Semantic and Syntactic Interoperability for Agricultural Open-Data Platforms in the Context of IoT Using Crop-Specific Trait Ontologies  
[PERSON]  
1 Department of Management Information Systems, Kadir Has University, Istanbul 34083, Turkey; [EMAIL_ADDRESS]  
Footnote 1: email: {[EMAIL_ADDRESS]}  
27 May 202024 June  
which are widely used in the agriculture domain, help improve efficient and safety agricultural production [4]. Advancement in sensor technologies leads to an increase in the usage of WSN in precision agriculture. WSN-based agricultural systems, which are supported with mobile-based or web-based data management tools can improve the productivity of farmers by providing mixed crop farming and ubiquitous computing [5]. Most meteorological factors, environmental and site-specific data can be obtained by utilizing the sensor technologies. Farmers--who play a key role in agricultural production cycle--can benefit by collecting, processing, providing and using data efficiently [6]. In other words, it is necessary to provide farmers with relevant data related to all agricultural production activities to enable effective decision making. Considering the variability of environmental and site-specific data throughout the agricultural production cycle, processing and managing such data obtained from sensors is crucial for PA. In the agricultural domain, there can be varied data obtained from farmers, sensors, markets and government stats related to agricultural products [7]. Processing, integrating and exporting data collected from such heterogeneous sources can be provided by using open-data platforms. Considering the lack of research endeavors on the application or platform level for ensuring integrated web-accessible data in a unified way, it is clear that more focus is needed on openly distributed platforms, which facilitate integrating sensor data and understanding the exact meaning of integrated data [8]. Ontologies enable us to share a common understanding of the structure of information and to allow communication among people or heterogeneous and widely spread application systems [9; 10]. This research is aimed to develop an ontology-based data management (OBDM) system. OBDM which consists of ontology, mapping and data source layers, makes a link between ontologies and data sources using a formal way [11]. These layers have a vital role while performing the data integration processes for agricultural open-data platforms. Therefore, a data-integration approach is proposed in this study. Data integration components and tools of open-data platform are developed based on the proposed data-integration approach. The proposed approach examines data integration processes between IoT devices and open-data platforms at syntactical and semantic interoperability levels. With this approach, it shall be focused on how to use crop-specific trait ontologies for achieving semantic interoperability. The motivation of proposing this approach is two-fold. First, this approach illustrates how agricultural data are gathered from IoT devices using varied sensors in WSNs; how the gathered data are integrated to the open-data platform; and how to publish these data through web services for consumption by domain stakeholders. Second, this approach depicts how to annotate agricultural data using semantic web technologies. In addition, this approach constructs a framework to create data models for object relational mapping for data storage options. Domain ontologies can be used to annotate data gathered from devices with the contextual information, thus providing well understood data by the users and allowing data interoperability [12]. In this study, hazelnut trait ontology, which plays the domain ontology role of open-data platform and is introduced in the implementation section, is used to make connections between sensor measurement values and site-specific parameters of agricultural products. Furthermore, it facilitates transforming mapped sensor data to linked-open sensor data as well.