Aiming at the problems of labor intensity, strong subjectivity and low detection efficiency in the current manual sensory inspection of cotton preparation, a machine vision-based cotton preparation inspection system was designed. The system consisted of cotton pressing mechanism, image acquisition mechanism, detection processor, detection control board and touch screen. Firstly, a low-angle direct lighting system and an image acquisition mechanism were designed, where the LED light source was illuminated at an angle of 45° to the normal of the inspection window, and the industrial camera collected cotton images through the optical glass. Then the system adopted image texture features to express the appearance morphology of cotton, and established a relationship model between image texture features and appearance morphology by measuring the angular second moment of cotton preparation sample standards. In the adaptive filtering and Canny algorithm, it integrated the noise point evaluation and the high and low threshold adaptive methods for image filtering and segmentation identification, and the ginning quality level determination was made according to the Euclidean distance. Finally, cotton samples were selected for system performance test verification. The results showed that the angluar second moment of the ginning quality physical standards P1, P2 and P3 were [0.8932, 1], [0.6891, 0.7761], [0.2136, 0.5873], respectively, and the difference in the texture eigenvalues of the angular second moment between the grades was obvious, which verified the feasibility of the image texture to express the appearance and morphology of cotton. The relative deviation of the inspection of the number of defects index of the system was 0.15, and the separation effect of defects and background was obvious. Compared with the national standard inspection method, the detection accuracy of the preparation visual system reached 94.20%, and the detection deviation was not more than 1 preparation grade, which was in high consistency with the national standard inspection results. The detection time of single cotton sample system was 1.2s, and the detection efficiency was improved by 77.36%. The system can meet the requirements of field use, and provide a technical reference for the instrumental detection of cotton preparation indexes.