<p class="western" align="justify">Most of supply chain networks have encountered difficulties when trying to integrate all components and stakeholders (Customers, warehouse, transportation, and raw material production. Etc...), which make supply chain management system suffering from a lack of efficiency and transparency that make a steady increase in good’s falsification and consumer’s disappointment. All information about good’s production, storage and transportation should flow clearly during all stages of the supply chain by tracking and authenticating to avoid product’s contamination or fraud in the network. Current tracking IoT-based systems are built on top of centralized architecture and this leaves a gap for tampering and false information especially in urban area, but also the exsiting solutions cannot allow the information to be shared with authority or consumers. To effectively assess and assure traceability and transparency this paper propose an approach using a distributed ledger (blockchain) besides a configuable IoT-based system to take into account all needed data including specific case of urban area, with an opendata platform at the disposal of stockholders, authority and consumers.</p>

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The exponential growth in the number of automobiles over the past few decades has created a pressing need for a robust license plate identification system that can perform effectively under various conditions. In Morocco, as in other regions, local authorities, public organizations, and private companies require a reliable License Plate Recognition (LPR) system that takes into account all plates specifications (HWP, VWP, DP, YP, and WWP) and multiple fonts used. This research paper introduces an intelligent LPR system implemented using the Yolov5 and Detectron2 frameworks, which have been trained on a customized dataset comprising multiple fonts (such as CRE, HSRP, FE-S, etc.) and accounting for different circumstances such as illumination, climate, and lighting conditions. The proposed model incorporates an intelligent region segmentation approach that adapts to the plate's type, thereby enhancing recognition accuracy and overcoming conventional issues related to plate separators. With the use of image preprocessing and temporal redundancy optimization, the model achieves a precision of 97,181% when handling problematic plates, including those with specific illumination patterns, separators, degradations, and other challenges, with little advantage to Yolov5 over Detecton2.

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