Towards Ontology-based Expert System Development and Evaluation for Rice Disease Identification and Control Recommendation

Tracking #: 2777-3991

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Watanee Jearanaiwongkul
Chutiporn Anutariya
Teeradaj Racharak
Frederic Andres

Responsible editor: 
Tania Tudorache

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Full Paper
Various knowledge related to rice cultivation has been widely published on the web. Conventionally, this knowledge is manually studied by end-users for rice diseases and pests identification to prevent production losses. Despite their benefits, the knowledge has not yet been encoded in a machine-processable form. We improve this gap by turning the unstructured or semi-structured knowledge of rice diseases and their controls into the structured ones by using ontologies and semantic technologies. We externalize knowledge from existing reliable sources only. As a result, the developed ontologies offer axioms that describe abnormal appearances in rice diseases (and insects) and their corresponding controls. We also develop an expert system called RiceMan based on our ontologies to support technical and non-technical users for the identification of diseases and insects from their observed abnormalities. We also introduce a composition service to aggregate users’ observation data with others for realizing spreadable diseases and controls. This composition mechanism, together with ontology reasoning, lies at the heart of our methodology. Finally, we evaluate our methodology practically with four groups of stakeholders in Thailand: senior agronomists, junior agronomists, agricultural students, and ontology specialists. Both ontologies and the RiceMan application are evaluated to ensure their usefulness and usability in various aspects. Our experimental results show that ontology reasoning is a promising approach for this domain problem.
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