Big Data Analytics and Machine Learning Approach for Smart Agriculture System Using Edge Computing

Abstract

With the development of the Internet of Things (IoT) and machine learning technology, a smart agriculture environment produces more agricultural land and crop-associated data for knowledge discovery systems. Machine learning decision-making algorithm is applied to discover hidden knowledge patterns from the agricultural data stored in the distributed database. Big data analytics extract useful information from the large, distributed, and complex datasets, which helps the farmer to increase crop yield and quality of the production. The edge computing node collects crop data and land environment data from the agricultural lands using a different kind of IoT sensors. The predicted smart agricultural knowledge pattern can provide needed information to the farmers and other users like an agent, agriculture officers, researchers, and producers to get more profit. Cloud and fog computing provides efficient distributed data storage for big data and execute dynamic operations to predict business intelligence facts to increase production and minimize natural resource utilization. We have compared traditional data mining techniques with the business analytical tool hybrid association rule-based decision tree (HDAT) MapReduce approach for implementing decision tree algorithm to predict and forecast the future needs of the farmer to increase the profit and reduce the resource wastage.