A Hybrid Classifier Approach to Multivariate Sensor Data for Climate Smart Agriculture Cyber-Physical Systems


In this paper, we propose a novel climate-smart Agriculture Cyber-Physical System (ACPS) for precision farming. The primary motive of the ACPS is to perform real-time fault location tracking in the agricultural field using multivariate sensor data. The computing model in the ACPS uses a novel hybrid classification approach which combines two classifiers for the location estimation of the sensor node. The novelty of the proposed method lies in predicting the locations that need more irrigation, soil nutrients or immediate human intervention using the sensor data. We also derive the computational complexity of the proposed method. The location accuracy improves reasonably as compared to the current-state-of-the-art methods.

Meeting Name

20th International Conference on Distributed Computing and Networking, ICDCN '19 (2019: Jan. 4-7, Bangalore, India)


Computer Science

Research Center/Lab(s)

Intelligent Systems Center

Second Research Center/Lab

Center for High Performance Computing Research


The work was supported in part by Science and Engineering Research Board (SERB), Government of India, Early Career Research project (ECR/2016/001532) titled "Cyber-Physical Systems for MHealth".

Keywords and Phrases

Agriculture; Cyber Physical System; Embedded systems; Learning systems; Location; Sensor nodes; Wireless sensor networks; Agricultural fields; Human intervention; Hybrid classification; Hybrid classifier; Location estimation; Multivariate sensors; Smart agricultures; State-of-the-art methods; Distributed computer systems; Cyber-Physical Systems; Machine Learning

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


File Type





© 2019 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

01 Jan 2019