An ontology-based approach to integration of hilly citrus production knowledge

The rapid development of information and communication technologies and wireless sensor networks has transformed agriculture practices. New tools and methods are used to support farmers in their activities. This paper presents a context-aware system that automates irrigation decisions based on sensor measurements. Automatic irrigation overcomes the water shortage problem, and automatic sensor measurements reduce the observational work of farmers. This paper focuses on a method for developing context-aware systems using ontologies. Ontologies are used to solve heterogeneity issues in sensor measurements. Their main goal is to propose a shared data schema that precisely describes measurements to ease their interpretations. These descriptions are reusable by any machine and understandable by humans. The context-aware system also contains a decision support system based on a rules inference engine. We propose two new ontologies: The Context-Aware System Ontology addresses the development of the context-aware system in general. The Irrigation ontology automates a manual irrigation method named IRRINOV®. These ontologies reuse well-known ontologies such as the Semantic Sensor Network (SSN) and Smart Appliance REFerence (SAREF). The decision support system uses a set of rules with ontologies to infer daily irrigation decisions for farmers. This project uses real experimental data to evaluate the implementation of the decision support system.

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“…The works of [7,8] present some ontologies dedicated to hilly citrus tree cultivation. One ontology addresses the irrigation decision.…”

Section: State Of the Artmentioning

confidence: 99%

Development Experience of a Context-Aware System for Smart Irrigation Using CASO and IRRIG Ontologies

et al. 2020

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“…Experts in the field of crop diseases and insect pests have combed and integrated agricultural knowledge and constructed many ontologies of pests and diseases. For example, Wang et al [16] constructed an ontology to organize and manage citrus production knowledge in the hilly areas of Chongqing, China, extracted citrus fertilizer and water ontology from documents and charts of citrus production knowledge, and developed a decision support system for citrus fertilizer and water management based on semantic ontology. Chougule and Mukhopadhyay [17] proposed a method to construct a crop pest ontology in India.…”

Section: A Knowledge Representation 1) Rdf and Ontologymentioning

confidence: 99%

Review and Trend Analysis of Knowledge Graphs for Crop Pest and Diseases

et al. 2019

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Current techniques of knowledge management have some common defects in efficiency, scalability, and applicability. Knowledge graph provides a new way for knowledge management and is a more flexible knowledge management method. Considering the specific features of crop diseases and pest data, this paper analyzed and classified the key techniques and methods of knowledge graph technology in the field of crop diseases and pest in recent years. We introduced the definition and connotation of the crop diseases and pest knowledge, and the current construction method of it was analyzed in detail from four aspects: knowledge representation, extraction, fusion, and reasoning. Furthermore, the application of crop diseases and pest knowledge graph was introduced in detail in the expert system, search engine, and knowledge question-answering system. At last, this paper summarized the challenges and important problems of diseases and pest knowledge graph and forecasted the prospect of knowledge graph according to the key points and difficulties of current knowledge graph research.INDEX TERMS Knowledge graph, crop disease and pest, knowledge extraction, knowledge fusion, knowledge reasoning.

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“…Approach that automatically generate complex alignments between ontologies are emerging [17,16,14]. Ontology alignments can be used for various applications such as ontology merging [24,15] and query mediation [9].…”

Section: Agricultural Sourcesmentioning

confidence: 99%

Cross-Querying LOD Datasets Using Complex Alignments: An Application to Agronomic Taxa

Thiéblin

Amarger

Hernandez

et al. 2017

Metadata and Semantic Research

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Farmers have new information needs to change their agricultural practices. The Linked Open Data is a considerable source of knowledge, separated into several heterogeneous and complementary datasets. This paper presents a process to query LOD datasets from a known ontology using complex alignments. The approach was applied on Agro-nomicTaxon, a taxonomic classification ontology, to query Agrovoc and DBpedia.

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An ontology-based approach to integration of hilly citrus production knowledge

Cited by 38 publications

References 12 publications

Development Experience of a Context-Aware System for Smart Irrigation Using CASO and IRRIG Ontologies

Development Experience of a Context-Aware System for Smart Irrigation Using CASO and IRRIG Ontologies

Review and Trend Analysis of Knowledge Graphs for Crop Pest and Diseases

Cross-Querying LOD Datasets Using Complex Alignments: An Application to Agronomic Taxa

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