Context-Aware Control Service Model Based on Ontology for Greenhouse Environment

Bae, Nam-Jin; Kwak, KyungHun; Sivamani, Saraswathi; Shin, Chang-Sun; Park, Jang-Woo; Cho, Kyungryong; Cho, Yongyun

doi:10.1007/978-3-642-41674-3_46

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…Moreover, the system enables convenient management of the environment, e.g., the automatic/remote operation of HVAC, window opening and closing, and the supply of CO 2 and nutrient feed. However, faults in ICT equipment can cause considerable damage to farms, making an effective fault diagnosis system essential [20][21][22][23].…”

Section: Smart Farming Systemsmentioning

confidence: 99%

Artificial Intelligence-Based Fault Diagnosis and Prediction for Smart Farm Information and Communication Technology Equipment

Choe,

Lee

2023

Agriculture

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Despite the recent increase in smart farming practices, system uncertainty and difficulties associated with maintaining farming sites hinder their widespread adoption. Agricultural production systems are extremely sensitive to operational downtime caused by malfunctions because it can damage crops. To resolve this problem, the types of abnormal data, the present error determination techniques for each data type, and the accuracy of anomaly data determination based on spatial understanding of the sensed values are classified in this paper. We design and implement a system to detect and predict abnormal data using a recurrent neural network algorithm and diagnose malfunctions using an ontological technique. The proposed system comprises the cloud in charge of the IoT equipment installed in the farm testbed, communication and control, system management, and a common framework based on machine learning and deep learning for fault diagnosis. It exhibits excellent prediction performance, with a root mean square error of 0.073 for the long short-term memory model. Considering the increasing number of agricultural production facilities in recent years, the results of this study are expected to prevent damage to farms due to downtime caused by mistakes, faults, and aging.

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Section: Smart Farming Systemsmentioning

confidence: 99%

Artificial Intelligence-Based Fault Diagnosis and Prediction for Smart Farm Information and Communication Technology Equipment

Choe,

Lee

2023

Agriculture

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“…(2014) afirmam que os serviços oferecidos pela TI nesta área são, basicamente, divididos em monitoramento e controle e que, uma vez que eles são subordinados a um usuário em particular, apresenta limitações no que tange o acompanhamento ativo das exceções do sistema e das variáveis ambientais. Basicamente, Bae et al (2014) pensaram em um modelo ontológico que, utilizando-se de dados comunsbaseados em informações contextuais como temperatura, umidade, iluminação e concentração de CO2, viabiliza a colaboração natural entre os recursos computacionais para minimizar a intervenção humana.…”

Section: Trabalhos Correlatosunclassified

“…Foi utilizado como base a ontologia proposta por Bae et al (2014), no artigo intitulado Context-Aware Control Service Model Based on Ontology for Greenhouse Environment.…”

Section: Reutilização De Ontologias Pré-existentesunclassified

Ontology for Domain Representation in Smart Greenhouse Ontologia Para Representacao De Dominio Em Casa De Vegetacao Inteligente

Filho¹,

Batti²,

Gonçalves³

et al.

19th CONTECSI International Conference on Information Systems and Technology Management

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With the increase in population, the concern with food production combined with good crop management practices has been prioritized in the agricultural environment. One of the methods that stands out in this segment is production in greenhouses, where it is possible to control internal climatic factors, generating products with added value. With the advent of computer technologies, applications aimed at agriculture have grown exponentially, such as the use of sensing and artificial intelligence techniques, transforming these environments into intelligent places. Although there are a number of technological devices that can be used to improve the environmental aspects of greenhouses, these domains must be created in a structured way, so that it represents the knowledge involved between all agents and parameters. Considering these aspects, this study proposes an ontology-based model for smart greenhouses. To achieve this goal, it searches the literature for existing ontology models and implements, using the Prótége software, the classes and relationships of this domain. As a result, the ontology proves to be a facilitating tool in the creation of conceptual models that serve as a knowledge base and representation of cultivation environments in smart greenhouses, expressing aspects of the real world.

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Context-Aware Control Service Model Based on Ontology for Greenhouse Environment

Cited by 2 publications

References 4 publications

Artificial Intelligence-Based Fault Diagnosis and Prediction for Smart Farm Information and Communication Technology Equipment

Artificial Intelligence-Based Fault Diagnosis and Prediction for Smart Farm Information and Communication Technology Equipment

Ontology for Domain Representation in Smart Greenhouse Ontologia Para Representacao De Dominio Em Casa De Vegetacao Inteligente

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