## 2 Data and Methods
### ResNet-Locust-BN Network  
In recent years, CNN models have been widely used in the field of image processing. They have achieved excellent results in image classification, target detection, semantic segmentation, and video processing [34; 35; 36]. In general, the CNN network structure extracts abstract image features via convolution and pooling processes at many levels, integration, and reduction of dimensions with full connection processes, and provides a nonlinear modeling capability with different activation functions. On this basis, a large number of sample images are used as input, and, via BP, the gradient descent algorithm can be performed to iteratively modify the model parameters. Finally, it constructs a classifier oriented to a specific target. However, can CNN models accurately identify AM locust species and instars in the wild? To solve this problem, we selected five typical CNN models (the network structure is the same as the one used by the original author of the model) at the beginning of this study. Each model showed excellent accuracy even when designed with different characteristics. AlexNet proposed data augmentation, dropout method, and provided a pioneering contribution to the development of deep learning. Similarly, VggNet uses a convolutional layer combining small filters to express more powerful features of the input data with fewer parameters. In GoogLeNet, inception-based modular architecture was employed instead of increasing network depth to enhance the adaptability of the network to multiple scales and improve training accuracy. ResNet provided an available solution to vanishing gradient problem that results from network depth increase. In this study, we used both ResNet18 and ResNet50 as experimental models because ResNet18 and ResNet50 have different layer structures. ResNet18 is only layered with basic building block, and ResNet50 has a structure of BottleNeck building block with an additional layer of \(1\times 1\) convolution, which could reduce and restore the dimensions. He [37] pointed out that increasing the layers of training network only will not decrease training error correspondingly. He also noted that doing so could result in a degradation problem. To be more precise, it is necessary to list both the testing result for ResNet18 and ResNet 50 as well. The above-mentioned sample dataset was used as the model input, and the classification tests of the locust species and instars were directly performed. The experimental results obtained are shown in Table 3, where the experimental accuracy is maximized by modifying the batch size, activation function, initial learning rate parameters, and conducting multiple experiments. The results from the experiments performed show that CNN models have excellent applicability in species and instars recognition of locusts from visible field images. Out of all the models tested, ResNet50 produced the \begin{table}
\begin{tabular}{c c c c l} \hline \hline
**Species** & **Instar** & **Antennae Segments** & **Length (mm)** & **Coloring** \\ \hline AM locust & 3 & 20-21 & 10–20 &
\begin{tabular}{l} Grearious: black; \\ Dispersed: gray-green, brown-green; \\ Gregorious: head reddish-brown, \\ back black-brown, back foot diameter \\ node light yellow \\ Dispersed: in the color of \\ the environment; \\ Grearious: reddish-brown; \\ Dispersed: mostly green, following \\ the environmental color, overall body \\ color is lighter; \\ Green-brown green, or brownish back, \\ side green; \\ Yellowish green, hind wings are rose; \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Analysis of the phenotypic characteristics of East Asian migratory (AM) locusts, rice locusts, and cotton locusts. highest accuracy rate of approximately 80.84%. 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. However, can we further improve the classification accuracy by optimizing the network structure?