12
A. Chatzimichail et al.
presented in [71] in a project from the National Institute of Standards and Technology
(NIST).
The main challenges for the semantic technologies in IoT in Industry are in setting ontologies for integration and interoperability between the already existing old
industry standards, use of existing ontologies (e.g. SSN, SAREF etc) to make applications for industry 4.0 using sensor data provided by autonomous systems and the
execution of exemplary use case scenarios in real industrial conditions.
2.1.10 Agriculture—Farming
In agriculture, there is very fast-growing trend with smart devices entering the fields
and helping farmers to comprehend better the crops and their production. This trend is
called precision agriculture and currently is generating huge volumes of raw data from
IoT sources such as: chemical sensors, electro-chemical sensors, drones, weather
stations and so on. Those thousand of lines raw data are meaningless and isolated,
and therefore they do not add extra knowledge to the farmer. Agriculture activities
are based on a multiparameter knowledge with many interconnections between the
parameters. The efficacy of data derives from context and meaning, as well as its
combination with other data from different agriculture sources. Semantic technologies can provide practicality to the agriculture/farming data by providing common
interchange data formats. Also, through SW new knowledge can be provided through
the use of reasoners.
Semantic resources are typically divided in two different categories for general agriculture or specialized domains of agriculture. Several significant agriculture
ontologies are OntoAgroHidro [72], Crop ontology [73], GCP ontology [74], Agroportal [75], Agricultural Technology Ontology [76], Citrus Ontology [77], Agriculture Activity Ontology (AAO) [78], AgOnt [79], Agronomy Ontology [80]. Projects
like Agrovoc [81] consists of +36,000 concepts and +750,000 terms in up to 35
languages, have provided with structured vocabularies for the agriculture domain.
The Global Agricultural Concept Scheme (GACS) [82] contains in its f iles of interoperable concepts the schemes related to agriculture from AGROVOC multilingual
agricultural thesaurus (35,000 concepts), the CAB Thesaurus [83] (140,000 concepts) and the NAL Thesaurus [84] (53,000 concepts).
Agriculture requires common data schemes for semantic web technologies to
render plausible the transfer of semantically described data and the development of
common ontologies. One such a standard is known as: The Agricultural Metadata
Element set (AgMes) [85]. AgroRDF [86] is one of the major standards for data
exchange, which is designed specifically for agricultural data. The applications with
agriculture semantic technologies are divided mainly into those different categories:
Knowledge based systems, Remote Sensing, Decision Support and Expert Systems
[87].
A. Chatzimichail et al.
presented in [71] in a project from the National Institute of Standards and Technology
(NIST).
The main challenges for the semantic technologies in IoT in Industry are in setting ontologies for integration and interoperability between the already existing old
industry standards, use of existing ontologies (e.g. SSN, SAREF etc) to make applications for industry 4.0 using sensor data provided by autonomous systems and the
execution of exemplary use case scenarios in real industrial conditions.
2.1.10 Agriculture—Farming
In agriculture, there is very fast-growing trend with smart devices entering the fields
and helping farmers to comprehend better the crops and their production. This trend is
called precision agriculture and currently is generating huge volumes of raw data from
IoT sources such as: chemical sensors, electro-chemical sensors, drones, weather
stations and so on. Those thousand of lines raw data are meaningless and isolated,
and therefore they do not add extra knowledge to the farmer. Agriculture activities
are based on a multiparameter knowledge with many interconnections between the
parameters. The efficacy of data derives from context and meaning, as well as its
combination with other data from different agriculture sources. Semantic technologies can provide practicality to the agriculture/farming data by providing common
interchange data formats. Also, through SW new knowledge can be provided through
the use of reasoners.
Semantic resources are typically divided in two different categories for general agriculture or specialized domains of agriculture. Several significant agriculture
ontologies are OntoAgroHidro [72], Crop ontology [73], GCP ontology [74], Agroportal [75], Agricultural Technology Ontology [76], Citrus Ontology [77], Agriculture Activity Ontology (AAO) [78], AgOnt [79], Agronomy Ontology [80]. Projects
like Agrovoc [81] consists of +36,000 concepts and +750,000 terms in up to 35
languages, have provided with structured vocabularies for the agriculture domain.
The Global Agricultural Concept Scheme (GACS) [82] contains in its f iles of interoperable concepts the schemes related to agriculture from AGROVOC multilingual
agricultural thesaurus (35,000 concepts), the CAB Thesaurus [83] (140,000 concepts) and the NAL Thesaurus [84] (53,000 concepts).
Agriculture requires common data schemes for semantic web technologies to
render plausible the transfer of semantically described data and the development of
common ontologies. One such a standard is known as: The Agricultural Metadata
Element set (AgMes) [85]. AgroRDF [86] is one of the major standards for data
exchange, which is designed specifically for agricultural data. The applications with
agriculture semantic technologies are divided mainly into those different categories:
Knowledge based systems, Remote Sensing, Decision Support and Expert Systems
[87].