Rice yield prediction and optimization using association rules and neural network methods to enhance agribusiness

Abstract

Objectives: This study aims to implement data analytics and machine learning approaches to rice data and to establish association rules on fixed attributes and their correlations for yield prediction of crops. Methods: The data of rice crop is collected from district Larkana as per defined parameters: area, production, yield, temperature, rainfall, humidity and wind speed. The pre-processing operations are applied on prepared dataset to execute data analytics and machine learning algorithms. The processed data are then input into an Apriori algorithm for generating association rules. Neural network model is used to perform optimization on resulted association rules. Findings: Minimum support and confidence values equal to 3 and 80 respectively were set using Apriori algorithm on prepared rice dataset and obtained 88 association rules. Among them, results of 28 associated rules predicted `High Yield Production\'. Neural network model is experimented to optimize the predicted yield of district Larkana through which overall network performance of 97.8% is calculated. Previously, rice yield data of Larkana were not statistically and digitally predicted and investigated. Application: Group of effective and well-built association rules of yield prediction are core outcome of this study which will be helpful for researchers, farmers and government officials to improve the productivity of rice crop. Keywords: Rice data sets; Association rules; Yield prediction; Data optimization; Neural network