## 2 Related Works  
The term IoT, which is first used in 1999, is characterized in three visions such as Internet-oriented, things-oriented and semantic-oriented [13; 14]. It also consists of three components which enable ubiquitous computing such as hardware, middleware and presentation [15]. Due to having such features, IoT-based applications are used in a wide range of sectors such as discrete manufacturing, transportation, logistics, utilities, healthcare, energy and natural resources, retail, insurance and agriculture. This leads to a dramatic rise in the number of connected IoT devices and the amount of data generated [16]. However, there still exist varied issues to handle with respect to IoT, such as standards, scalability, device diversity, constructing generally accepted service language, finding service for a particular domain and providing interoperability [17]. This study mainly focuses on semantic and syntactic interoperability in the context of IoT. Understanding the true meaning of data by utilizing the semantic web technologies is a key factor, which simplifies providing interoperability among IoT components. Semantic web technologies are also used for creating data models and integrating data gathered from heterogeneous IoT devices. This approach constitutes an enabling-technology in different IoT-related fields, like Industry 4.0 and smart manufacturing [18], smart and IoT-enhanced environments [19] and healthcare [20]. Annotating IoT data using the strength of semantic web technologies ensures the interoperability between IoT applications [21]. Utilizing IoT data, which are obtained from heterogeneous data sources, requires more complex operations and technical skills due to interoperability issues. Considering the heterogeneity of IoT data, semantic web technologies can be used for sharing data gathered from heterogeneous IoT devices by generating \"common models\" [22] of domains of knowledge--i.e., an ontology, shared and explicit conceptualization of the knowledge and of the relationships of the concepts composing a domain [23; 24]. The diversity of IoT devices from several vendors can uncover semantic and syntactic errors [20]. However, IoT data are generally bound up with heterogeneous data models; and to overcome this heterogeneity, it is necessary to make data available homogeneously to allow integration from a wide variety of sources [8]. Due to the aforementioned reasons, one needs to take a closer look at interoperability in IoT. One can examine various interoperability levels [25; 26; 27; 28; 29], but since syntactic and semantic levels are particularly essential for data integration, we focus on these two levels. Syntactic interoperability is concerned with software infrastructure needed, whereas semantic level addresses the meaning of data while sharing or exchanging data between systems [30]. It is worth bearing in mind that implementing semantic interoperability requires more effort such as developing an ontology and utilizing it to meet the interoperability requirements [31]. Table 1 summarizes representative studies on ontologies in the context of IoT and sensors. These ontologies generally aim to model resources, services and location information; however, new ontologies are developed to meet interoperability requirements as well [32]. The overall purpose of these ontologies is to provide robust solutions to heterogeneity issues regarding hardware, software and the data management for IoT devices [33]. Each one of the ontologies represented in Table 1 provides significant and valuable contribution to solve the interoperability issues in IoT applications. However, they are not aimed to links between the mapping domain-specific variables that needed crop-specific trait ontologies and sensor measurements. Furthermore, crop-specific trait ontologies can be used to create, publish and share linked open data concerning a particular agricultural product on the web-based platforms as well.