INTERNET OF THINGS BASED SMART AGRICULTURE SYSTEM USING PREDICTIVE ANALYTICS

Suhas M Patil; Sakkaravarthi R

doi:10.22159/ajpcr.2017.v10s1.19601

Authors

Suhas M Patil School of Computing Science & Engineering, VIT, Chennai, Tamil Nadu, India.
Sakkaravarthi R School of Computing Science & Engineering, VIT, Chennai, Tamil Nadu, India.

DOI:

https://doi.org/10.22159/ajpcr.2017.v10s1.19601

Keywords:

Wireless sensor networks, Internet of things, Data compression, Kalman filter, Prediction analytics

Abstract

Due to the use of internet of things (IoT) devices, communication between different things is effective. The application of IoT in agriculture industry
plays a key role to make functionalities easy. Using the concept of IoT and wireless sensor network (WSN), smart farming system has been developed
in many areas of the world. Precision farming is one of the branches comes forward in this aspect. Many researchers have developed monitoring and
automation system for different functionalities of farming. Using WSN, data acquisition and transmission between IoT devices deployed in farms will be easy. In proposed technique, Kalman filter (KF) is used with prediction analysis to acquire quality data without any noise and to transmit this data for cluster-based WSNs. Due to the use of this approach, the quality of data used for analysis is improved as well as data transfer overhead is minimized in WSN application. Decision tree is used for decision making using prediction analytics for crop yield prediction, crop classification, soil classification, weather prediction, and crop disease prediction. IoT components, such as and cube (IOT Gateway) and Mobius (IOT Service platform), are integrated in proposed system to provide smart solution for crop growth monitoring to users.

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