# Lightweight Multiscale CNN Model for Wheat Disease Detection
## 2 Materials and Methods
### Proposed Approach
#### 2.3.4 Proposed Model  
The Inception module can expand the network width and speed up the training. The residual block can not only solve the degradation problem but also address the gradient problem. Combining these two modules reduces network complexity and redundancy while maintaining high accuracy. The attention mechanism can assess and weigh channel attention and spatial attention simultaneously, which is beneficial to improve the model's efficiency and accuracy.  
Therefore, this paper proposes a multiscale CNN information fusion model with multiple attention mechanisms, named Inception-ResNet-CE (IRCE) model, as shown  
Figure 4: CBAM module diagram. (**a**) CBAM architecture; (**b**) channel attention; (**c**) spatial attention.  
in Figure 5. This model is an efficient and lightweight neural network model suitable for mobile devices, which can efficiently and quickly identify wheat diseases in complex backgrounds. The model framework includes six Residual-CE structures (as shown in Figure 6), three Inception structures (as shown in Figure 7), three pooling layers, and a fully connected layer.  
Figure 5: IRCE model structure.  
Figure 6: Residual attention structure. (**a**) Residual-CE-1; (**b**) Residual-CE-2.  
We list all parameters of the IRCE model in Table 2. To obtain comprehensive image feature information, the depth and width of the network were expanded by adding three different Inception modules to the model. Inception-1 module contains a 1 \(\times\) 1 convolution, a 3 \(\times\) 3 convolution, and a MaxPool. To reduce the computation, two 3 \(\times\) 3 convolutions replaced the original 5 \(\times\) 5 convolutions. Branch1-branch4 has seven convolution kernels (the number of kernels is 8, 12, 24, 8, 12, 24, 24) and a 3 \(\times\) 3 MaxPool. The Inception-2 structure was combined using 1 \(\times\) 1 convolution, asymmetric 1 \(\times\) 7 convolution, and 7 \(\times\) 1 convolution in series. To reduce complexity, two 1-dimensional 1 \(\times\) 7 convolutions and 7 \(\times\) 1 convolutions decomposed the original 7 \(\times\) 7 convolution. Branch1-branch4 has nine convolution kernels (the number of kernels is 32, 32, 64, 64, 32, 64, 64, 64, 64, 32) and a 3 \(\times\) 3 MaxPool. The Inception-3 structure was used in a series-parallel combination of 1 \(\times\) 1 convolution, asymmetric 1 \(\times\) 3 convolution, and 3 \(\times\) 1 convolution. Branch1-branch4 has ten convolution kernels (the number of kernels is 64, 64, 128, 96, 96, 256, 256, 256, 96, 96) and a 3 \(\times\) 3 AvgPool.  
Figure 7: Inception structure. (**a**) Inception-1; (**b**) Inception-2; (**c**) Inception-3.  
The Residual-CE module can recombine the features of the original wheat disease image, which is beneficial to the learning of the model. Residual-CE can also map samples from high-dimensional feature space to low-dimensional feature space, and make the mapped samples still have excellent separability. The Residual-CE-1 structure includes two 3 \(\times\) 3 convolutional layers, a CBAM module, an ECA module, and an identity map. The Residual-CE-2 adds a \(1\times 1\) convolution short link to Residual-CE-1.