Fog Computing Enabled Hydroponic Farming Systems

Abstract

Intelligent hydroponic farming that leverages IoT advantages is a pattern of modern farming technology as it not only increases crop productions but also reduces negative impacts from traditional methods. This paper proposed a fog computing enabled hydroponic farming framework that devises low-cost data collection and novel data analysis mechanisms to deliver intelligent farming systems. In this framework, the data from multiple IoT sensors at the garden are collected, filtered and analyzed by artificial neural network (ANN) models deployed at the fog landscapes, while the ANN models are trained in the cloud with a large amount of historical farming data. This approach allows the intelligent models being updated, reducing the communication cost and response time, while utilizing computing resources available on the network edge. The evaluation results on the developed prototype depict the effectiveness and the performance of the proposed approach revealing that it is feasible and ready to be applied in real-world applications.