Comparison Study Using Arima and Ann Models for Forecasting Sugarcane Yield

¹ University of Alkafeel, Najaf, Iraq
² PSG College of Arts and Science, Tamil Nadu, India
³ Centre for Water Resources Development and Management, Kozhikode, India
⁴ Centurion University of Technology and Management, Odisha, India
⁵ JNKVV College of Agriculture, Rewa, India
⁶ South Ural State University, Chelyabinsk, Russia

^* Corresponding author: ali.j.r@alkafeel.edu.iq

Abstract

Sugarcane is the largest crop in the world in terms of production. We use sugarcane and its byproducts more and more frequently in our daily lives, which elevates it to the status of a unique crop. As a result, the assessment of sugarcane production is critical since it has a direct impact on a wide range of lives. The yield of sugarcane is predicted using ARIMA and ANN models in this study. The models are based on sugarcane yield data collected over a period of 56 years (1951-2017). Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) have been used to analyze and compare the performance of different models to obtain the best-fit model. The results show that the RMSE and MAPE values of the ANN model are lower than those of the ARIMA model and that the ANN model matches best to this data set.