Abstract: Abstract: The processing technology of the machine picking seed cotton (MPSC) should be influenced significantly by the kinds and the contents of impurities. But during the MPSC processes in China, there is a lot of the mixed level and the mixed ginned, as well as the lack of online detection technology. In view of the present situations, a method for the identification of impurities in MPSC image using genetic algorithm (GA) to optimize the SVM parameters has been presented in this paper. First, in order to label three categories of the cotton fiber, the light-colored impurities and the dark impurities, the feature vectors were constructed for the image segmentation by the color characteristics of hue, saturation and intensity of pixel neighborhood, and the texture features of the average brightness, the average contrast, the smoothness, the third moment, the consistency and the entropy. GA of optimal retention strategy was used to optimize the penalty parameter and kernel function parameter to establish the SVM classifier of image segmentation, and then morphological operation such as hole filling, opening operation, closing operation was adopted. The specific operation was the use of open operation on light-colored impurities to eliminate the influence of small area noise; while the use of closed operation on dark impurities to smooth the target boundary on the basis of retaining small areas of dust. Then, for the impurity recognition process, shape features including area, perimeter, eccentricity, rectangle degree and shape factor were added to feature vectors besides color feature and texture feature of marked region; five categories of cottonseed, cotton leaf, bell shell, stiff valve and dust miscellaneous were marked; and the SVM classifier for impurity recognition by GA was established. At last, automatic segmentation and impurity recognition for MPSC image were realized using these two SVM classifiers. For the experiment MPSC was taken as the test material. The color planar array CCD camera of technical grade (MV-EM510C/M, Microvision, Inc.) with the industrial lens (M0824-MPW2) was used for the shooting system which included the LED diffuse light source of a 4-segment strip (AFT-WL21244-22W) and the light source controller (AFT-ALP2430-02). When shooting, the camera and the light source were placed in the darkroom, and the MPSC used for the test was pressed against the transparent glass plate on the other side of the darkroom. The data training and the picture test were conducted using MATLAB R2014b and libsvm-3.21 toolbox, and 60 pictures were equally used for both training and testing. The SVM multi-classifier was established during segmentation and recognition operation instead of simple binary classifier. This method utilized SVM small sample and high dimensionality learning ability, and the segmentation and recognition accuracy were further increased. Experimental results show that, comparing with the segmentation results from fuzzy C - means clustering algorithm and traditional SVM algorithm, the classification accuracy was improved significantly by using neighborhood spatial information. When the contrast of the edge of the target area in the image was weak, the suggested method could effectively avoid the situation of taking the shadow of the target as the edge which had shown great adaptability in applications. In this study, compared with the traditional SVM recognition algorithm, GA was used to automatically optimize SVM penalty parameter and kernel function parameter. SVM classifiers for image segmentation and impurity recognition were obtained through the suggested method overcame the blindness of parameter selection and the shortcomings of BP neural network generalization ability. Since the data distribution shapes of the homogeneous image in the color space are usually of similarity, and the characteristics of MPSC impurities texture are of stability, the optimal SVM parameters are common to the same kind of images, while for the other MPSC images with impurities, these two classifiers can be directly used to achieve the image segmentation and the impurity recognition automatically. The test results showed that the suggested method was suitable for the segmentation of natural impurities in MPSC image of low edge contrast and rich texture information, and its effective recognition rate of natural impurities was 92.6%. The study can provide important reference for the parameter optimization of cotton processing equipment and the development and optimization of domestic cotton picker.