International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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

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Volume 7 Issue 2
March-April 2025

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A Supply Chain Approach Highlighting the Use of Artificial Intelligence and Computer Vision to Improve the Efficiency of Food Supply Chains in the United States

Author(s) Luis Polo
Country United States
Abstract Advanced technological solutions have become necessary to manage the complicated food supply chains within the United States because they enhance operational efficiency while minimizing waste and optimizing logistical operations. The supply chain management industry benefits from artificial intelligence-based computer vision, which has become a revolutionary tool. This research investigates how computer vision algorithms specializing in fruit and garlic count and piece selection processes optimize supply chain efficiency in multiple ways. These algorithms achieve better inventory control, enhanced accuracy, and decreased human mistakes through their combination of machine-learning models and real-time object detection with automated quality assessment. The research discusses essential deployment methods, key barriers, and financial advantages of adopting computer vision technology in food supply systems. This paper presents quantitative evidence through case studies and experimental findings that validate the results of computer vision applications. Systematic research enables this study to offer both strategic knowledge about AI-driven supply chains of the future and an implementation model that industries can utilize.
Keywords Computer Vision in Food Supply Chains, AI-Based Food Sorting and Quality Control, Supply Chain Optimization with Machine Learning, Automation in Agricultural and Food Processing, AI-Powered Inventory Management and Logistics
Field Computer
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-03-14
DOI https://doi.org/10.36948/ijfmr.2025.v07i02.38464
Short DOI https://doi.org/g895kp
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E-ISSN 2582-2160
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