# Autonomous Detection of _Spodoptera frugiperda_ by Feeding Symptoms Directly from UAV RGB Imagery
## 2 Materials and Methods
### Convolutional Neural Network  
After a long period of development since AlexNet [39], convolutional neural networks, composed of a convolution layer, a pooling layer, and a fully connected layer, have evolved into series of models that can automate the extraction of features through training iterations [40,41]. ResNet [42], through the application of residual blocks, has solved the problem of network degradation and parameter disappearance with a continuous increase in neural network layers, making an indelible contribution to the progress of deep learning, called Deep Convolutional Neural Network (DCNN). DCNN can automatically extract the features of convolutional check images with different specifications to obtain higher data classification accuracy, and it has become the most common identification method [43].  
Based on ResNet, the integration of different methods leads to the development of various network structures, such as grouping convolution [44], self-attention mechanism [45], and selective attention mechanism [46]. Therefore, in this study, the feasibility of the feeding symptoms method based on maize _Spodoptera frugiperda_ was verified by using several kinds of ResNet related networks, including ResNet, ResNeS [47], SE-Net, and SK-Net. Although the residual structure has been widely applied with its simple structure and convenient modular design, its performance is not satisfactory in downstream applications, which is affected by factors such as receptive field size and channel interaction. Recently, the successful application of the channel and attention mechanism has introduced new possibilities for its improvement. ResNext first introduced the idea of grouping convolution. The SE-Net introduces a channel-attention mechanism for feature construction by adaptively recalibrating the channel feature response. SK-Net extracted the channel information from the feature map through the construction of the grouped channels. Therefore, according to the idea of taking the channel as the operation unit and dividing the input data into more fine-grained weighted subgroups or subchannels based on the global context, it is able to build a channel-based split attention structure. During training, each subgroup is able to perform different mapping abstractions on the input channel data of its own part so as to build different feature representations. In the model, the module is named a distraction  
Figure 5: Augmentation: (**a**) The original image; (**b,c**) Horizontal flips and rotations of 90\({}^{\circ}\), respectively; (**d**) Shift scale rotate; (**e**) Random grid transformation; (**f,g**) Contrast transformation; (**h**) Random Contrast transformation; (**i**) IAASharpen; (**j**) IAAEmboss; (**k**) CLAHE; (**l**) Shift cutout.  
block. Thanks to the simple and modular structure, the distraction blocks can perform multiple reuse and stacking and then construct the universal structure bodies similar to the same residue model. Therefore, the block can be simply described as replacing the original residual part of the attention operations with channels as units and thus giving the corresponding weight to the identity.  
At the same time, to increase the comparison, other classic network models such as EfficientNet [48] and RegNet [49] are also selected. The networks used in this study were consistent with the architecture of ResNet50, and the original block was replaced with a split-attention block, as shown in the Figure 6 below.