TY - GEN

T1 - Connectivity Conceptual Modelling for Plant Agriculture Artificial Intelligence Information Systems

AU - Kalichkin, Vladimir

AU - Koryakin, Roman A.

AU - Maksimovich, Kirill

PY - 2023/1/10

Y1 - 2023/1/10

N2 - Progressive development of intellectual and expert information systems in plant agriculture requires more fundamental knowledge about local land agro- and ecosystem unique features, especially for regions with extreme climate conditions like Northern Asia. Each farm agriculture complex needs a lot of specific customization for digital technology applications which rises a need for effective knowledge base organization to perform an efficient data analysis and simulation modelling. For this purpose, conceptual modeling of spatial land characteristics was conducted using semantic network model. Formal modeling language UML was applied to fix 46 classes, attributes and relations as main abstract objects for agriculture land characteristic ontologies. Basing on which and independently of expert knowledge, a variety of 11 218 UML methods was designed and described. Upon expert consideration of the research, 7 types of data dependencies were classified, each of them allowing to calculate one given land characteristic using collected data for other ones. Results reveal clear classification of trajectories to build a digital image of agricultural land saving all possible variants for simulation modeling interpretations. Generalized semantic network for agricultural intellectual information system development is presented containing 36 basic entities and separating real agriculture from its digital image.

AB - Progressive development of intellectual and expert information systems in plant agriculture requires more fundamental knowledge about local land agro- and ecosystem unique features, especially for regions with extreme climate conditions like Northern Asia. Each farm agriculture complex needs a lot of specific customization for digital technology applications which rises a need for effective knowledge base organization to perform an efficient data analysis and simulation modelling. For this purpose, conceptual modeling of spatial land characteristics was conducted using semantic network model. Formal modeling language UML was applied to fix 46 classes, attributes and relations as main abstract objects for agriculture land characteristic ontologies. Basing on which and independently of expert knowledge, a variety of 11 218 UML methods was designed and described. Upon expert consideration of the research, 7 types of data dependencies were classified, each of them allowing to calculate one given land characteristic using collected data for other ones. Results reveal clear classification of trajectories to build a digital image of agricultural land saving all possible variants for simulation modeling interpretations. Generalized semantic network for agricultural intellectual information system development is presented containing 36 basic entities and separating real agriculture from its digital image.

UR - https://www.scopus.com/inward/record.url?eid=2-s2.0-85146507075&partnerID=40&md5=349ed0ce9e859ec10b4590ff5bd1bfe5

UR - https://www.mendeley.com/catalogue/2781da85-add5-3c51-878f-82042a03eb49/

U2 - 10.1063/5.0113836

DO - 10.1063/5.0113836

M3 - Conference contribution

SN - 9780735442795

T3 - AIP Conference Proceedings

BT - AIP Conference Proceedings

PB - American Institute of Physics Inc.

ER -