Fog Data Processing and Analytics for Agriculture IoT Data Streams

Abstract

Pervasiveness rise of smart devices and sensor-based gadgets in building IoT systems is increasing unprecedented in technological innovations. The emergence of the Internet of Things technologies reshaped nearly every sector including agriculture. The agriculture sector contributes a significant figure to the country’s economy and it has a wide-ranging involvement in the advancement of human civilization. In the current scenario, the proper techniques of farming are needed as utmost propriety for better crop quality and quantity in a high-competition market. Crop disease prediction is key to shattering the problems of the farmer, reducing the usage of insecticides, and pesticides, and improving the financial conditions of the farmer. The Internet of Things and data analytics possess the ability to positively modernize the agricultural sector. However, Internet of Things-based applications needed to be deployed on a platform that offers real-time experience, reduced latency, and optimal bandwidth usage. Fog computing extends the computational power closer to the edge network where the devices reside and facilitate edge intelligence. In this paper, the fog importance, fog computing architecture along with the perceptions of data analysis in a fog environment, and emerging research challenges are discussed. Furthermore, in this paper an IoT-Fog based framework for the prediction of crop disease is proposed. The proposed framework consists of four phases, sensor layer, fog layer, cloud layer and End-user. The proposed framework may be beneficial in the farming domain for the analysis of crop disease prediction in the early stage and may reduce the chances of disease outbreaks in the field