## 4 Results and Discussion  
In order to make sure the dependability of network training, this paper randomly selects 15% of pest data sets as the testing images. The rest of the pictures are set as the training and verification data set. The proportion is 75% and 10%. Based on this division, this article uses Ubuntu 16.04 LTS as the system to build a cloud server platform. This platform is equipped with a double-core processor with Intel Core i7-8600, a memory of 256 G, and a 4 \(\times\) 4T solid-state hard drive. p40 GPU, computing cache is 96 G, using deep-learning framework Tensorflow1.4 and pytorch3.0. In order to verify the performance advantage of the fusion model, this article trained multiple comparison models, including multiple coarse-grained deep networks such as Vgg19 [18], Resnet50 [19], Densenet121 [33], SEnet [34], and multiple fine-grained models such as NTS-Net [35], FAST-MPN-COV [36], and DFL [37]. The evaluation metrics in this paper includes: the top-1 and top-5 accuracies of measuring the overall performance of the model on the data set, using the F1-score (F1) to measure the overall robustness of the model, and ASP is used to indicate the average single sample recognition time. In order to verify the preferable ability of the proposed method for fine-grained pest identification, some practical experiments are accomplished compared with other classical CNNs method, which has already achieved remarkable success in other fields, to illustrate the better performance of the proposed algorithm. In other fields, those CNNs have obtained lots of remarkable success in many aspects. Additionally, some fine-grained models are also used to illustrate the effectiveness of the probability fusion module in our proposed method for  
Figure 5: The cascaded architecture of soft-VLAD and gated fusion modules. fine-grained pest classification. ASP, top-1 accuracy, and top-5 accuracy of each comparative model are listed in Table 1 as follows:  
As shown in Table 1, It can be seen that compared to common coarse-grained models such as vgg19, Resnet50, etc., the fine-grained models obviously have more significant recognition accuracy, whether from top-1 accuracy or top-5 accuracy. However, from the perspective of the top-1 accuracy and top-5 accuracy of the single fine-grained model DFL, FAST-MPN-COV, NTS-Net in the table, although they have significantly improved compared to the coarse-grained model, their accuracy is also difficult to reach a satisfactory level. Through the corresponding comparison, we can see that after clustering at the NetVLAD layer, the corresponding fine-grained models such as FAST-MPN-COV and NTS-Net all have a little improvement in recognition accuracy, which shows that the feature clustering adopted in this paper has a positive effect. Secondly, from the results of the fusion of FAST-MPN-COV and NTS-Net, the accuracy of the fusion is significantly improved compared to the single model, which proves the effectiveness of the decision-level fusion strategy. The effectiveness of the above-mentioned NetVLAD clustering and fusion strategy is shown in the table. The proposed FPNT with a gated fusion strategy and NetVLAD modules achieves the best recognition effect. Its top-1 accuracy and top-5 accuracy reached 91.18% and 94.85%, respectively, which achieves the best recognition effect on this data set. Similarly, our end-to-end method consistently shows better performance on ASP outperforming state-of-the-art methods and has relatively compact multi-layer network architecture without extra annotation. This clearly indicates that FPNT has better learning and representation capabilities for actual pest recognition applications. In addition, we also compare the parameters of the models. From Table 1, we can see that the parameters of our model are the smallest, so our model is the best in terms of lightweight, which is more suitable for use on smart devices than other models.