PREDICTIVE MODELLING FOR SUGARCANE PRODUCTION: A COMPREHENSIVE COMPARISON OF ARIMA AND MACHINE LEARNING ALGORITHMS (Applied Biological Research, 2024)
Accurate prediction of sugarcane yield is essential for trade, economic planning,  and sustainable agriculture in India. This study addressed the challenge of  forecasting sugarcane yield by evaluating the effectiveness of time series  modelling and machine learning algorithms. Leveraging data spanning from  2001 to 2020, the research focuses on predicting the sugarcane yield for the  subsequent years. The problem statement revolves around the need for precise  yield predictions to inform decision-making in the agricultural sector. Methods  employed included the utilization of Autoregressive Integrated Moving Average  (ARIMA) for time series analysis and machine learning algorithms such as  Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting  Machine (GBM). The analysis encompassed sugarcane yield data spanning  multiple years, with predictions extending for a specified duration. Through analysis of temporal patterns and dependencies within the sugarcane yield time  series data using Autocorrelation Function (ACF) and Partial Autocorrelation  Function (PACF), the study optimized the predictive models. Results indicated  that ARIMA outperformed machine learning algorithms, exhibiting superior  performance with a root meansquare error of 36700.68 anda minimumAICvalue  of 456.7. The study emphasizes the significance of accurate yield predictions for  agricultural planning and decision-making, highlighting the implications for  sustainable crop management and the fortification of Indian sugar industry.The study affirms the importance of informed decisions facilitated by accurate yield  predictions in resilient agricultural sector. Overall, this study contributes to the  advancement of sugarcane yield prediction, offers practical insights for  stakeholders and policymakers in India's agricultural landscape.
Keywords: ARIMA; gradient boosting machine; machine learning methods; random forest; sugarcane yield prediction; support vector machine