# Detection of Wheat Yellow Rust Disease Severity Based on Improved GhostNetV2
## 1 Introduction
proposed a detection method for wheat head blight based on chlorophyll-related phenotypes and machine learning, using hyperspectral reflectance, chlorophyll fluorescence imaging, and biochemical parameters to extract features related to head blight, and using a feature selection algorithm and multivariate k-nearest neighbor model to detect and classify head blight. This method can detect scabs at an early stage and help control the spread of the pathogen and optimize fungicide application. The method based on RGB images uses the color characteristics of plant leaves or ears in the visible light range, combined with deep learning and other methods, to identify and classify different types of severity of diseases. [PERSON] et al. [16] developed a new red-edge head blight index (REHBI) to detect and evaluate the severity of FHB in wheat canopies at a regional scale. [PERSON] et al. [17] used a large annotation database taken by a standard RGB camera to detect and classify stripe rust on wheat leaves, using the U2 Net model and the deep residual neural network (ResNet) model to achieve 90% accuracy. [PERSON] et al. [18] proposed a computer model for detecting and classifying wheat yellow rust, named Yellow-Rust-Xception, which can judge whether wheat has yellow rust according to the structural changes in wheat leaf images and how bad the yellow rust is, and obtained a high accuracy rate. In addition, some researchers have utilized segmentation techniques to assess disease severity. [PERSON] et al. [19] proposed a deep-learning-based semantic segmentation method for segmenting stripe rust regions on wheat leaves and automatically calculated the macroscopic disease index (MDI) for measuring canopy disease incidence. [PERSON] et al. [20] proposed a method based on an automatic serial double BlendMask network for segmenting wheat ear and head blight area, and divided head blight into four grades according to the ratio of lesion area to the total area. [PERSON] et al. [21] proposed a deep-neural-network-based scab diagnostic system capable of identifying and localizing four infection levels and generating descriptive phrases. Hyperspectral and multispectral technologies have the advantages of high precision and high resolution in agricultural disease detection, but they still have shortcomings, such as high costs and difficult data processing and analysis. Conventional deep learning networks may face disadvantages, such as high model complexity, long training times, high hardware resources, and energy consumption, in agricultural disease identification. A lightweight convolutional neural network can reduce the amount of calculation and memory usage required by the model while ensuring the recognition accuracy by weighing factors, such as model parameters and computational complexity, and realizing efficient disease identification and classification on the mobile terminal. There are advantages to real-time mobile applications. The mobile terminal has become more and more mature. The mobile terminal uses image processing technology and deep learning methods to conveniently and precisely identify crop diseases, assess the severity of diseases and track disease progress, and provide effective prevention and control suggestions while reducing production costs, protecting the environment, and improving the market competitiveness of agricultural products. Additionally, through a lightweight convolutional neural network model to achieve a balance between recognition accuracy, computing speed, and network size, it can quickly and cheaply disseminate information to other people in remote areas and strengthen contact with human experts. Therefore, the identification of agricultural diseases on the mobile side of the lightweight network is a worthy study. The severity of wheat yellow rust is difficult to automatically detect, and the traditional deep learning network consumes a lot of money, which is not suitable for mobile and edge terminals.