## 3 Results and Discussion
### Model Comparison Analysis  
To ensure the final enhanced model meets the desired specifications, a series of experiments were conducted, and three lightweight model backbones (GhostNet, ShuffleNet, and MobileNet) were integrated into the YOLOv5s model. Concurrently, to address the diminution of feature values resulting from model lightweight, three attention mechanisms were incorporated: the Shuffle Attention (SA), Coordinate Attention (CA), and CBAM mechanisms were employed. As can be seen from Table 2, the YOLOv8s model outperforms YOLOv5s in terms of performance and _mAP50_. However, YOLOv8s has a larger size, which makes it too bulky for model lightweighting. Also, YOLOv5s can effectively improve the _Precision_, _Recall_, and _mAP50_ of model for the same model improvement, but YOLOv8s does not show the same performance improvement. In the final model, with the same improvement, we found that YOLOv5s-ShuffleNet-CBAM could obtain similar results to YOLOv8s-ShuffleNet-CBAM while being much smaller, so we decided not to choose the improved YOLOv8s model, but instead, the YOLOv5s was finally chosen. Additionally, compared with the original model (YOLOv5s), the improved YOLOv5s models all have reduced model sizes compared to the original model, with the YOLOv5s-ShuffleNet-CA model being the smallest but not having a good improvement. While the YOLOv5s-MobileNet-CBAM model is the most balanced, the _Recall_ and _mAP50_ values have a good improvement, and the model size is greatly reduced but not as significantly compared to other models. Meanwhile, YOLOv5s-ShuffleNet-CBAM has higher _mAP50_ values and has the highest _Precision_ value and has a small model size. YOLOv5s-GhostNet-CBAM has the highest _mAP50_ value, but the model size is not significantly reduced. Also, through Figure 11, we could see that with 1000 rounds of training, the models YOLOv5s-MobileNet-CBAM, YOLOv5s-MobileNet-SA, YOLOv5s-ShuffleNet-CA, and YOLOv5s-GhostNet-CBAM had _mAP50_ values that eventually converged to 0.93, and considering a combination of model size and _mAP50_ values, we decided to choose the YOLOv5s-ShuffleNet-CBAM model as the final model. It can be seen that the YOLOv5s model finally stabilized at around 85%. The YOLOv5s-ShuffleNet-CBAM model was stable at 0.946, and the stability of the improved model was greatly improved [26]. Gradient-weighted Class Activation Mapping (Grad-CAM) is a powerful technique used to visualize the regions in an image that contribute the most to the predictions made by a CNN. This method helps in understanding how models make decisions, particularly in tasks like image classification and object detection. As shown in Figure 12, it was observed that Grad-CAM in the \"Sound\" seeds exhibited smooth isotherms. In contrast, the \"Mild\" and \"Severe\" seeds displayed irregular isotherms. The \"Mild\" seeds exhibited the presence of small black spots, whereas the \"Severe\" seeds displayed a predominantly deep red \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & _Precision [\%] \(\uparrow\)_ & _Recall [\%] \(\uparrow\)_ & _mAP50 [\%] \(\uparrow\)_ & **Size (MB) \(\downarrow\)** \\ \hline YOLOv8s & 86.7 & 90.9 & 95.9 & 21.48 \\ YOLOv8s–ShuffleNet & 89.6 & 90.7 & 96.3 & 12.45 \\ YOLOv8s–ShuffleNet-CBAM & 87.5 & **93.2** & **96.6** & 13.11 \\ YOLOv5s & 88.3 & 88.0 & 94.3 & 13.80 \\ YOLOv5s–ShuffleNet & 87.0 & 87.4 & 92.8 & **1.97** \\
**YOLOv5s–ShuffleNet–CBAM** & **90.2** & 88.0 & 95.5 & 2.40 \\ YOLOv5s–MobileNet–CBAM & 86.9 & 90.3 & 94.8 & 3.46 \\ YOLOv5s–MobileNet–CA & 87.3 & 88.1 & 93.6 & 3.05 \\ YOLOv5s–MobileNet–SA & 86.7 & 89.9 & 94.5 & 3.04 \\ YOLOv5s–ShuffleNet–CA & 86.8 & 85.5 & 92.9 & 1.99 \\ YOLOv5s–GhostNet–CBAM & 83.3 & 90.9 & 95.7 & 11.82 \\ \hline \hline \end{tabular} Note: (\(\uparrow\)) means the higher value is better; (\(\downarrow\)) means the lower value is better and the most accurate item are shown in bold.  
\end{table} 
Table 2: Results of YOLOv5s model. coloration.