Backpropagation algorithm for rice yield prediction

Abstract

Parameters that affect rice yield are many, for instance diseases, pests and weeds. Statistical or mathematical model is unable to describe the correlation between plant diseases, pests and weeds on the amount of rice yield. In this study, a Backpropogation (BP) algorithm is utilized to develop a neural network model to predict rice yield based on the aforementioned factors in MUDA irrigation area, Malaysia. The result of this study shows that the BP algorithm is able to predict the rice yield to a deviation of less than 0.21.