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

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

doi:10.24294/jipd8966

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

Mirza Nurhaziq Mazatul Affendy

R. Kanesaraj Ramasamy

Venushini Rajendran

Journal of Infrastructure Policy and Development 2025, 9(2), 8966; https://doi.org/10.24294/jipd8966

Submitted：03 Sept 2024

Accepted：22 Nov 2024

Published：10 Feb 2025

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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.

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References

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