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An Enrichment Framework for Multi-Faceted Observations

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dc.contributor.advisor Anutariya, Dr. Chutiporn
dc.contributor.author Jearanaiwongkul, Watanee
dc.contributor.other Andres, Dr. Frederic (Co-chair)
dc.contributor.other Taparugssanagorn, Dr. Attaphongse
dc.contributor.other Silpasuwanchai, Dr. Chaklam
dc.date.accessioned 2020-12-08T07:37:14Z
dc.date.available 2020-12-08T07:37:14Z
dc.date.issued 2020-12
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/989
dc.description.abstract Dealing with plants in farms is one of the important challenges during the cultivation. Plants can be damaged by various factors, especially plant diseases and insects. When they are damaged, different kinds of abnormal characteristics can be occurred and observed by farmers. In real-life situations, farmers manually manage the observation data by comparing it with their background knowledge; or consulting the others e.g. friends and agronomists. To improve this traditional practice and to avoid the problems of diverse skills and personal knowledge, we have investigated and proposed a novel framework to manage observation data. Specifically, we are interested in managing multiple observation data from one or various farmers at a certain time and place since a plant disease can be disseminated. Currently, various knowledge related to plant cultivation has been widely published on the web. This has motivated us to externalize and utilize such knowledge for detecting of plant diseases based on the observation data and recommending appropriate treatments. To this end, both observation data and existing knowledge are investigated in theoretically and practically. In the theoretical phase, we formalize desirable characteristics of observation data from farmers and develop a farmer’s observation management formalism which deal with an observation data representation, treatment representation and, observation data composition. Key characteristics of the formalism are defined according to a type for representing observation data and a composition function for composing relevant observation data. We also show that our formalism helps any observation data to become richer and improve a mechanism of recommending treatments for plant diseases. In our practical phase, we have applied the proposed formalism into a practical use case of rice plants in Thailand. We study: (1) a representation of observations data, in which it can be deployed in practice at Thailand rice fields, (2) a representation of knowledge base of rice plant, which enables to refer rice diseases and treatments by a farmer’s observation, and (3) the design and development of our expert system reflecting our proposed framework from theoretical to practical studies. Our contributions in this part are twofold. First, we model ontologies using the Web Ontology Languages (OWL) for Rice Disease Ontology (RiceDO) and Treatment Ontology (TreatO) from reliable knowledge sources. Second, we develop RiceMan which is an expert system for supporting daily-life activities of farmers and agronomists who are responsible to advise farmers. In RiceMan, users can opt in to integrate their observations with others for the disease and treatment prediction. This composition mechanism (coincided with our theoretical formalism), together with ontology reasoning, lies at the heart of RiceMan. Finally, our thesis is evaluated practically with four kinds of stakeholders: (1) domain expert agronomists, (2) non-domain expert agronomists, (3) students in agricultural field, and (4) ontology engineers. Results are carefully analyzed and discussed immensely in this thesis. en_US
dc.description.sponsorship AIT en_US
dc.language.iso en_US en_US
dc.publisher AIT en_US
dc.subject Observation Data Management, Knowledge Representation, Rice Disease Ontology, Ontological Reasoning, Semantic-based Recommendation, and Ontology-based application. en_US
dc.title An Enrichment Framework for Multi-Faceted Observations en_US
dc.type Dissertation en_US

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