## 4 Results and Discussion
Figure 6 provides more experimental analysis of precision-recall histograms of different categories, which can further illustrate the feasibility and effectiveness of FPNT. In comparison to other models, the proposed FPNT method achieves better performance on the average recall and precision indicators, of which value is, respectively, up to 94.89% and 93.25%. The results show that the FPNT model can excavate the advanced features with excellent presentation skills from large-scale pest images, which is a further response to the better stability and accuracy than other models. Thus, it is much suitable for pest identification tasks in agriculture and forestry applications. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Top-1 Accuracy** & **Top-5 Accuracy** & **ASP (ms)** & **Parameter(M)** \\ \hline Vgg19 & 78.14\% & 80.32\% & 38 & 383.3 \\ SEnet & 79.09\% & 82.25\% & 46 & 376.5 \\ Resnet50 & 85.74\% & 87.95\% & 35 & 381.9 \\ Densenet121 & 81.38\% & 84.14\% & 33 & 396.2 \\ DFL & 75.81\% & 79.83\% & 74 & 254.3 \\ FAST-MPN-COV & 87.58\% & 90.49\% & 64 & 196.7 \\ NTS-Net & 87.12\% & 89.57\% & 57 & 120.3 \\
**FPNT** & **91.18\%** & **94.85\%** & **61** & **89.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results of ASP, top-1, and top-5 accuracies. In order to test the convergence effect of the model, we conducted an ablation experiment to compare the numerical changes of loss values offered by different methods through the control variables. The loss value of each model is estimated by calculating the inconsistency extent between all sample labels and the homologous prediction results, except for our method, which is achieved by using the fuse loss function as Equation (9). As shown in Figure 7, there are eight curves of loss value depicted in this experiment. From the overall evaluation, the smaller the loss value of one curve, the better the robustness of the relevant method. It is observed that the overall direction of loss curves offered by all comparative models has a downward trend, which shows that they all tend to converge and stabilize after 15 update epochs. Moreover, some popular coarse-grained models, including SEnet, Vgg19, Resnet 50, and Densenet-121, are obviously not as robust and effective as fine-grained classifiers during network training. This directly indicates that the pest image data set collected in this work has the cogent characteristics of fine-grained classification problems, and it is still challenging to existing deep-learning models without fine-grained techniques. On this basis, the proposed FPNT method has achieved the preferable performance in terms of loss value and curve changing than all models, which verifies the gated fusion and Soft-VALD aggregation modules work well in promoting the training efficiency and convergence speed. To a certain extent, the changes result of loss value also shows that the proposed FPNT method has a better classification ability than other models to handle the massive fine-grained pest images, furthermore suitable for various practical applications in complex environmental scenes. Figure 6: Precision and recall results of the comparison experiments. Figure 7: Comparative loss value curves of different models. In order to illustrate the role of the fusion module, this paper carries out a visual comparison experiment by comparing the FPNT method with three fine-grained models. We analyze the accuracy of each model for each type of image on the data set, as shown in Figure 8. It can be found that a single model has different recognition capabilities for different categories and even achieves obvious accuracy discrepancy among each pest category. In fact, the detailed accuracy of each model for different samples in the same pest category is also diverse. With the help of a designed fusion module, FPNT can effectively integrate multi-dimensional features extracted by different modules and eliminate redundant information among various complements, thereby improving the recognition accuracy for each type of pest image.