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Rolling quality is a key index of the cotton quality, which directly influences the quality of the lint and textiles, however, it is mainly decided through visual classification by skilled personnel. In order to realize the intelligent rapid classification of cotton quality, this paper proposed a decision-level fusion recognition method for the cotton quality grade based on colored-image information. After the preprocessing of images, RGB and HSV features were calculated, respectively. The features are normalization processed and principal component analysis (PCA) is employed to extract the greater contribution features of RGB and HSV images, which are adopted as BP neural network (BPNN) input parameters to identify the quality grade recognition of cotton, respectively, and then output parameters of BPNN are used as independent evidence to construct Basic Probability Assignment (BPA). Finally, D-S Theory is used to obtain the decision fusion and realize the high accuracy the recognition of cotton quality grades. The compared experimental results show that the precision of proposed method is significantly superior to classification using RGB and HSV features respectively. The method provided in this paper can realize the intelligent rapid classification of cotton quality, and proves the feasibility of cotton-graded artificial intelligent classification.
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