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In order to solve the problem of lack of on-line monitoring device for the quality of soybean during mechanized harvesting, the methods of image acquisition, soybean component identification and quality monitoring for mechanized harvesting were presented based on machine vision. The improved watershed algorithm was used to segment the soybean image effectively, and the color spatial characteristic values of RGB and HSV were used to classify and identify the components of the soybean image. The image acquisition system can acquire a clear soybean image in real time, segment and identify each component of the soybean sample, and calculate the real-time crushing impurity rate of mechanized harvest. The quantitative evaluation model was constructed, and the accuracy of the algorithm was tested and field experiments were carried out. The results showed that the accuracy of whole soybean seeds was 87.26% and 86.17%, respectively. The accuracy of crushed soybean grains was 86.45%, and the recall rate was 79.42%. The detection rate of soybean impurities was 85.19% and 83.69% respectively. The results of quality and performance evaluation of grain combine harvester were consistent with that of manual inspection. The results showed that the proposed algorithm can quickly, efficiently and stably identify intact grains, broken grains and impurities, and the quantization model can accurately calculate the fraction of broken impurities. At the same time, the soybean image acquisition system designed can replace the manual detection, and became an effective method for the quality evaluation of soybean combine harvester. Additionally, it can provide real-time data of the crushing miscellaneous rate of soybean during mechanized harvesting, realize visual monitoring and alarm, and provide data support for parameter adjustment of intelligent combine harvester, so as to improve the quality of soybean mechanized harvesting.