# Lightweight Multiscale CNN Model for Wheat Disease Detection
## 4 Discussion  
For the three Inception modules introduced by the model, we decided to embed them in the network in a certain order. We considered that Inception-1 was the most basic Inception module, which could be used to extract features of different scales, so we placed it at the front as the basis of the network. Inception-2 was a reduction module that could be used to change the size of the feature map, thereby increasing the depth and receptive field of the network, so we placed it in the middle as a transition of the network. Inception-3 was a more complex Inception module, which could be used to extract larger-scale and finer-grained features, so we placed it at the end as the upper layer of the network. The experiments in Section 3.2 verified the correctness of our embedding methods for the three inception modules. In deep learning, the residual structure is a method to solve the degradation problem when the number of network layers increases through the identity mapping. The number of residual structures needs to be determined according to the task and dataset to balance the expressiveness and computational efficiency of the network. In this paragraph, we discuss how we tried three ways to introduce the residual structure and compared their results. The first way was to introduce four layers, each layer having two BasicBlocks. This way, although the network became relatively deep, also increased the complexity of the model and the amount of calculation. The second way was to introduce two layers, each layer having two BasicBlocks. For this way, the model complexity was low and the number of parameters was small. The third way was to introduce three layers, each layer having two BasicBlocks. In Table 9, Param, FLOPs, and the training time of method 1 are the highest. Although method 2 consumes fewer resources, the integrity of the model is too low, and method 3 has the highest overall model performance and the least resource consumption. Therefore, we concluded that the third way was the best one for our task. In order to improve the model's attention to the disease characteristics of wheat leaves, we tried adding the five attention mechanisms of CBAM, NAM, SE, CA, and ECA to the residual structure. First, taking into account CBAM and NAM with mixed attention mechanisms, we chose CBAM as it had a better performance after comparison. Since CBAM passes features through the channel attention mechanism and spatial attention mechanism in a linear manner, channel features are lost to a certain extent. Therefore, we added another layer of the channel attention mechanism. After comparison, ECA was better than CA and SE. We found that the combination of CBAM and ECA had the best effect on focusing on diseased regions and improved the detection performance of the model. Finally, we compared our proposed model with classical CNN models and lightweight models in Section 3. The recognition accuracy of the classical CNN model and the lightweight model of wheat diseases is low. This makes it difficult to apply pesticides accurately, which can reduce the yield and quality of wheat, and reduce the efficiency and safety of agricultural production. Our model has the advantages of high precision, a fast speed, and small parameters, and can handle the identification problems of various diseases in complex backgrounds. Our proposed model facilitates the intelligent identification and diagnosis of crop diseases by mobile terminals, discovers the types and proportions of diseases, and provides farmers with timely prevention and control suggestions. However, our method also has limitations. Our model is more accurate than classical lightweight models, but our model is computationally intensive and requires more