<![CDATA[The use of storage systems for managing personal and work related items continues to increase in many environments. However, inventory management in small-scale storage, such as drawers, is still commonly performed manually. This approach often causes problems, including recording errors, inconsistent inventory data, and limited ability to detect missing or unauthorized items. For this reason, this study develops a smart drawer system that combines Internet of Things (IoT) technology with the YOLOv8 deep learning model to support automatic inventory management and basic item security. In the proposed system, a camera is installed inside the drawer to capture images of the stored objects. These images are processed directly on the device using the YOLOv8 object detection model to identify and count items. The detection results are then sent to an IoT platform so that inventory data and drawer activity can be monitored through a server. During operation, the system also records drawer access events, which allows irregular situations, such as missing items or unauthorized removal, to be observed. Experimental testing shows that the YOLOv8 based detection model is capable of recognizing stored objects with acceptable accuracy under typical drawer lighting conditions. The integration with the IoT platform enables inventory updates to be performed with low delay, making the system suitable for real-time monitoring. Compared with manual inventory methods, the proposed smart drawer helps reduce data inconsistencies and improves the visibility of stored items. This research indicates that the integration of computer vision and IoT can be applied effectively to small-scale storage systems using edge-based devices. The developed smart drawer can be implemented in offices, laboratories, and similar environments, and it may provide useful insights for future studies related to intelligent inventory and storage automation.]]>
