IRRISENS: An IoT Platform Based on Microservices Applied in Commercial-Scale Crops Working in a Multi-Cloud Environment

Research has shown the multitude of applications that Internet of Things (IoT), cloud computing, and forecast technologies present in every sector. In agriculture, one application is the monitoring of factors that influence crop development to assist in making crop management decisions. Research on the application of such technologies in agriculture has been mainly conducted at small experimental sites or under controlled conditions. This research has provided relevant insights and guidelines for the use of different types of sensors, application of a multitude of algorithms to forecast relevant parameters as well as architectural approaches of IoT platforms. However, research on the implementation of IoT platforms at the commercial scale is needed to identify platform requirements to properly function under such conditions. This article evaluates an IoT platform (IRRISENS) based on fully replicable microservices used to sense soil, crop, and atmosphere parameters, interact with third-party cloud services for scheduling irrigation and, potentially, control irrigation automatically. The proposed IoT platform was evaluated during one growing season at four commercial-scale farms on two broadacre irrigated crops with very different water management requirements (rice and cotton). Five main requirements for IoT platforms to be used in agriculture at commercial scale were identified from implementing IRRISENS as an irrigation support tool for rice and cotton production: scalability, flexibility, heterogeneity, robustness to failure, and security. The platform addressed all these requirements. The results showed that the microservice-based approach used is robust against both intermittent and critical failures in the field that could occur in any of the monitored sites. Further, processing or storage overload caused by datalogger malfunctioning or other reasons at one farm did not affect the platform’s performance. The platform was able to deal with different types