# An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation
###### Abstract  
A great deal of information related to rice cultivation has been published on the web. Conventionally, this information is studied by end-users to identify pests, and to prevent production losses from rice diseases. Despite its benefits, such information has not yet been encoded in a machine-processable form. This research closes the gap by modeling the knowledge-bases using ontologies and semantic technologies. Our modeled ontologies are externalized from existing reliable sources only, and offer axioms that describe abnormal appearances in rice diseases (and insects) and the corresponding controls. In addition, we developed an expert system called RiceMan, based on our ontologies, to support technical and non-technical users for diagnosing problems from observed abnormalities. We also introduce a composition procedure that aggregates users' observation data with others for realizing spreadable diseases. This procedure, 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 RiceMan are evaluated to verify their correctness, 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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to investigations of the design and modeling of rice disease ontologies and their adoption to practical applications. Therefore, our research questions (RQs) are set up as follows:  
* RQ1: how could a knowledge base for rice disease and paddy insects be modeled from the available unstructured resources?
* RQ2: How could a knowledge base for rice disease be applied practically?
* RQ3: How can a designed expert system be evaluated to ensure its appropriateness, from the viewpoints of stakeholders?  
This research is an extended study of our proceeding research, which developed the first preliminary version of the ontologies \"RiceDO\" and \"TreatO\", covering 22 rice diseases and paddy insects, and introduced a semantic-based framework for supporting rice disease and control identification. In this paper, our novelties consist of two ontologies (RiceDO and TreatO version 2) which have been improved from RiceDO and TreatO to meet the accepted standards. The structures of RiceDO and TreatO version 2 are reorganized and are designed for retrieving possible rice pests and controls, respectively. Additionally, to practically utilize both ontologies, we extended and implemented a novel framework and expert system, called RiceMan, and then performed a thorough evaluation to ensure the correctness, usefulness, and usability. The contributions of this work are threefold:  
* Existing ontologies were investigated, remodeled, revised, and evaluated, practically, from the viewpoints of four groups of stakeholders;
* An expert system, called RiceMan, was designed and developed for rice disease identification and control recommendation;
* Results of applying a expert system in the rice domain were evaluated and reported.  
Section 2 reviews the related works. Sections 3 and 4 present the ontologies and the RiceMan expert system, respectively. Section 5 explores the empirical evaluation. In Section 6, we draw our conclusions.