Precision Agriculture using Data Mining Techniques and IOT

Abstract

The objective of the paper is to improve the crop yield in agriculture by measuring the factors affecting, by reducing the consumption and labor work, increase the crop productivity using IOT smart farming and Data mining techniques. Precision agriculture makes use of the technology like IOT sensors, GPS services for communication, (M2M) machine to machine and Data mining techniques. The research concentrates on the extension of two paper which focuses on improving the agriculture using data mining techniques such as DBSCAN, PAM, CLARA, Chameleon, regression techniques. The extended work focuses on identifying the critical factors from the results of clustering techniques and adapting the different sensors for each cluster associated with critical factors. The results include analysis of the cluster results and selection of sensors based on critical factors. Also provides an overview of the selection of sensors.