Classification recognition of impurities in seed cotton based on local binary pattern and gray level co-occurrence matrix

Abstract: When cleaning seed cotton, cleaning devices of different types had different cleaning efficiencies on different types of impurities. Therefore, the classification identification of seed cotton impurities had a guiding significance for adjusting the parameter of seed cotton cleaning equipment. A classification recognition algorithm of impurities in seed cotton based on local binary pattern and gray level co-occurrence matrix was proposed in this paper. First, the images were transformed to local binary pattern images, and so the gray value of each pixel was also converted to the local binary pattern value. Local binary pattern reflected the micro-structure of the center pixel and its 3×3 neighborhood, but it could not reflect a wider range of image structure. If the micro-structures of images were similar but macro-structures were different, the local binary pattern could not effectively distinguish the images. Gray level co-occurrence matrix was used to the statistics on the position of pixel pair. The pixel pairs had some relationship of gray values. The distances of pixel pairs could be controlled by the step length. In this paper, gray level co-occurrence matrix was used for local binary pattern image. It could describe the image structures of different scales by adjusting the step-length value. This paper calculated the characteristic values of seed cotton images and all kinds of impurities images with the step-length values from 1 to 8. The characteristics included contrast, angular second moment, correlation and inverse difference moment. The test results showed that these characteristics could distinguish seed cotton and every kind of impurity when the step-length value was equal to 3 or 4. The classifier of this algorithm used the support vector machine. In solving the small-sample, nonlinear and high-dimension problems, the support vector machine had more advantages than the traditional machine learning methods. The support vector machine was a typical two-class classifier. But classification recognition of seed cotton and impurities needed multi-class classifier. Several classifiers of support vector machine were combined into one multi-class classifier, and radial basis function was used as the kernel function of the classifier. This paper compared the standard local binary pattern algorithm (LBP), the standard gray level co-occurrence matrix algorithm (GLCM) and the algorithm designed in this paper (LBP-GLCM). The test results showed that the average recognition rate of the algorithm designed in this paper, which reached 94%, was higher than the LBP algorithm and the GLCM algorithm. Among different objects, the recognition rate of the boll shell and the cotton bush was 100%, the recognition rate of the leaf fragment was 92%, the recognition rate of the dust miscellaneous was 94%, the recognition rates of the deed cotton and barren cotton seed were 90% and 88%, respectively. The recognition rate could satisfy the demand of practical application.