# Citrus Pest Identification Model Based on Improved ShuffleNet
## 3 Results and Discussion
### Ablation Study  
To evaluate the impact of each enhancement on the experimental outcomes, we conducted an ablation study, the details of which are presented in Table 4. In this table, the symbol \"\(\vee\)\" indicates the inclusion of improvements to the ShuffleNet V2 network, whereas \"\(\mathbf{\times}\)\" denotes no improvements. The integration of the BCBAM attention mechanism into the original ShuffleNet V2 model increased accuracy by 2.41% and the F1 score by 2.59%. However, this integration also led to increases in the number of parameters, floating-point operations, and model size by 0.2 \(\times\) 10\({}^{6}\), 1.48 \(\times\) 10\({}^{6}\), and 0.81 MB, respectively, thereby increasing the complexity of the network. The application of transfer learning methods did not change the number of parameters, floating-point operations, or model size but resulted in improvements of 0.78% in accuracy and 0.87% in the F1 score. Substituting the ReLU activation function with the PReLU function in the original ShuffleNet V2 model led to an increase of 1.06% in accuracy and 1.04% in the F1 score. Although the number of parameters and model size remained unchanged, the floating-point operations increased by 2 \(\times\) 10\({}^{6}\). When combining the BCBAM attention mechanism, transfer learning methods, and PReLU activation function with the original ShuffleNet V2 model, the accuracy improved by 2.48% and the F1 score by 2.6%. These enhancements, however, also increased the model complexity. Following the architectural adjustments, there was a notable decrease in the number of parameters, floating-point operations, and model size.  
The SCHNet recognition model proposed in this study achieved an accuracy rate of 94.48% and an F1 score of 94.38%, marking a 3.12% and 3.13% increase, respectively, compared to the original ShuffleNet V2 model. The model comprised 1.97 \(\times\) 10\({}^{6}\) parameters, executed 121.11 \(\times\) 10\({}^{6}\) floating-point operations, and had a size of 3.84 MB. Compared  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **BCABM** & **T L** & **PReLU** & **AAd** & **Acc** & **F1 Score** & **Par/10\({}^{6}\)** & **Floats/10\({}^{6}\)** & **MS/MB** \\ \hline \multirow{8}{*}{ShuNet} & ✗ & ✗ & ✗ & ✗ & 0.9136 & 0.9125 & 2.28 & 152.71 & 4.97 \\  & ✓ & ✗ & ✗ & ✗ & 0.9377 & 0.9384 & 2.48 & 154.19 & 5.78 \\  & ✗ & ✓ & ✗ & ✗ & 0.9214 & 0.9212 & 2.28 & 152.71 & 4.97 \\  & ✗ & ✗ & ✓ & ✗ & 0.9242 & 0.9229 & 2.28 & 154.71 & 4.98 \\  & ✓ & ✓ & ✗ & ✗ & 0.9341 & 0.9344 & 2.48 & 154.19 & 5.78 \\  & ✓ & ✓ & ✓ & ✓ & 0.9384 & 0.9385 & 2.48 & 155.03 & 5.80 \\ \hline SCHNet & ✓ & ✓ & ✓ & ✓ & 0.9448 & 0.9438 & 1.97 & 121.11 & 3.84 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiments.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 score** & **Parameters/10\({}^{6}\)** & **Floats/10\({}^{6}\)** \\ \hline Scheme 1 & 0.9235 & 0.9255 & 0.9211 & 0.9233 & 2.48 & 1.5340 \\ Scheme 2 & 0.9345 & 0.9344 & 0.9347 & 0.9345 & 2.48 & 1.5419 \\ Scheme 3 & 0.9377 & 0.9401 & 0.9368 & 0.9384 & 2.48 & 1.5419 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results of attention mechanisms.  
to the original ShuffleNet V2 model, the parameters decreased by 0.31 \(\times\) 10\({}^{6}\), floating-point operations by 31.6 \(\times\) 10\({}^{6}\), and model size by 1.13 MB, corresponding to reductions of 13.6%, 20.7%, and 22.7%, respectively. In summary, the introduction of the BCBAM attention mechanism notably enhanced the model performance. Architectural adjustments effectively reduced the structural complexity without compromising the accuracy. The SCHNet recognition model in this study is feasible due to the high precision and lightweight design (annotation: TL: Transfer Learning; AAd: Architectural Adjustment; Acc: Accuracy; Par: Parameters; MS: Model Size; ShuNet: ShuffleNet V2).