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

Dhal, Sambandh Bhusan; Kar, Debashish

doi:10.3390/forecast6040046

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

Journal Article · 18 October 2024 · Forecasting

DOI: https://doi.org/10.3390/forecast6040046 · OSTI ID:2474807

^[1]; Kar, Debashish ^[2]

Texas A & M Univ., College Station, TX (United States); Idaho National Laboratory (INL), Idaho Falls, ID (United States)
Texas A & M Univ., College Station, TX (United States). Texas A & M AgriLife Research

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.

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Research Organization:: Idaho National Laboratory (INL), Idaho Falls, ID (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC07-05ID14517

OSTI ID:: 2474807

Report Number(s):: INL/JOU--24-81560-Rev000

Journal Information:: Forecasting, Journal Name: Forecasting Journal Issue: 4 Vol. 6; ISSN 2571-9394

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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An IoT-Based Data-Driven Real-Time Monitoring System for Control of Heavy Metals to Ensure Optimal Lettuce Growth in Hydroponic Set-Ups Dhal, Sambandh Bhusan; Mahanta, Shikhadri; Gumero, Jonathan Sensors, Vol. 23, Issue 1 https://doi.org/10.3390/s23010451	journal	January 2023
Environmental Assessment of an Urban Vertical Hydroponic Farming System in Sweden Martin, Michael; Molin, Elvira Sustainability, Vol. 11, Issue 15 https://doi.org/10.3390/su11154124	journal	July 2019
Small-Scale Farming: A Review of Challenges and Potential Opportunities Offered by Technological Advancements Dhillon, Rajveer; Moncur, Qianna Sustainability, Vol. 15, Issue 21 https://doi.org/10.3390/su152115478	journal	October 2023
Challenges of Sustainable and Commercial Aquaponics Goddek, Simon; Delaide, Boris; Mankasingh, Utra Sustainability, Vol. 7, Issue 4 https://doi.org/10.3390/su7044199	journal	April 2015
Soil Degradation, Land Scarcity and Food Security: Reviewing a Complex Challenge Gomiero, Tiziano Sustainability, Vol. 8, Issue 3 https://doi.org/10.3390/su8030281	journal	March 2016
Aquaponics: A Sustainable Path to Food Sovereignty and Enhanced Water Use Efficiency Ibrahim, Lubna A.; Shaghaleh, Hiba; El-Kassar, Gamal Mohamed Water, Vol. 15, Issue 24 https://doi.org/10.3390/w15244310	journal	December 2023
Deep Predictive Models Based on IoT and Remote Sensing Big Time Series for Precision Agriculture International Journal of Emerging Technology and Advanced Engineering, Vol. 12, Issue 11 https://doi.org/10.46338/ijetae1122_09	journal	November 2022
Comparative Analysis of CNN, LSTM And Random Forest for Multivariate Agricultural Price Forecasting ÖZden, Cevher Black Sea Journal of Agriculture, Vol. 6, Issue 4 https://doi.org/10.47115/bsagriculture.1304625	journal	July 2023
CNN Features are also Great at Unsupervised Classification Guérin, Joris; Gibaru, Olivier; Thiery, Stéphane Computer Science & Information Technology https://doi.org/10.5121/csit.2018.80308	conference	February 2018
Theoretical approaches to AI in supply chain optimization: Pathways to efficiency and resilience International Journal of Science and Technology Research Archive, Vol. 6, Issue 1 https://doi.org/10.53771/ijstra.2024.6.1.0033	journal	March 2024
Navigating the Financial Landscape: The Power and Limitations of the ARIMA Model Liu, Jin Highlights in Science, Engineering and Technology, Vol. 88 https://doi.org/10.54097/9zf6kd91	journal	March 2024
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Advancing Sustainable Agriculture: A Comprehensive Review for Optimizing Food Production and Environmental Conservation Saikanth, D. R. K.; Singh, Bal Veer International Journal of Plant & Soil Science, Vol. 35, Issue 16 https://doi.org/10.9734/ijpss/2023/v35i163169	journal	June 2023

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