## 4 Experiment
### Qualitative Analysis  
Our framework demonstrates superior performance on the D0 dataset, but faces challenges on the IP102 dataset due to the varied appearances of pests at different growth stages. This variability complicates classification, as species within the same genus can exhibit significant differences across stages, and unrelated species can appear similar at specific stages. To evaluate the framework's robustness, Figure 8 displays instances of misclassifications, highlighting difficulties in pest recognition during the egg, larval, and pupal stages. These stages often lack distinct morphological features, leading to a homogenized appearance that confuses the framework. In contrast, adult pests develop unique features that aid correct recognition. However, the framework's performance is also impacted by issues such as mislabeled categories and absent targets in the IP102 dataset images [20,39], as shown in Figure 6c.  
To further illustrate the strengths and limitations of our framework, Table 5 compares our multi-task framework with several models on the IP102 dataset. MobileNetV3, ideal for resource-constrained environments due to its small number of parameters and low FLOPs, performs poorly in terms of Acc and MF1. ConvNeXt base outperforms standard CNNs like GoogLeNet and ResNet-50, showing superior overall performance. While the Swin Transformer base achieves high performance, its complex hierarchical architecture results in 88 million parameters and 15.7 GFLOPs. Additionally, it lacks specialized fine-grained feature recognition to effectively handle variations in pest appearances. In contrast, our multi-task learning framework integrates the DAM-Net and the ResNet-50 with an adaptive weighted loss mechanism, enhancing feature extraction and reducing overfitting. This approach achieves superior accuracy of 74.1% with only 49 million parameters and 20.3 GFLOPs, demonstrating the effectiveness and efficiency of our framework.  
Our method incurs higher FLOPs due to processing 448 \(\times\) 448 inputs, compared to the 224 \(\times\) 224 used by other methods. However, this increased computational load allows for capturing finer details and richer contextual information, enhancing pest recognition  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**\# D0**} & \multicolumn{3}{c}{**\# IP102**} \\ \cline{2-7}  & **Acc (\%)** & **MF1 (\%)** & **GM (\%)** & **Acc (\%)** & **MF1 (\%)** & **GM (\%)** \\ \hline DAM-Net & 99.6 & 99.5 & 99.5 & 71.4 & 63.7 & 58.0 \\ ResNet-50 & 99.3 & 99.1 & 99.1 & 70.9 & 63.5 & 59.5 \\
**Multi-task framework** & **99.7** & **99.6** & **99.5** & **74.1** & **65.9** & **59.5** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study results for the multi-task learning framework on D0 and IP102 datasets. The bold highlights indicate the best results.  
Figure 8: Instances of misclassifications by our multi-task framework on the IP102 dataset. Predictions that are correct are marked in green, while incorrect predictions are highlighted in red. All pest names are shown first with their original dataset category names, followed by their Latin names in parentheses.  
accuracy. Our framework is designed for cloud server deployment, where computational resources are less constrained. Although network transmission delays exist, they increase tolerance for computational demands. Thus, this study focuses on achieving superior pest recognition performance rather than minimizing computational cost.