Design and application of an ontology to identify crop areas and improve land use

Agricultural development in Colombia has been characterized by being carried out in a local and traditional way, where important basic aspects are not always considered for the best performance of crops. These characteristics are presented in official government documentation distinguished by its he...

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Bibliographic Details
Published in:Acta geophysica 2023-06, Vol.71 (3), p.1409-1426
Main Authors: Riaño, Maddyzeth Ariza, Rodriguez, Andres Ovidio Restrepo, Velandia, Julio Barón, García, Paulo Alonso Gaona, Marín, Carlos Enrique Montenegro
Format: Article
Language:English
Subjects:
Agricultural development
Agriculture
Applications programs
Crop development
Crops
Cultivation
Documentation
Domains
Earth and Environmental Science
Earth Sciences
Geophysics/Geodesy
Geotechnical Engineering & Applied Earth Sciences
Heterogeneity
Homogeneity
Land use
Ontology
Research Article - Special Issue
Structural Geology
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Summary:Agricultural development in Colombia has been characterized by being carried out in a local and traditional way, where important basic aspects are not always considered for the best performance of crops. These characteristics are presented in official government documentation distinguished by its heterogeneity. Ontologies in the domain of agriculture allow the organization and structuring of information to represent knowledge in such a way that the homogeneity of agricultural data dispersed in different types of documents such as manuals, weather reports, and official technical sheets is achieved. In accordance with the above, this work presents the development of an ontology in the agricultural domain to facilitate the identification of cultivation areas and improve land use, relating the basic concepts for an effective crop development according to the specifications and recommendations proposed in Colombian government documentation, using the Methontology methodology. This is achieved with the application of descriptive logic that, based on rules, generates inferences to identify the cultivation options and cultivable areas that present the highest performance. The interaction and use of the ontological model and inference rules are done through a web application made with Python and Flask. The precision of the model is evaluated using historical data of crops produced, making a comparison between the real data and the results obtained through the ontological model, obtaining as a result 80% reliability.
ISSN:1895-7455
1895-6572
1895-7455
DOI:10.1007/s11600-022-00808-5