Fog Computing for Agriculture Applications and its Issues

Abstract

A significant increase in the Internet of Things (IoT) applications is due to an emerging concept known as smart agriculture, which plays an important role in increasing various farming activities, such as crop cultivation and growth and horticulture. Smart agriculture can be defined as a method that uses modern technology to improve yield in the quantity and quality of agricultural products. Traditional cloud-based systems with IoT models could not handle the traffic and overflow database. At the same time, fog computing works to improve the quality, quantity, long-term viability, and cost-effectiveness of agricultural production. The use of integrated cloud computing (CC) has several drawbacks that eventually lead to network failures, such as unstable and long-delay links between agricultural appliances and cloud data bearers. As a result, fog computing has been deployed to address the aforementioned issue, where it links up with IoT-based models to address the aforementioned issues. The data generated by IoT devices are pushed to the cloud server via fog computing. Compared with CC, it provides high-quality services that are delivered quickly. In this chapter, the characteristics of fog computing, its architecture, agricultural applications, issues, and other related topics are reviewed. Keywords: fog computing, agriculture, cloud computing, machine learning