# Detection of Wheat Yellow Rust Disease Severity Based on Improved GhostNetV2
## 4 Discussion  
We discussed, in Section 3.1, the effect of four commonly used attention mechanisms on the model. SE calculates the attention weight of each channel through two layers of fully connected layers and global average pooling and then scales the features of each channel to enhance the feature channels that are important to the current task and weaken the feature channels that are irrelevant to the current task, but SE needs to use two fully connected layers to calculate channel attention weights, which will increase parameters and calculations. CA is a convolutional neural network utilizing multiple attention mechanisms, which consists of three modules: a combination of a spatial attention module, a channel attention module, and a scale attention module. It needs to introduce additional attention modules  
Figure 15: Six model training results. (**a**) Accuracy curve; (**b**) loss curve. to calculate spatial, channel, and scale attention, which will lead to complex networks and more difficult calculations. CBAM is a module that integrates channel attention and spatial attention, which can dynamically adjust the importance of convolutional features. CBAM can consider the attention of both channel and space dimensions at the same time to better capture important features in the image, but CBAM needs to introduce additional modules to calculate channel and spatial attention, which will increase the complexity of the network and calculation volume. Efficient ECA can be embedded into existing CNN architectures to improve their performance. Its core idea is to use one-dimensional convolution to obtain local interaction information across channels and to control the coverage of the interaction by automatically adjusting the size of the convolution kernel. ECA avoids dimensionality reduction operations, thereby preserving complete information between channels. ECA uses fast 1D convolutions to achieve cross-channel interaction, which reduces the computational cost and parameter volume. In Section 3.2, we explored the improvements in GhostNetV2. First of all, we considered that the Ghost module would lead to the insufficient learning ability of the model, so we optimized Ghost and added a channel shuffling operation in the Ghost module to enhance the information flow between feature maps. In view of the long training time of the model, we concluded from EfficientNetV2 that the depthwise convolution of the shallow layer of the network would lead to low model training efficiency, and the use of ordinary convolution in the shallow network could significantly improve the training speed, so we replaced the first five layers of the GhostNetV2 network for Fused-MBConv, the training time of the model was significantly reduced and the training efficiency was enhanced. Finally, we utilized ECA to further improve the performance of the model and reduce the parameters of the model. Although the calculation of the improved model became larger, its accuracy was higher and the training time was shorter, which was worth it. We further tested the model with three public datasets (Rice Leaf Diseases Dataset, Plant-Village, and LWDCD2020) to evaluate its practical effect and generalization ability. The Rice Leaf Diseases Dataset contains images of rice leaves from different regions, with four different categories: healthy leaves, bacterial blight, brown spot, and sheath blight. Each category contains 300 RGB images with a resolution of 512 \(\times\) 384 pixels, for a total of 1200 images. The Plant-Village dataset contains 54,303 pictures of plant leaves, which can be used to identify 38 plant disease datasets. LWDCD2020 contains about 4500 images covering four classes of wheat diseases. Table 5 shows the test performance of the improved model and four lightweight models on three public datasets. The test accuracy of the improved model on the Plant-Village, LWDCD2020, and Rice Leaf Diseases datasets reached 98.12%, 97.87%, and 99.12%, respectively.