Input Value Chain Affect Vietnamese Rice Yield: An Analytical Model Based on a Machine Learning Algorithm

Abstract

Input value chains greatly affect rice yield, however previous related studies were mainly based on empirical survey and simple statistics, which lacked generality and flexibility. The article presents a new method to predict the influence of input value chain on rice yield in Vietnam based on a machine learning algorithm. Input value chain data is collected through field surveys in rice-growing households. We build a predictive model based on the neural network and swarm intelligence optimization algorithm. The prediction results show that our proposed method has an accuracy of 96%, higher than other traditional methods. This is the basis for management levels to have orientation on the input supply value chain for Vietnamese rice, contributing to the development of the Vietnamese rice brand in the world market.