Title: Modelling big data analysis approach with multi-agent system for crop-yield prediction

Authors: Jaya Sinha; Shri Kant; Megha Saini

Addresses: Department of Computer Science and Engineering, Galgotias College of Engineering and Technology, Greater Noida, India ' Research and Technology Development Centre, Sharda University, Greater Noida, India ' Department of Information Technology, Galgotias College of Engineering and Technology, Greater Noida, India

Abstract: Big data environment in current scenario is dealing with challenges in handling inherent complexity residing in the massive heterogeneous, multivariate and continuously evolving real-time data along with offline statistics. The role of big data analytics to analyse such a highly diverse data also plays a significant role in estimating predictive performance of a system. This paper thus aims at proposing an intelligent agent-based architecture that coordinates with big data analytics framework to model a system with an objective to improve the predictive performance of system by handling such diverse data. The paper also includes implementing predictive algorithm to predict crop yield in the agricultural domain. Various machine learning analytical tools have been used for data analysis to produce comprehensive and more accurate prediction using the proposed architecture.

Keywords: multi-agent system; MAS; big data; data acquisition; data analysis; data storage; machine learning; intelligent agents.

DOI: 10.1504/IJIDS.2023.129657

International Journal of Information and Decision Sciences, 2023 Vol.15 No.1, pp.27 - 45

Received: 01 Oct 2020
Accepted: 23 Jan 2021
Published online: 20 Mar 2023 *