Leveraging Artificial Intelligence for Enhanced Food Security: Innovations in Farm and Warehouse Management.
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| Title: | Leveraging Artificial Intelligence for Enhanced Food Security: Innovations in Farm and Warehouse Management. |
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| Authors: | Amiri, Azam1 (AUTHOR) azamamiri@eco.usb.ac.ir, Reuter, Julia1 (AUTHOR) jreuter@wiley.com |
| Source: | Journal of Food Quality. 4/30/2026, Vol. 2026, p1-16. 16p. |
| Subjects: | Food security, Artificial intelligence, Precision farming, Warehouse automation, Machine learning, Supply chain management, Robotics, Farm management |
| Abstract: | Global food security demands transformative solutions integrating advanced digital technologies into agricultural production and supply chain management. This manuscript reviews how artificial intelligence (AI) is revolutionizing farm operations and logistics by integrating AI‐enabled warehouse automation with cross‐sector data. Advanced machine learning algorithms, deep learning models, and predictive analytics are increasingly applied to optimize inventory management, forecast demand, and ensure food safety. In the warehouse sector, AI systems help prevent overstocking and understocking by synchronizing inventory levels with consumption trends, weather, and soil conditions, thereby minimizing wastage and improving supply chain responsiveness. AI applications in agriculture, ranging from precision irrigation to pest detection using machine vision, significantly improve crop yield prediction and disease management. These advances are further strengthened by integrating cloud computing services that facilitate seamless data sharing between farm fields and warehouse operations. AI‐driven decision support systems aggregate big data from diverse sources, enabling stakeholders to monitor environmental conditions, optimize resource allocation, and adjust real‐time supply chain logistics. In both production and storage environments, robotics and automation are increasingly used to translate AI outputs into physical actions—streamlining repetitive tasks, improving throughput, and strengthening end‐to‐end traceability. At the same time, scaling AI beyond pilots remains constrained by practical and governance barriers, including inconsistent data quality across stakeholders, exposure to cyber threats, substantial capital and maintenance requirements, and societal concerns related to privacy, accountability, and labor transitions. Building on these considerations, this review proposes an implementation‐oriented framework that links data acquisition, modeling, and monitoring choices to measurable outcomes in efficiency, sustainability, and transparency across agri‐food operations, with the overarching goal of improving system‐level resilience. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Global food security demands transformative solutions integrating advanced digital technologies into agricultural production and supply chain management. This manuscript reviews how artificial intelligence (AI) is revolutionizing farm operations and logistics by integrating AI‐enabled warehouse automation with cross‐sector data. Advanced machine learning algorithms, deep learning models, and predictive analytics are increasingly applied to optimize inventory management, forecast demand, and ensure food safety. In the warehouse sector, AI systems help prevent overstocking and understocking by synchronizing inventory levels with consumption trends, weather, and soil conditions, thereby minimizing wastage and improving supply chain responsiveness. AI applications in agriculture, ranging from precision irrigation to pest detection using machine vision, significantly improve crop yield prediction and disease management. These advances are further strengthened by integrating cloud computing services that facilitate seamless data sharing between farm fields and warehouse operations. AI‐driven decision support systems aggregate big data from diverse sources, enabling stakeholders to monitor environmental conditions, optimize resource allocation, and adjust real‐time supply chain logistics. In both production and storage environments, robotics and automation are increasingly used to translate AI outputs into physical actions—streamlining repetitive tasks, improving throughput, and strengthening end‐to‐end traceability. At the same time, scaling AI beyond pilots remains constrained by practical and governance barriers, including inconsistent data quality across stakeholders, exposure to cyber threats, substantial capital and maintenance requirements, and societal concerns related to privacy, accountability, and labor transitions. Building on these considerations, this review proposes an implementation‐oriented framework that links data acquisition, modeling, and monitoring choices to measurable outcomes in efficiency, sustainability, and transparency across agri‐food operations, with the overarching goal of improving system‐level resilience. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 01469428 |
| DOI: | 10.1155/jfq/2405451 |
