Leveraging deep learning for agricultural market forecasting and supply chain optimization in Malaysia

Mirza Nurhaziq Mazatul Affendy, R. Kanesaraj Ramasamy, Venushini Rajendran

Article ID: 8966
Vol 9, Issue 2, 2025


Abstract


The Agriculture Trading Platform (ATP) represents a significant innovation in the realm of agricultural trade in Malaysia. This web-based platform is designed to address the prevalent inefficiencies and lack of transparency in the current agricultural trading environment. By centralizing real-time data on agricultural production, consumption, and pricing, ATP provides a comprehensive dashboard that facilitates data-driven decision-making for all stakeholders in the agricultural supply chain. The platform employs advanced deep learning algorithms, including Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), to forecast market trends and consumption patterns. These predictive capabilities enable producers to optimize their market strategies, negotiate better prices, and access broader markets, thereby enhancing the overall efficiency and transparency of agricultural trading in Malaysia. The ATP’s user-friendly interface and robust analytical tools have the potential to revolutionize the agricultural sector by empowering farmers, reducing reliance on intermediaries, and fostering a more equitable trading environment.


Keywords


agriculture trading platform; deep learning algorithms; market forecasting; data-driven decision making; agricultural supply chain

Full Text:

PDF


References


Azhar, R., Kesumah, F. S. D., Ambya, A., et al. (2020). Application of short-term forecasting models for energy entity stock price (study on Indika energi Tbk, JII). International Journal of Energy Economics and Policy, 10(1), 294–301. https://doi.org/10.32479/ijeep.8715

Chimmula, V. K. R., & Zhang, L. (2020). Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos, Solitons & Fractals, 135, 109864. https://doi.org/10.1016/j.chaos.2020.109864

Department of Agriculture Malaysia. (2022). Paddy statistics. Available online: https://www.dosm.gov.my/v1/index.php?r=column/ctwoByCat&parent_id=45&menu_id=Z0VTZGU1UHBUT1VJMFlpaXRRR0xpdz09 (accessed on 1 September 2024).

Department of Statistics Malaysia. (2020). Household income and expenditure survey report 2018/2019. Available online: https://www.dosm.gov.my/v1/index.php?r=column/cone&menu_id=cUp6NlNndGlaQkZhK0gwYUMyWFRxdz09 (accessed on 1 September 2024).

Department of Statistics Malaysia. (2021). Gross domestic product (GDP) estimates 2021. Available online: https://www.dosm.gov.my/v1/index.php?r=column/cthemeByCat&cat=100&bul_id=ckRVWlQrNVF4K2k3M1BWYU8vVTM0Zz09&menu_id=TE5CRUZCblh4ZTZMODZIbmk2aWRRQT09 (accessed on 1 September 2024).

Department of Statistics Malaysia. (2022). Monthly External Trade Statistics. Available online: https://www.dosm.gov.my/portal-main/release-content/monthly-external-trade-statistics-apr-2023 (accessed on 1 September 2024).

Department of Veterinary Services Malaysia. (2022). Livestock statistics. Available online: https://dosm.gov.my/v1/index.php?r=column/cglossary2&menu_id=eWd2VFdIZ2xpdzBmT2Y0a0pweDcwQT09α=WlFWbFFFZWw1OE9FeloxaWoxNEhrQT09 (accessed on 1 September 2024).

Guevara-Viejó, F., Valenzuela-Cobos, J. D., Vicente-Galindo, P., et al. (2021). Application of K-Means Clustering Algorithm to Commercial Parameters of Pleurotus spp. Cultivated on Representative Agricultural Wastes from Province of Guayas. Journal of Fungi, 7(7), 537. https://doi.org/10.3390/jof7070537

Malaysian Palm Oil Council. (2021). Malaysian palm oil exports set new record in 2021. Available online: https://mpoc.org.my/ (accessed on 1 September 2024).

Manogna, R. L. (2020). Innovation and firm growth in agricultural inputs industry: empirical evidence from India. Journal of Agribusiness in Developing and Emerging Economies, 11(5), 506–519. https://doi.org/10.1108/jadee-07-2020-0156

Mao, L., Huang, Y., Zhang, X., et al. (2022). ARIMA model forecasting analysis of the prices of multiple vegetables under the impact of the COVID-19. PLOS ONE, 17(7), e0271594. https://doi.org/10.1371/journal.pone.0271594

Ministry of Agriculture and Food Industry. (2022). Agriculture sector overview. Available online: https://mafi-events.com/about-ministry-of-agriculture-and-food-industries-malaysia/ (accessed on 1 September 2024).

Naorem, A., Rani, D., Roy, S., et al. (2019). Frontier soil technologies for sustainable development goals (SDGs) in India. In: Challenges and Emerging Opportunities in Indian Agriculture. National Academy of Agricultural Research Management, Hyderabad, India. pp. 113–152.

Raza, Z., Haq, I. U., Muneeb, M. (2023). Agri-4-All: A Framework for Blockchain Based Agricultural Food Supply Chains in the Era of Fourth Industrial Revolution. Available online: https://ieeexplore.ieee.org/document/10077566/ (accessed on 1 September 2024).

Sabu, K. M., & Kumar, T. K. M. (2020). Predictive analytics in Agriculture: Forecasting prices of Arecanuts in Kerala. Procedia Computer Science, 171, 699–708. https://doi.org/10.1016/j.procs.2020.04.076

Saprabha M., Dhanaraj, R. K. (2020). Machine learning based pedantic analysis of predictive algorithms in crop yield management. In: Proceedings of 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA); 5–7 November 2020; Coimbatore, India. pp. 1340-1345.

Siami-Namini, S., Tavakoli, N., Siami Namin, A. (2018). A comparison of ARIMA and LSTM in forecasting time series. Available online: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8614252 (accessed on 1 September 2024).

Virginia, V. E., Ginting, J., Elfaki, F. A. M. (2018). Application of GARCH model to forecast data and volatility of share price of energy: Study on Adaro Energy Tbk, LQ45. International Journal of Energy Economics and Policy, 8(3), 131-140.

Weng, Y., Wang, X., Hua, J., et al. (2019). Forecasting Horticultural Products Price Using ARIMA Model and Neural Network Based on a Large-Scale Data Set Collected by Web Crawler. IEEE Transactions on Computational Social Systems, 6(3), 547–553. https://doi.org/10.1109/tcss.2019.2914499




DOI: https://doi.org/10.24294/jipd8966

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Author(s)

License URL: https://creativecommons.org/licenses/by/4.0/

This site is licensed under a Creative Commons Attribution 4.0 International License.