facilitating the integration and standardization of diverse data sources and ensuring a unified representation for seamless analysis and decision-making, ensuring interoperability. This paper describes Ontology-Driven Knowledge-based Farm Animal Data Management (ODKFADM) framework demonstrating a case study for the development and application of Livestock Health Ontology (LHO) by integrating AGROVOC thesauruses, specifically designed for cattle, poultry, and pigs. The aim is to enhance integration and obtain a unified view of the data for analysis and visualization. This approach focuses on resource descriptive framework (RDF) data generation, utilizing SPARQL queries for data analysis, and data visualization to enhance LHO's applications in disease surveillance and early detection. The results are served to multiple data users, and showcased via a visualization tool to provide insights to veterinarians, researchers, and farmers. In the near future, we envision that this tool, or similar ones, capture pathogen trends, provide early warnings, support informed vaccination decisions, and facilitate effective disease control on farms.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15356227 |
| institution | Zenodo |
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| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Advancing precision livestock farming through ontology-driven interoperable health data management: Extending the Livestock Health Ontology (LHO) for enhanced disease surveillance Noor, Saba <p>Precision Livestock Farming (PLF) is a form of smart farming that advances agriculture by prioritizing animal care through real-time monitoring of individual animal health. Despite its potential, some challenges need attention. PLF faces challenges in integrating diverse data from different locations and formats, ensuring consistency and understanding for better animal health and welfare. To address these challenges, ontologies can play a central role. Ontologies are formal and explicit representations of knowledge that define concepts, relationships, and properties within a specific domain. In the context of PLF, ontologies serve as a structured framework, facilitating the integration and standardization of diverse data sources and ensuring a unified representation for seamless analysis and decision-making, ensuring interoperability. This paper describes Ontology-Driven Knowledge-based Farm Animal Data Management (ODKFADM) framework demonstrating a case study for the development and application of Livestock Health Ontology (LHO) by integrating AGROVOC thesauruses, specifically designed for cattle, poultry, and pigs. The aim is to enhance integration and obtain a unified view of the data for analysis and visualization. This approach focuses on resource descriptive framework (RDF) data generation, utilizing SPARQL queries for data analysis, and data visualization to enhance LHO's applications in disease surveillance and early detection. The results are served to multiple data users, and showcased via a visualization tool to provide insights to veterinarians, researchers, and farmers. In the near future, we envision that this tool, or similar ones, capture pathogen trends, provide early warnings, support informed vaccination decisions, and facilitate effective disease control on farms.</p> |
| title | Advancing precision livestock farming through ontology-driven interoperable health data management: Extending the Livestock Health Ontology (LHO) for enhanced disease surveillance |
| url | https://doi.org/10.5281/zenodo.15356227 |