A method for discriminating the appearance quality of soybeans based on the low-rank sparse (LRS) representation frame of multimodal lexicon features in the visible spectrogram is presented to accurately determine the soybean quality level. Firstly, multi-scale spatial gradient features and YCbCr color space features of the visible spectrogram of soybeans are extracted and regarded as visual vocabularies. The Kernel K-means clustering algorithm is used to form the local distribution cluster center of visual vocabularies in kernel space,thereby generating a vision lexicon. Secondly,the LRS representation method is used to couple the two type of features, thereby eliminating the effect of redundant information in high-dimensional heterogeneous modal dictionary descriptors. Finally, the LRS representation coupling multi-modal dictionary features are classified according to the metric between samples in the high-dimensional coupling space. The proposed method makes full use of multi-modal and multi-scale spatial gradient features and YCbCr color space features to describe the semantic feature attribution of appearance quality of soybeans. The experimental results show that the recognition accuracies of training set and prediction set are 92.7% and 80.1% respectively, and the discrimination accuracy of the proposed method is better than that of single-visual-mode based vision lexicon feature representation method.