# Detection of Wheat Yellow Rust Disease Severity Based on Improved GhostNetV2
## 1 Introduction  
Wheat is a worldwide high-economic crop with rich nutritional value and is one of the important sources of food and industrial raw materials for human beings. Diseases have a great impact on the yield and quality of wheat, and yellow rust is a very harmful disease. Wheat yellow rust is a disease caused by fungi, which has a high incidence and rapid spread under suitable temperatures and moisture and poses a serious threat to wheat yield. The characteristics of emerging yellow rust are similar to the characteristics of natural or mechanical damage to leaves. If it is misjudged as disease-free and the disease is allowed to continue to infect, the wheat yield will inevitably decline. However, farmers usually judge the degree of damage based on the size of the diseased area on the leaves, but this method is prone to misjudgment, resulting in the excessive use of pesticides or failure to achieve the level of pest control, which will reduce crop production and cause economic losses. Thus, the timely and accurate judgment of wheat yellow rust and the degree of damage is an urgent problem to be solved [1; 2; 3; 4; 5]. The assessment of the severity of wheat diseases is of great significance for preventing and controlling the spread of diseases and improving the yield and quality of wheat [6]. The assessment of traditional wheat disease severity mainly relies on manual diagnosis, which is not just time-consuming and labor-intensive but also subjective and error-prone. Currently, the most professional detection of agricultural diseases is based on biochemical tests, of which polymerase chain reaction (PCR) is commonly used [7]. However, using remote sensing technologies, such as hyperspectral, multispectral, or RGB images, combined withmachine learning and other methods to realize automatic, accurate, and rapid assessment of wheat disease severity, is a current research hotspot and development trend. The method based on hyperspectral and multispectral images uses the reflectance characteristics of plant leaves in different bands, combined with regression models such as partial least squares regression (PLSR), support vector regression (SVR), and Gaussian process regression (GPR) to estimate disease severity on wheat leaves or canopy. [PERSON] et al. [8] proposed a monitoring method for wheat leaf rust based on hyperspectral reflectance. [PERSON] et al. [9] introduced a spectral feature extraction method based on continuous wavelet analysis (CWA) to detect the severity of wheat yellow rust. Compared with traditional spectral features, this method has higher accuracy and speed. [PERSON] et al. [10] compared the performance of three regression models in detecting wheat leaf rust. [PERSON] et al. [11] proposed a method for detecting wheat diseases and insect pests using hyperspectral data and machine learning methods, which have high accuracy and robustness and are superior to traditional methods. [PERSON] et al. [12] introduced a detection method for wheat powdery mildew based on hyperspectral imaging and machine learning. [PERSON] et al. [13] used continuous wavelet transform and random forest recursive feature elimination to select consistent spectral bands and developed a canopy-based difference index to differentiate healthy and infected wheat canopies and an estimation of head blight severity. [PERSON] et al. [14] proposed a multi-scale spectral index (SI)-based monitoring method for peanut leaf spot disease, using hyperspectral data to construct and screen SI related to leaf spot disease and using machine learning classifiers to evaluate its detection capabilities. The method has high accuracy and stability at different scales. [PERSON] [15] et al.