## 3 Results
### Recognition Accuracy of Different CNN Models  
In this study, we used the training dataset as input to build a classifier model based on six CNN models, which are as follows: AlexNet, GoogLeNet, ResNet 18, ResNet 50, VggNet, and ResNet-Locust-BN. Afterward, we used the test dataset to evaluate the accuracy of each model for the identification of AM locusts (third-instar, fifth-instar, and adult), rice locusts (adult), and cotton locusts (adult), as shown in Figure 8. For each CNN model, the accuracy was calculated as the ratio of the number of correctly identified test samples to the total number of test samples. The experimental results show that the six models evaluated generally exhibited high accuracies in the identification of rice locusts and cotton locusts. In contrast, identification of AM locusts (especially the third-instar AM locust) was more complicated. AlexNet, GoogLeNet, and ResNet 18 have a low recognition accuracy for third-instar and adult AM locusts and do not meet the application requirements. ResNet50, VggNet, and ResNet-Locust-BN present higher accuracy for the identification of fifth-instar AM locusts, adult East AM locusts, adult rice locusts, and adult cotton locusts. The ResNet50 model when tested with fifth-instar AM locust, produced a recognition accuracy rate of 91.00%, which slightly higher than the accuracy of ResNet-Locust-BN (88.40%) proposed in this paper. However, because ResNet-Locust-BN has a significantly higher recognition accuracy for third-instar AM locusts (77.20%) than other models, and has the highest overall accuracy (90.16%), we believe that ResNet-Locust-BN has better stability and applicability than the other models.  
We constructed a confusion matrix to present a more detailed description accuracy of the proposed ResNet-Locust-BN model in recognizing AM locusts, rice locusts and cotton locusts base on as shown in Figure 9. The recognition accuracy of adult locusts is generally higher than 93.50% and thus, can be considered adequate with respect to the application requirements. The true positive rate of the model in identifying these pests is 93.80%, 93.60%, and 97.80%, respectively. The low recognition accuracy of third-instar AM locusts is the greatest shortcoming of the proposed ResNet-Locust-BN model as well as other CNN models. Almost all erroneous judgments of third-instar and fifth-instar AM locusts are  
Figure 7: Comparison of the accuracy of ResNet-Locust-BN based on different batch sizes (activation function = ReLU, initial learning rate = 0.1).  
made in identifying AM locust with different instars. The third-instar AM locusts are more likely to be misidentified as fifth-instar AM locusts (with a corresponding error recognition rate of 10.70%), and vice versa (with a corresponding error recognition rate of 5.90%).  
Figure 8: Accuracy comparison of the six CNN locust identification models.  
Figure 9: Confusion matrix base on ResNet-Locust-BN model.  
It seems that a key factor limiting the recognition accuracy of third-instar AM locusts is insufficient information of the RGB images generated in visible wavelengths rather than the CNN models. Hence, to improve the recognition rate, multispectral or hyperspectral technology could be integrated into the model to explore more significant differences among AM locust with different instars. For AM locusts with each instar type (third-instar, fifth-instar, and adult), several samples were misidentified as rice locusts (adult) or cotton locusts (adult), which may be due to interference by grass, soil, crops, rocks, and other objects in the outdoor test areas. Few rice locusts (adult) and cotton locusts (adult) samples were misidentified as AM locusts. Further, rice locusts (adult) are more likely to be misidentified as cotton locusts (adult) with a corresponding error recognition rate of 4.40%.