Ontologies for Smart Agriculture: A Path Toward Explainable AI—A Systematic Literature Review
Smart agriculture has grown significantly over the last few years, particularly with the integration of advanced technologies (e.g., the Internet of Things, robots, artificial intelligence, etc.), leading to the development of intelligent agricultural systems. However, these systems often lack data...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10973055/ |
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| Summary: | Smart agriculture has grown significantly over the last few years, particularly with the integration of advanced technologies (e.g., the Internet of Things, robots, artificial intelligence, etc.), leading to the development of intelligent agricultural systems. However, these systems often lack data integration, interoperability, and semantic explainability. Various approaches have been proposed to address these challenges. This study provides a comprehensive literature review that addresses, on the one hand, the use of semantic resources (e.g., semantic web technologies and ontologies) to tackle data integration and interoperability in smart agriculture systems and, on the other hand, the integration of explainable artificial intelligence into smart agricultural systems. Furthermore, it aims to identify limitations in existing studies and explore potential avenues for future research. This research introduces key concepts related to smart agriculture, semantic resources, and explainable artificial intelligence. Subsequently, three clusters of studies are presented, including semantic resources for smart agriculture, leveraging the explainable artificial intelligence for smart agriculture, and the role of semantic resources in the explainable artificial intelligence-based agriculture systems. Lastly, the limitations of the semantic-based smart agriculture system are examined, along with potential future research areas. |
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| ISSN: | 2169-3536 |