Deep Learning Python-Based Time-Series Model for Oil Palm Yield Prediction

Authors

  • Yuhao Ang Faculty of Sustainable Agriculture, Universiti Malaysia Sabah Sandakan Campus, Locked Bag No. 3, 90509 Sandakan, Sabah, Malaysia https://orcid.org/0000-0002-3014-3024
  • Helmi Zulhaidi Mohd Shafri Department of Civil Engineering and Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Universiti Putra Malaysia (UPM), 43400 Serdang, Selangor, Malaysia
  • Yang Ping Lee Geoinformatics Unit, FGV R&D Sdn Bhd, FGV Innovation Centre, PT35377, Lengkuk Teknologi, 71760 Bandar Enstek, Negeri Sembilan, Malaysia. https://orcid.org/0000-0002-2750-6701
  • Shahrul Azman Bakar Geoinformatics Unit, FGV R&D Sdn Bhd, FGV Innovation Centre, PT35377, Lengkuk Teknologi, 71760 Bandar Enstek, Negeri Sembilan, Malaysia.
  • Haryati Abidin Geoinformatics Unit, FGV R&D Sdn Bhd, FGV Innovation Centre, PT35377, Lengkuk Teknologi, 71760 Bandar Enstek, Negeri Sembilan, Malaysia.
  • Mohd Umar Ubaydah Mohd Junaidi FGV Agri Services Sdn Bhd, Level 9, West, Wisma FGV, Jalan Raja Laut, 50350 Kuala Lumpur, Malaysia.
  • Hwee San Lim School of Physics, Universiti Sains Malaysia (USM), 11800 Gelugor, Penang, Malaysia.
  • Rosni Abdullah School of Computer Sciences, Universiti Sains Malaysia (USM), 11800 Gelugor, Penang, Malaysia.
  • Yusri Yusup School of Industrial Technology, Universiti Sains Malaysia (USM), 11800 Gelugor, Penang, Malaysia.
  • Syahidah Akmal Muhammad School of Industrial Technology, Universiti Sains Malaysia (USM), 11800 Gelugor, Penang, Malaysia.
  • Sin Yin Teh School of Management, Universiti Sains Malaysia (USM), 11800 Gelugor, Penang, Malaysia.

Keywords:

ARIMA, Neural prophet, Facebook prophet, Time-series analysis, Oil palm, Yield production

Abstract

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.

Author Biographies

Yuhao Ang, Faculty of Sustainable Agriculture, Universiti Malaysia Sabah Sandakan Campus, Locked Bag No. 3, 90509 Sandakan, Sabah, Malaysia

Dr Ang Yuhao is an expert in agricultural remote sensing and artificial intelligence. He is currently working as a senior lecturer at Universiti Malaysia Sabah, under the Faculty of Sustainable Agriculture.

Helmi Zulhaidi Mohd Shafri, Department of Civil Engineering and Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Universiti Putra Malaysia (UPM), 43400 Serdang, Selangor, Malaysia

Dr Helmi Zulhaidi Mohd Shafri is currently a Professor and Remote Sensing & GIS Programme Coordinator at the Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM). He was previously the Head of the Department of Civil Engineering and the Deputy Dean of Postgraduate Studies at the Faculty of Engineering, UPM. He obtained his bachelor degree from Royal Melbourne Institute of Technology (RMIT) University, Melbourne, Australia and PhD from the University of Nottingham, United Kingdom. His fields of specialization include Geomatics Engineering, Remote Sensing, Machine Learning and Data Science.

Yang Ping Lee, Geoinformatics Unit, FGV R&D Sdn Bhd, FGV Innovation Centre, PT35377, Lengkuk Teknologi, 71760 Bandar Enstek, Negeri Sembilan, Malaysia.

Dr Lee Yang Ping is a Head of Precision Agriculture and Genomics who utilise remote sensing, precision tools and geographical information system in field research to maximise yield with improved efficiency inreplanting, minimize crop loss with early detection of diseases in oil palm plantation management.

Shahrul Azman Bakar, Geoinformatics Unit, FGV R&D Sdn Bhd, FGV Innovation Centre, PT35377, Lengkuk Teknologi, 71760 Bandar Enstek, Negeri Sembilan, Malaysia.

Shahrul Azman Bakar is a research officer at oil palm company. He mainly focused on the utilization of remote sensing in oil palm management

Haryati Abidin, Geoinformatics Unit, FGV R&D Sdn Bhd, FGV Innovation Centre, PT35377, Lengkuk Teknologi, 71760 Bandar Enstek, Negeri Sembilan, Malaysia.

Haryati abidin is the researcher officer and focus on the utilization of remote sensing in oil palm management

Mohd Umar Ubaydah Mohd Junaidi, FGV Agri Services Sdn Bhd, Level 9, West, Wisma FGV, Jalan Raja Laut, 50350 Kuala Lumpur, Malaysia.

Umar Ubaydah is an officer in FGV agri service

Hwee San Lim, School of Physics, Universiti Sains Malaysia (USM), 11800 Gelugor, Penang, Malaysia.

Hwee San Lim is an associate professor in Universiti Sains Malaysia

Rosni Abdullah, School of Computer Sciences, Universiti Sains Malaysia (USM), 11800 Gelugor, Penang, Malaysia.

Rosni Abdullah is a professor at the Universiti Sains Malaysia.

Yusri Yusup, School of Industrial Technology, Universiti Sains Malaysia (USM), 11800 Gelugor, Penang, Malaysia.

Yusri Yusup is an associate professor in meteorology, atmospheric science, and environmental engineering at the School of Industrial Technology. He is also the deputy dean of the academic matters of the school. He has more than ten years of experience in teaching and research in those fields. He has worked with external institutes such as PETRONAS Research Sdn. Bhd., Washington State University, the Malaysian Department of Environment, and the Malaysian Palm Oil Board.

Syahidah Akmal Muhammad, School of Industrial Technology, Universiti Sains Malaysia (USM), 11800 Gelugor, Penang, Malaysia.

Dr Syahidah Akmal is an associate professor at School of Industrial Technology, Universiti Sains Malaysia.

Sin Yin Teh, School of Management, Universiti Sains Malaysia (USM), 11800 Gelugor, Penang, Malaysia.

TEH SIN YIN, Ph. D. is a senior lecturer at the School of Management, Universiti Sains Malaysia (USM). She joined the university after completing her PhD. She was a recipient of the meritorious International Congress of Mathematicians 2014 Travel Fellowship Fund to Seoul Korea awarded by the International Mathematical Union. She was also recipient of Outstanding Dissertation Award 2014 awarded by Industrial Engineering and Operations Management Society. Moreover, she was also a recipient for the Malaysian Mathematical Sciences Society Award 2013 for Ph.D. thesis and 2009 for Master thesis. Teh was a visiting fellow at City University of Hong Kong in 2014. Her research articles have been published in several renowned international peer reviewed journals which include Plos One, Communications in Statistics-Theory and Methods, Computers and Industrial Engineering, Quality and Reliability Engineering International and etc. Teh has also presented more than 25 papers of her research works at various local and international conferences. She also serves as member of the editorial boards and technical/advisory committee of international journals and conferences of repute. She was involved in an Information, Communication and Technologies (ICT) research project with the Penang state government; and a mobile learning project with Motorola Solutions Malaysia. Currently, she is involving in a ‘Product Quality and Reliability Improvement’ project with Sanmina Corporation. Her research interests are statistical process/quality control, robust statistics, data mining, operations management, and quality management.

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Published

22-07-2025

How to Cite

Ang, Y., Mohd Shafri, H. Z., Lee, Y. P., Bakar, S. A., Abidin, H., Mohd Junaidi, M. U. U., … Teh, S. Y. (2025). Deep Learning Python-Based Time-Series Model for Oil Palm Yield Prediction. Applications of Modelling and Simulation, 9, 315–323. Retrieved from https://www.ojs.arqiipubl.com/index.php/AMS_Journal/article/view/945

Issue

Vol. 9 (2025)

Section

Articles

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Copyright (c) 2025 Yuhao Ang, Helmi Zulhaidi Mohd Shafri, Yang Ping Lee, Shahrul Azman Bakar, Haryati Abidin, Mohd Umar Ubaydah Mohd Junaidi, Hwee San Lim, Rosni Abdullah, Yusri Yusup, Syahidah Akmal Muhammad, Sin Yin Teh

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