Abstract: Crop seeds are the basis of agricultural production. Seed testing, as an important tool, plays an indispensable role in all aspects of seed production, trade, and utilization. However, traditional crop seed identification methods are inefficient and require the support of manpower as well as specialized testing equipment. In contrast, machine vision technology can realize non-destructive detection of targets by simulating human visual function with high efficiency and accuracy, which helps to realize the automation and intelligence of variety identification, grading, and classification of crop seeds. The paper first briefly describes the method of image acquisition and pre-processing in machine vision technology, and gives the current mainstream processing flow by taking corn seeds as an example, then specifically describes the application of the two detection methods of traditional machine learning and deep learning in machine vision technology in the detection of crop seeds. Finally, for the research on corn unsound kernels, while dividing it into the above two detection methods, this paper gives a specific description, and also points out the current problems and the future research direction of corn unsound kernel detection.