Deep Learning Python-Based Time-Series Model for Oil Palm Yield Prediction - Ang, Yuhao; Mohd Shafri, Helmi Zulhaidi; Lee, Yang Ping; Bakar, Shahrul Azman; Abidin, Haryati; Mohd Junaidi, Mohd Umar Ubaydah; Lim, Hwee San; Abdullah, Rosni; Yusup, Yusri; Muhammad, Syahidah Akmal; et al. (Applications of Modelling and Simulation, 2025)
The application of time-series analysis in agricultural yield forecasting has gained much attention in recent years with the use of machine learning models. However, the utilization of Python-based time-series models for predicting oil palm yield remains limited. This study aims to explore the potential of two Python-based time-series models, Neural Prophet and Facebook Prophet, as alternatives to the conventional Autoregressive Integrated Moving Average (ARIMA) model for oil palm yield prediction. The study utilized historical yield data from two oil palm estates, Estate A and Estate B, covering a total of 100 data points from 2015 to 2022 on a monthly basis. The results demonstrate that the Neural Prophet and Prophet models outperformed the ARIMA model in terms of predictive accuracy. For Estate A, the Neural Prophet model achieved the highest accuracy, with a Mean Absolute Error (MAE) of 0.16, a Root Mean Square Error (RMSE) of 0.18, and a Mean Absolute Percentage Error (MAPE) of 0.14. Similarly, in Estate B, the Neural Prophet model obtained an MAE of 0.17, an RMSE of 0.21, and a MAPE of 0.10. The superior performance of the Neural Prophet model can be attributed to its ability to capture the complex patterns and nonlinear relationships inherent in the time-series data, owing to the adoption of deep learning principles. The fast implementation and robust forecasting capabilities of the Neural Prophet and Fb Prophet models make them viable alternatives to the conventional ARIMA model for oil palm yield prediction. The time-series predictive models developed in this study can assist plantation management in making informed decisions concerning yield forecasting, which is crucial for effective management of inputs, ultimately leading to cost optimization and enhanced sustainability in oil palm cultivation.