AI-Enabled Food Systems: Integrating Predictive Analytics, Smart Agriculture and Supply Chain Intelligence

James Landay

AI-Enabled Food Systems: Integrating Predictive Analytics, Smart Agriculture and Supply Chain Intelligence

Author(s)	James Landay
Country	United States
Abstract	The global food system is facing increasing pressure from population growth, climate variability, resource scarcity, changing consumer demand, supply-chain disruptions, and food-security challenges. Artificial Intelligence (AI), predictive analytics, Internet of Things (IoT), remote sensing, robotics, and data-driven supply-chain management are emerging as important tools for improving the efficiency, resilience, and sustainability of food production and distribution. This study examines the integration of AI-enabled predictive analytics, smart agriculture, and supply-chain intelligence across the food-system value chain. A qualitative and descriptive research methodology based on secondary literature is adopted to examine technological applications, opportunities, challenges, and future directions. The study analyses AI applications in crop forecasting, soil and crop monitoring, disease detection, precision irrigation, livestock management, post-harvest optimisation, demand forecasting, inventory management, logistics, food-quality monitoring, and traceability. The analysis indicates that AI can enable a transition from reactive food-system management towards predictive and adaptive decision-making. Smart agricultural technologies can optimise inputs and improve farm productivity, while supply-chain intelligence can improve forecasting, reduce waste, strengthen traceability, and increase resilience against disruptions. However, barriers including limited rural connectivity, high technology costs, data-quality problems, interoperability limitations, cybersecurity risks, skills shortages, algorithmic bias, and unequal access to technology may constrain adoption. The paper proposes an integrated AI-enabled food-system framework connecting farm-level intelligence, predictive analytics, digital supply chains, intelligent logistics, and consumer information systems. The study concludes that AI can contribute significantly to food security and sustainable agriculture when technological innovation is combined with farmer participation, responsible data governance, accessible infrastructure, and supportive institutional policies.
Keywords	Artificial Intelligence, Smart Agriculture, Predictive Analytics, Food Systems, Supply Chain Intelligence, Precision Agriculture, Food Security, Agri-Tech, Machine Learning, Sustainable Agriculture.
Field	Engineering
Published In	Volume 5, Issue 6, November-December 2023
Published On	2023-11-27

View / Download PDF File

About AJAMRI Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+1-872-302-3851	editor@ajamri.com

American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

AI-Enabled Food Systems: Integrating Predictive Analytics, Smart Agriculture and Supply Chain Intelligence

Share this