Sequential pattern mining combined multi-criteria decision-making for farmers’ queries characterization

Abstract

Agricultural policymakers use various types of expert systems to identify agricultural problems and to explore their potential solutions. However, in the current scenario, there is no robust system that can be used to collect and analyze information regarding the problems faced by farmers of the developing countries on a large scale. This article outlines the possible mechanisms through which information and communication technology (ICT) with the use of Knowledge Discovery in Databases could facilitate agricultural adoption. The goal of this study is to explore data from a farmers’ helpline center as a new medium to gain hidden insights in terms of association rules regarding the problems faced by Indian farmers. The dataset used in this study is collected from the “Kisan Call Center”, a farmers’ helpline center managed by the Ministry of Agriculture, Government of India. For this objective, we propose a new approach that uses association rule mining integrated with a multi-criteria decision-making technique, TOPSIS to extract only the most relevant patterns from the dataset. Later, we perform experiments in order to analyze the output of the proposed framework and verify the discovered knowledge against the validation data. The best experiment generates a rule-set, consisting of 702 association rules, including insights from 25 states of India, with an average confidence value of 73.21% on the validation data. The extracted inference reveals many hidden patterns regarding associations among the farmers’ issues from the remote states of India. Finally, we identify various potential applications of our work and conclude with some possible future developments in the proposed approach.