Smart Farming Irrigation Systems using Internet of Things
Description
The data was collected through a questionnaire that was administered to participants to capture their opinions, experiences, and perceptions on IoT Smart Farming Irrigation Systems. A panel consisting of 10 experts participated in a Delphi Survey exercise. This survey was conducted in two iterative questionnaire rounds with each round building on the responses from the previous round to refine the data. Inductive coding method was used to analyze the textual responses from the Delphi panel of experts. The initial phase of the coding process involved reading the data and identifying key phrases that participants mentioned. The initial codes were subsequently reduced into categories and duplicates were removed to refine the data for further analysis. This was finally followed by developing a hierarchical coding framework the themes that emerged from the responses. The panel of experts completed all iterative rounds with their informed consent. Participants had the right to withdraw from the Delphi survey at any stage during the exercise.
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For this study, the Delphi technique often referred to as the Delphi survey was used to gather qualitative data from a panel of 10 experts in the IoT smart farming systems domain. This technique allowed a group of experts to reach a consensus on opinions through expressing their views and experiences on the subject matter under investigation. In this situation, a questionnaire consisting of open-ended questions was sent to a panel of experts. Upon completion, the responses were summarized and provided to participants for review before proceeding to the next round. Content analysis was used to analyze the data obtained from open-ended questions distributed to the Delphi panel of experts. The initial phase in the content analysis involved an iterative process of re-reading and going through the data obtained to gain an understanding of participants opinions and experiences on smart farming irrigation systems using IoT. This was followed by identifying codes and themes. These codes were grouped into clusters around similar and interrelated ideas and concepts. Inductive coding approach was applied where the researchers derived the codes from data as opposed to starting with predefined set of codes usually derived from previous research studies. A hierarchical coding structure was created that captures how the codes relate to each other.