Abstract: Abstract: Late blight is a serious disease that occurs of potato, which can reduce the yield and even kill the crop. Therefore, the recognition and control of potato late blight is of important practical significance to improve potato yield. Based on machine vision technology, a rapid recognition method of potato late blight was proposed in this paper. According to the different characteristics of the color, texture and shape of late blight on the potato leaves, the characteristic parameters of the lesion areas on leaves were extracted, and the mathematical model was established to evaluate the disease. The potato leaves of Xiazhai No.8 were selected and inoculated with phytophthora infestans in the artificial climate chamber. The image information of potato leaves was collected by image acquisition system, and the collected images were preprocessed by median filtering algorithm, eliminating noise interference while retaining more complete leaf color information. The Grab Cut algorithm was used to separate the foreground and background of the image and extract the image of the potato leaf. The image was binarized by the OTSU method, and the lesion information was initially extracted. In order to remove the noise in the image and make the edge of the extracted lesion smoother, the open operation was selected. For the recognition based on color features, in the RGB and HSV color spaces, according to the change of leaf color of potato leaves in early stage of disease, the disease-free and disease model of potato blight was established by using color features. The correct recognition rate of the model in early stage of disease was 67.5%. For the recognition based on texture features, using the gray level co-occurrence matrix and the statistical parameters of texture features to evaluate the disease level, using entropy and energy values to describe whether the potato leaves were in the late stage of disease, using contrast ratio and entropy to judge the disease degree, the recognition rate of texture feature to the disease was relatively stable, and the recognition rate of middle and late stage of disease was more than 70%. For recognition based on shape features, using the relative characteristics of the shape features, i.e. the area ratio of the lesions to judge whether the late blight was, and the recognition rate was as high as 90%. Traditionally, the judgment of potato late blight mainly depends on human eyes, which is difficult to quantify the degree of leaf disease, and requires experienced disease diagnosis experts, often misdiagnosed, missed diagnosis, and it takes a long time to detect the pathological value of potato late blight, but using machine vision to detect potato late blight is relatively fast and accurate. The comparative test results show that the recognition time for potato late blight based on color features was about 4 s, the recognition time based on texture feature was 7 s, the recognition time based on shape feature was 3 s, and the recognition time for comprehensive color texture shape features was 9 s due to the large amount of calculation. This study provides a reference for the real-time detection of potato late blight, realizes the accurate identification of the disease when it appears, and achieves the purpose of timely detection and control of late blight