An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation

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-process...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Watanee Jearanaiwongkul, Chutiporn Anutariya, Teeradaj Racharak, Frederic Andres
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	knowledge-based system rice disease ontology ontology evaluation knowledge representation and reasoning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/2ce952c314134ff7a539682269a2d709
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:2ce952c314134ff7a539682269a2d709
record_format	dspace
spelling	oai:doaj.org-article:2ce952c314134ff7a539682269a2d7092021-11-11T15:24:18ZAn Ontology-Based Expert System for Rice Disease Identification and Control Recommendation10.3390/app1121104502076-3417https://doaj.org/article/2ce952c314134ff7a539682269a2d7092021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10450https://doaj.org/toc/2076-3417A 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.Watanee JearanaiwongkulChutiporn AnutariyaTeeradaj RacharakFrederic AndresMDPI AGarticleknowledge-based systemrice disease ontologyontology evaluationknowledge representation and reasoningTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10450, p 10450 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	knowledge-based system rice disease ontology ontology evaluation knowledge representation and reasoning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	knowledge-based system rice disease ontology ontology evaluation knowledge representation and reasoning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Watanee Jearanaiwongkul Chutiporn Anutariya Teeradaj Racharak Frederic Andres An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation
description	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.
format	article
author	Watanee Jearanaiwongkul Chutiporn Anutariya Teeradaj Racharak Frederic Andres
author_facet	Watanee Jearanaiwongkul Chutiporn Anutariya Teeradaj Racharak Frederic Andres
author_sort	Watanee Jearanaiwongkul
title	An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation
title_short	An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation
title_full	An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation
title_fullStr	An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation
title_full_unstemmed	An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation
title_sort	ontology-based expert system for rice disease identification and control recommendation
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2ce952c314134ff7a539682269a2d709
work_keys_str_mv	AT wataneejearanaiwongkul anontologybasedexpertsystemforricediseaseidentificationandcontrolrecommendation AT chutipornanutariya anontologybasedexpertsystemforricediseaseidentificationandcontrolrecommendation AT teeradajracharak anontologybasedexpertsystemforricediseaseidentificationandcontrolrecommendation AT fredericandres anontologybasedexpertsystemforricediseaseidentificationandcontrolrecommendation AT wataneejearanaiwongkul ontologybasedexpertsystemforricediseaseidentificationandcontrolrecommendation AT chutipornanutariya ontologybasedexpertsystemforricediseaseidentificationandcontrolrecommendation AT teeradajracharak ontologybasedexpertsystemforricediseaseidentificationandcontrolrecommendation AT fredericandres ontologybasedexpertsystemforricediseaseidentificationandcontrolrecommendation
_version_	1718435356814082048

An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation

Ejemplares similares