Applications of Auto-regressive Integrated Moving Average (ARIMA) Model in Agricultural Engineering: A Review

Abstract

In recent years, the use of cutting-edge statistical techniques has significantly enhanced the precision and effectiveness of agricultural practices. The auto-regressive integrated moving average (ARIMA) model has emerged as a highly effective and potent tool for the time series analysis and prediction of various subsets of agricultural engineering viz. prediction of agricultural yield, weather forecasting, water management, pest and disease outbreak prediction, livestock production forecasting, soil health monitoring and market analysis and price prediction. Due to its capacity to identify and evaluate temporal patterns, seasonality and trends in agricultural data, ARIMA was identified as a useful model for tackling complicated problems in agricultural engineering. This paper demonstrates the adaptability of ARIMA in offering precise forecasts and insightful information for agricultural systems decision-making processes through an extensive examination of the literature and case studies. The primary objectives of this review are to: (1) Analyze the use of ARIMA models in forecasting agricultural indicators such as crop yields, irrigation water needs and pest population dynamics; (2) Explore the integration of ARIMA with climatic and environmental datasets to support precision agriculture and (3) Evaluate its role in optimizing resource use and improving strategies for managing agricultural risks. The findings suggest that ARIMA models are effective tools for accurate short-to medium-term forecasting of key variables like irrigation needs, crop yields, soil moisture and climate conditions in the field of agricultural engineering. These forecasts foster decision-making, efficient resource use and reduced environmental impact. Additionally, hybrid approaches that combine ARIMA with machine learning techniques have shown further improvement in their performance, underscoring their adaptability and value in data-driven agricultural practices. ARIMA is combined with other models like LSTM, ANN, or clustering methods to capture nonlinearities and enhance prediction accuracy. Overall, ARIMA remains a foundational tool in agricultural forecasting, offering robust support for data-driven agricultural engineering practices.