Affiliations: [a] Department of Information and Communications Engineering, University of Murcia, Murcia, Spain. E-mails: jcadenas@um.es, carmengarrido@um.es | [b] Department of Computer Engineering, Catholic University of Murcia, Murcia, Spain. E-mail: rmartinez@ucam.edu
Abstract: Precision agriculture has different strategies to collect, process and analyze different types and nature data to be able to make decisions that improve the efficiency, productivity, quality, profitability and sustainability of agricultural production. Specifically, crop sustainability is directly related to reducing costs for farmers and minimizing environmental impact. In this paper, an application to help in the decision making about the most convenient type of crop to plant in a certain zone is developed, taking into account the climate conditions of that zone, in order to make a sustainable crop. This application is integrated within the Internet of Things system, which can be adapted and parameterized for any kind of crop and zone. The Internet of Things system components are described in detail and a fuzzy clustering model is proposed for the system’s intelligent module. This fuzzy model focuses on making a zone grouping (management zones), taking into account the zone climate conditions. The model manages fuzzy data, which allows us more extensive information and a more natural data treatment. A real study case of the proposed application is presented using data from the Region of Murcia (Spain). In this study case, the entire deployed Internet of Things system has been described, the fuzzy model to group similar areas in terms of meteorology has been validated and evaluated and the recommendation module has been implemented, taking into account the actual production data and the needed resources for the crops in the Region of Murcia (Spain).
Keywords: Precision agriculture, sustainable agriculture, intelligent data analysis, clustering analysis
