## 4 Results and Discussions
Additionally, it is worth noting that the achieved mAP values of these models are higher compared to previous works [26; 32; 33], indicating an improvement in performance. Furthermore, the performance of the models for individual classes can be seen in Table 2 for YOLOv5n and Table 3 for YOLOv7. These tables provide the mAP50 and mAP50:95 values specifically for each class, allowing a more detailed analysis of the model's performance across different object categories. The calculations of mAP for different IoU threshold were conducted using the provided equations:  
\[mAP=\frac{1}{n}\sum_{k=1}^{k=n}AP_{k}\]  
Figure 17: Recall metric graph of YOLOv7. The performance metrics for the YOLOv5n and YOLOv7 models reveals their high accuracy and effectiveness in object detection tasks. Both models consistently achieved a precision of 0.972 for all classes, indicating their ability to accurately predict over 97% of true positive instances. The recall values were also impressive, with YOLOv5n achieving a recall of 0.988 and YOLOv7 achieving a recall of 0.986 for all classes, demonstrating their capability to identify a high proportion of true positive instances. Furthermore, the F1 scores for both models were equally impressive, with YOLOv5n and YOLOv7 achieving scores of 0.979 and 0.978, respectively, highlighting the model's balanced performance in combining precision and recall. Additionally, the mean Average Precision at IoU threshold 0.5 (mAP50) was 0.988 for YOLOv5n and 0.987 for YOLOv7, indicating their ability to accurately localize and classify objects as shown in Figures 20 and 21. The mean Average Precision within the IoU range of 0.5 to 0.95 (mAP50:95) was also remarkable, with YOLOv5n achieving a value of 0.932 and YOLOv7 achieving 0.951, further validating the model's robust performance across different IoU thresholds. Figure 19: mAP of YOLOv7. Figure 18: mAP of YOLOv5n. Figure 21: Results of YOLOv7 on validation batch. Figure 20: Results of YOLOv5n on validation batch. The prototype presented in Figure 22 comprises a laptop, a camera, a glass frame designed for secure placement of the camera at its highest position, a load cell, an hx711 module for data acquisition from the load cell, and an Arduino Uno for controlling and processing the acquired data. This setup was constructed with the purpose of securely accommodating the camera within the glass frame, allowing for stable and reliable positioning. Although the process of selling fruits and vegetables is automated by our intelligent IoT-based weighing scale, there are a few challenges associated with the utilization of  
Figure 22: Our Prototype. our intelligent IoT-based weighing scale, particularly in small-scale retail shops. Firstly, the current model of the smart scale is trained on a predefined dataset comprising only 12 classes, thus unrecognized fruits and vegetables will not be recognized by the scale. Gathering additional data and fine-tuning the model to encompass new classes is necessary to address this issue. Secondly, the scale requires specific hardware components, such as a Raspberry Pi or Jetson Nano, and an LCD screen for proper functioning, which may incur additional costs for small-scale retail shops. Lastly, the dynamic nature of product pricing requires manual input of updated prices into the GUI of each scale, which can be time-consuming for shopkeepers managing multiple scales. Addressing these challenges would enhance the scale's effectiveness and reliability.