Abstract: In order to obtain the Python image recognition model for detecting soybean diseases through effective training, and ensure the sample size, diversity and image acquisition accuracy, the Python crawler technology is used to write the soybean disease image acquisition program, combined with the data augmentation method, expand the data amount on the basis of the target image obtained by the crawler, and write the Python language feature matching program to accurately screen the pictures. The results showed that the collection of soybean disease images by reptilian technology could accelerate the acquisition speed of soybean mosaic disease, gray spot, sclerotia, downy mildew, root rot, bacterial keratosis, wilt and anthrax, and improve the diversity of the dataset. After the local binary mode processing, combined with the difference of texture features, the scope of judgment was effectively reduced, and the difficulty of similarity discrimination was reduced. After calculating the similarity by the mean hash algorithm, the accuracy of the screened images of sclerotium and wilt was 100%, the accuracy of root rot and gray spot images was 83.3%, and the accuracy of other images was above 90%, so after calculating the similarity by the mean hash algorithm, the accuracy of obtaining disease pictures was greatly improved. The data enrichment code was written in Python language, and after several processes such as image rotation, flipping blur, increasing noise, and changing brightness, it reached 17 times amplification, and 2 592 soybean disease images were finally obtained. This study improves the accuracy of soybean disease image data collection, and provides a technical reference for the automatic identification and diagnosis of common soybean diseases.