During crop growth, it is often infected by external factors such as pests and diseases. If effective monitoring, diagnosis and scientific control can’t be carried out, it will easily leds to improper or excessive spraying of pesticides. It will not only affect the yield of crops and the economic benefits of farmers, but also cause serious environmental pollution. In recent years, a serious muskmelon leaf spot caused by Cercospora citrullina occurred in Guangxi, which leads to yield reduction and economic losses. In this study, hyperspectral imaging technology was used to detect muskmelon Cercospora leaf spot. Hyperspectral images of healthy leaves and diseased leaves with varying degrees of lesionwere collected at 380~1 000 and 900~1 700 nm. Regions of interest were selected and the corresponding average reflectances spectra was obtained. It was found that the mean reflectance of healthy leaves and the diseased leaves were significantly different and changed regularly according to the degree of lesion. Near 540 nm, the spectra of healthy leaf and leaf with slight lesion had a peak, which disappeared gradually with the increase of lesion degree. In 700~750 nm, the leaf reflectance curve increased sharply, and there was a significant “red edge effect” of green plant spectral curve. In the range of 750~900 nm, the reflectance spectra of healthy leaves and leaves with mild lesions changed steadily. The reflectance of healthy leaves was higher than that of the lesion area. The reflectance decreased with the increase of lesion degree. And this change regularity lasted until 900~1350 nm in the near infrared region. Principal component analysis (PCA) and minimal noise fraction (MNF) were used to observe the characteristics of early leaf lesions. After pretreated with PCA and MNF, the area of infection was more obvious, especially for early lesions. Three-dimensional scatter plot was drawn based on the scores of the first three principal components extracted from hyperspectral images. Although some samples with different degrees of lesion overlap, the distribution of lesion samples and healthy samples is distinct. K-nearest neighbor (KNN) method and support vector machine (SVM) were used to establish the discriminant models. The correctness of KNN model for healthy sample discrimination in the test set was 98.7%. And the discriminant rate of lesion samples increases with the severity of lesion. For the lighter lesion samples, SVM model has higher discriminant accuracy and better classification effect than KNN model. Generally, hyperspectral images had a high discriminant rate (>97%) for healthy samples and lesion samples, however, the discrimination of different lesion degrees is not good enough. It can be concluded that hyperspectral imaging technology can be used to detect muskmelon Cercospora leaf spot disease, but the discrimination of different lesion degrees still needs to be improved in the future.