## 6 Conclusions  
This study sets out to propose an approach to integrating data collected from IoT devices to agricultural open-data platforms from the perspective of semantic and syntactic interoperability. The proposed data-integration approach has enhanced an understanding of data integration lifecycle for agricultural IoT applications in the context of open-data platforms. The general tendency to provide data integration for data gathered from IoT devices is to use existing geospatial ontologies. However, this study has demonstrated, for the first time that a crop-specific trait ontology by constructing mapping rules was used to annotate the environmental data for a particular agricultural product with the aim of achieving semantic interoperability. The most obvious finding to emerge from this study is that semantic interoperability for data collected from IoT devices can be carried out using a crop-specific trait ontology by constructing mapping rules. Another important finding is to demonstrate how to use web services and APIs to provide syntactical interoperability for data gathered from IoT devices using crop-specific trait ontologies. This research demonstrates the value of the proposed approach along with its implementation of software applications. First, the open-data platform which is developed based on the proposed approach enables agricultural domain stakeholders export linked open data in varied open formats or standards. It provides a visualization infrastructure for IoT devices and their data. Thus, stakeholders could access and export data gathered from IoT devices belonging to a particular wireless sensor network established for the relevant agricultural product. Second, web services and APIs allow stakeholders to share data about a particular agricultural product between different kinds of software applications. By this means, syntactical interoperability was carried out through these services and APIs. The open-data platform was evaluated in terms of usability considering five different scales such as efficiency, affect, helpfulness, control and learnability by using SUMI questionnaire [65]. According to the usability results, the developed platform has satisfactory scores for each one of them. Furthermore, the score of global usability scale is quite reasonable for the open-data platform. The implementation part of this study focused on hazelnut agricultural product. However, the proposed approach is appropriate for different types of crop-specific trait ontologies, which are created \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **n** & **Usability** & **Efficiency** & **Affect** & **Helpfulness** & **Control** & **Learability** \\ \hline Very experienced & & & & & & & \\ and technical & 19 & 69 & 64.4 & 63.9 & 64.9 & 65.6 & 59.1 \\ \hline I am experienced, & & & & & & & \\ but not technical & 2 & 60.5 & 52.5 & 63.5 & 54 & 57 & 51 \\ \hline I can cope with & & & & & & & \\ most software & 6 & 65.8 & 63.5 & 66.8 & 60.3 & 64.8 & 51.5 \\ \hline I find most software & & & & & & & \\ difficult to use & 0 & – & – & – & – & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Respondents’ profiles in terms of software skills and technical knowledge. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **n** & **Usability** & **Efficiency** & **Affect** & **Helpfulness** & **Control** & **Learability** \\ \hline Extremely & & & & & & & \\ important & 21 & 67.9 & 63.7 & 64 & 63.2 & 65.1 & 57.9 \\ Important & 6 & 66.8 & 62 & 66.2 & 62.5 & 63.8 & 53 \\ Not very important & 0 & – & – & – & – & – & – \\ Not important at all & 0 & – & – & – & – & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Importance of the platform for respondents. using OWL and includes site-specific variables. The present study has only examined semantic and syntactic interoperability. However, further research may investigate other interoperability layers for open-data platforms.