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Transforming Agricultural Productivity with AI-Driven Forecasting: Innovations in Food Security and Supply Chain Optimization

Author

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  • Sambandh Bhusan Dhal

    (Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA
    Directorate of Energy, Environment, Science and Technology, Idaho National Laboratory, US Department of Energy, Idaho Falls, ID 83415, USA)

  • Debashish Kar

    (Texas A&M AgriLife Research, Texas A&M University, College Station, TX 77843, USA)

Abstract

Global food security is under significant threat from climate change, population growth, and resource scarcity. This review examines how advanced AI-driven forecasting models, including machine learning (ML), deep learning (DL), and time-series forecasting models like SARIMA/ARIMA, are transforming regional agricultural practices and food supply chains. Through the integration of Internet of Things (IoT), remote sensing, and blockchain technologies, these models facilitate the real-time monitoring of crop growth, resource allocation, and market dynamics, enhancing decision making and sustainability. The study adopts a mixed-methods approach, including systematic literature analysis and regional case studies. Highlights include AI-driven yield forecasting in European hydroponic systems and resource optimization in southeast Asian aquaponics, showcasing localized efficiency gains. Furthermore, AI applications in food processing, such as plasma, ozone and Pulsed Electric Field (PEF) treatments, are shown to improve food preservation and reduce spoilage. Key challenges—such as data quality, model scalability, and prediction accuracy—are discussed, particularly in the context of data-poor environments, limiting broader model applicability. The paper concludes by outlining future directions, emphasizing context-specific AI implementations, the need for public–private collaboration, and policy interventions to enhance scalability and adoption in food security contexts.

Suggested Citation

  • Sambandh Bhusan Dhal & Debashish Kar, 2024. "Transforming Agricultural Productivity with AI-Driven Forecasting: Innovations in Food Security and Supply Chain Optimization," Forecasting, MDPI, vol. 6(4), pages 1-27, October.
    • Handle: RePEc:gam:jforec:v:6:y:2024:i:4:p:46-951:d:1502433
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    References listed on IDEAS

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