# Citrus Pest Identification Model Based on Improved ShuffleNet
## 3 Results and Discussion
### Comparative Experiments with Different Network Models  
This study comprehensively assessed the efficacy of various models in identifying citrus pests and diseases. It selected high-performance networks, including AlexNet, ResNet50, and EfficientNet_b2, from a pool of eight models for detailed comparison. The experimental results are outlined in Table 5. According to the data presented in Table 5, although the SqueezeNet1_0 model had fewer parameters than the SCHNet model, it was outperformed by SCHNet in terms of computational parameters and floating-point operation capacity. SqueezeNet1_0 recorded an accuracy of 79.97%, a precision of 80.35%, a recall rate of 78.94%, and an F1 score of 79.64%. These figures represent declines of 14.51%, 14.05%, 15.41%, and 14.74%, respectively, compared to SCHNet. Similarly, the other models also lagged behind SCHNet in these critical performance metrics. These findings underscore the dual advantages of the SCHNet model in enhancing performance and managing network complexity, establishing its superiority in pest identification tasks. \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 AlexNet & 0.9016 & 0.9024 & 0.9038 & 0.9031 & 61.10 & 714.21 \\ ResNet50 & 0.9030 & 0.9050 & 0.9044 & 0.9047 & 25.56 & 4133.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative experimental results of different models. Figure 10: Heat maps. From Table 6, it can be seen that the citation large network has a higher number of parameters, with a maximum accuracy of 95.3%, which is 0.82% higher than the lower accuracy of the SCHNet model, but the number of parameters is too high for deployment on mobile devices. In the citation's lightweight network, [PERSON] et al. [19]'s model has a higher accuracy and lower parameter count. However, compared to the model in this article, the accuracy is 0.85% lower and the parameter count is higher by 1.53M. This further illustrates that the SCHNet model in this article is superior to the network model chosen in the citation in terms of both accuracy and parameter count. Moreover, to illustrate the remarkable classification performance of the SCHNet model, this study employed the visualization analysis of the confusion matrix to demonstrate its ability to distinctly classify various types of citrus pest images. Each column in the confusion matrix represented the predicted category and each row corresponded to the actual category. The examination of Figure 11 reveals the proficiency of the SCHNet model in extracting features from each type of pest image and its efficient classification capabilities. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Large-Scale Models** & **Recall** & **Parameters/10\({}^{6}\)** \\ \hline [PERSON] et al. [15] & yes & 83.52\% & \textgreater{}5.38 \\ [PERSON] et al. [16] & yes & 81.9\% & \textgreater{}5.38 \\ [PERSON] et al. [17] & yes & 82.5\% & \textgreater{}5.38 \\ [PERSON] et al. [18] & yes & 95.3\% & \textgreater{}5.38 \\ [PERSON] et al. [19] & no & 69.45\% & 5.38 \\ [PERSON] et al. [20] & no & 93.63\% & 3.5 \\ [PERSON] et al. [21] & no & 71.32\% & 4.2 \\ SCHNet & no & 94.48\% & 1.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of neural network models and citation models. Figure 11: Confusion matrix. 0: _Lawana imitata_; 1: _Geisha distinctssma_; 2: _Icerya purchasi_; 3: _Xenocattan- tops prachceras_ 4: _Dialeurodes citri_; 5: _Papilio xuthus_; 6: _Panonychus citri_.