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In the era of Industry 4.0, logistics operations require smart, automated, and reliable solutions to ensure efficiency, traceability, and zero-defect delivery [1], [2]. At Yazaki Morocco Kenitra, manual verification of shipping boxes often results in mismatches between physical contents and ERP records, leading to costly delays and errors. To address this issue, we developed an AI-powered SmartBox Monitoring System using a Raspberry Pi 5 and Camera Module V3. A MobileNetV2 convolutional neural network, trained on a dataset of 2,000 real warehouse images and optimized with TensorFlow Lite, achieved 98% validation accuracy under industrial conditions. Detection results are transmitted to a Node.js backend, stored in a MySQL database, and displayed on a React-based dashboard that integrates real-time monitoring, KPI analytics, and automated email alerts. The prototype was successfully deployed as a functional system at Yazaki, demonstrating reliability, low cost, and scalability. These results highlight the potential of combining embedded AI and IoT to modernize logistics monitoring and align with Industry 4.0 objectives.
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DOI: 10.1109/commnet68224.2025.11288852
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