# Research on Innovative Apple Grading Technology Driven by Intelligent Vision and Machine Learning
## 3 Experiment and Result Analysis
### Fruit Stem Detection Experiments
#### 3.2.3 Ablation Study on the Improvement Process  
This paper conducted a series of ablation experiments to demonstrate the effectiveness of the proposed improvement strategies for enhancing fruit stem detection tasks. Based on the YOLOv5s detection network, the study specifically explored the use of FasterNet-t0 to improve the feature extraction network, the introduction of DBB-PANet to enhance the feature fusion network, and the combination of both methods for further improvement. The detailed ablation experiment results for different improvement strategies in the fruit stem detection task are presented in Table 3.  
Based on the ablation experiment results in Table 3, it is evident that using FasterNet-t0 as a feature extraction network reduces GFLOPs by 4.6 and decreases the model size by 2.8 MB, achieving model lightweighting. This indicates that the PConv operator in FasterNet effectively reduces memory access frequency, with accuracy remaining almost on par with the baseline model. Utilizing DBB-PANet to improve the feature fusion network boosts the P by 0.3% and R by 2.3%, with a slight increase in accuracy. This suggests that DBB-PANet enhances performance in fruit stem detection tasks, effectively improving the recognition capability for stems of varying sizes through a multi-scale structural re-parameterization strategy. When both strategies are combined to improve the YOLOv5-s model, the precision is increased by 2.7%, the [EMAIL_ADDRESS] value rises by 0.8%, and the GFLOPs is reduced by 1.8 compared to the baseline model. This demonstrates that DBB-PANet  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Backbone** & **P/\%** & **R/\%** & **[EMAIL_ADDRESS]/\%** & **[EMAIL_ADDRESS]:0.95/\%** & **GFLOPs** & **Size/MB** \\ \hline MobileNetv3 & 93.0 & 90.5 & 95.3 & 67.0 & 6.5 & 8.7 \\ GhostNet & 89.1 & 93.7 & 95.2 & 65.7 & 5.9 & 6.7 \\ FasterNet-t0 & 95.1 & 91.5 & 95.8 & 69.5 & 11.2 & 11.0 \\ FasterNet-t1 & 95.2 & 91.0 & 96.1 & 70.4 & 20.0 & 18.1 \\ FasterNet-t2 & 93.7 & 92.1 & 96.9 & 69.7 & 37.9 & 32.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative experiments on different feature extraction networks.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**FasterNet-t0** & **DBB-PANet** & **P/\%** & **R/\%** & **[EMAIL_ADDRESS]/\%** & **[EMAIL_ADDRESS]:0.95/\%** & **GFLOPs** & **Size/MB** \\ \hline  & & 93.0 & 91.9 & 96.1 & 70.1 & 15.8 & 13.8 \\ \(\surd\) & & 95.1 & 91.5 & 95.8 & 69.5 & 11.2 & 11.0 \\  & \(\surd\) & 93.3 & 94.2 & 96.2 & 70.2 & 18.1 & 16.6 \\ \(\surd\) & \(\surd\) & 95.7 & 91.0 & 96.9 & 69.8 & 13.6 & 14.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiments on the improvement process.  
fully integrates the feature information extracted by the FasterNet-t0 network, resulting in improved detection accuracy while simultaneously reducing computational demand.  
To more intuitively demonstrate that the proposed FDNet outperforms the baseline YOLOv5-s model, we visualized the bbox_loss, obj_loss, and [EMAIL_ADDRESS] during the training process, as shown in Figure 10. From Figure 10a,c, it is apparent that both loss curves for FDNet are superior to those of YOLOv5-s during training, indicating better model convergence. The [EMAIL_ADDRESS] value for FDNet also exceeds that of YOLOv5-s, as depicted in Figure 10b. Additionally, the Precision-Recall Curve was used to further analyze the detection accuracy of FDNet and YOLOv5-s on the validation set. Figure 10d presents the detection results for FDNet, while Figure 10e shows the results for YOLOv5-s. Notably, the [EMAIL_ADDRESS] value for FDNet is 0.969, which is higher than that of YOLOv5-s.