Author: Gaganpreet Singh Hundal (Trine University) - The adoption of IoT based precision or sustainable agriculture technologies such as variable rate irrigation (VRI) & variable rate fertilization (VRF) has seen an increasing trend with 20 % increase in adoption in the last five years among the US mid-western row crop producers. However, there are cost, operational, technical and data management barriers which discourage the adoption of IoT-based precision agriculture technologies. There is no previous study on IoT systems design framework with dearth of literature that defines decision variables and how they are related to each other. Therefore, it is important to develop a framework which can help to understand the relationships among the decision variables based on adoption barriers for IoT systems design to achieve sustainable agriculture applications. In this study, technical, operational, data management and cost barriers are analyzed for defining decision variables and their relationship among each other proposing an IoT system design framework. Interpretive structural modeling (ISM) methodology is used to analyze the relationship among key performance decision variables identified through focus group interviews in the previous study. Power consumption, cost, data scalability, data latency, data interoperability, data processing, data storage, type of precision agriculture application, type of wireless communication and communication range of IoT devices are the decision variables identified, and a cluster analysis is performed leading to an ISM framework for IoT systems design depicting relationships among variables. The type of precision agriculture application, type of wireless communication, data processing and power consumption requirements are identified as key decision variables which are highly related to other variables and are important to quantify and analyze before designing IoT systems.
