# Lightweight Multiscale CNN Model for Wheat Disease Detection
### The Advantages of Lightweight Models in Wheat Disease Detection and the Work of This Article  
With the development of technology, mobile devices are becoming more and more mature. Mobile devices can use computer vision technology to intelligently identify and diagnose crop diseases from the leaves, determine the type and severity of diseases, and provide farmers with timely suggestions for prevention and control. Therefore, lightweight networks have great potential and advantages in agricultural disease detection. Lightweight network models have the characteristics of high accuracy, low parameter numbers, and computational costs, and can serve scenarios with limited computing resources such as mobile devices and embedded systems. For example, without the need for professionals or laboratory equipment, smartphones or other portable devices can be used for testing, provide reasonable suggestions for prevention and control based on the test results, and interact with human experts or other data sources to improve control effectiveness and agricultural productivity. There is no doubt that the methods summarized above have achieved favorable results in wheat disease detection. However, these methods also have some limitations, such as hyperspectral remote sensing technology having high accuracy in disease detection but requiring very expensive equipment; large-scale network models being effective in disease detection but being difficult to run on mobile devices; environmental factors such as wind, temperature, and humidity that can affect the flight stability and safety of drones; the types of diseases studied being relatively limited, and the research on wheat disease detection under complex backgrounds being insufficient; and the coexistence of multiple different diseases and occluded diseases that are difficult to identify. To solve these problems, this paper develops a wheat disease identification method with a simple structure, a small amount of calculation, strong generalization ability, wide applicability, and that can be equipped with mobile devices. This method is of great significance to help farmers identify wheat diseases and improve wheat yield and quality. The contributions of this study are as follows: We design a lightweight Inception-ResNet-CE model for the automatic identification of wheat diseases on mobile and edge terminals. CE is composed of CBAM and ECA attention mechanisms.  
1. We combine three Inception structures with residual structures, which can increase the depth and receptive field of the network, aggregate image information at different scales, and rapidly extract disease features.
2. We introduce the CBAM and ECA attention mechanisms into the residual blocks in the Inception-ResNet model to enhance the model's ability to capture disease characteristics and reduce the interference of complex backgrounds in images on model recognition performance.
3. The Inception-ResNet-CE model has only 4.24 M parameters and achieves a recognition accuracy of 98.78% on the validation dataset. It can be applied to the automatic recognition of wheat diseases on edge terminals or mobile devices.  
The rest of the paper is organized as follows: Section 2 presents the experimental data and the proposed Inception-ResNet-CE model; Section 3 presents our five sets of experimental results; Section 4 discusses the optimal structure of our model; and Section 5 summarizes our work and looks at future research directions.