As the grape production increases year by year, the quality detection of grapes in the field becomes more and more important to improve the economic benefits after flowing into the market. The traditional method of external quality detection, which mainly relies on the observation of workman, introduces non-negligible errors. The intrinsic quality detection is considered as destructive and inefficient by using the method of sugar level testing of grapes. With the development of deep learning and image processing technology, the field grape quality detection based on machine vision overcomes the limitations of traditional manual inspection and has the advantages of fast, accurate, realtime and lossless. According to grape varieties and quality evaluation indicators, a systematical analysis and summary of the research related to the nondestructive quality detection method of grapes in the field was provided based on machine vision technology. The main body consisted of two parts, which were machine vision detection methods of grape varieties and machine vision detection methods of grape quality. The common factors affecting the quality of grapes were obtained on the basis of the analysis of different grape variety evaluation factors. The intrinsic quality factors included soluble solids, total acid, total phenol and moisture content while the external quality factors included fruit size, quantity, color, and disease defects and so on. Several methods of grape variety identification based on fruit and leaf were introduced, including canonical correlation analysis, support vector machine, and deep learning. The detection method based on fruit characteristics was more accurate, while the detection method based on leaf characteristics can be applied to a longer growth period. As the variety of grapes differred, the standard of their internal and external quality also varied. A detailed summary of the research related to the non-destructive quality detection methods for the intrinsic quality and external quality of grapes in the field was provided. For the quality detection of grapes, the comparison was conducted between the traditional morphological methods such as thresholding, the edge contour search and the corner detection algorithm with the deep learning methods such as Mask R-CNN. It was concluded that the deep learning detection method held the advantages of strong scalability, fast detection speed and high accuracy. In addition, the application principle and advantages and disadvantages of nearinfrared spectroscopy and hyperspectral imaging technology in intrinsic quality detection were summarized. Hyperspectral technology outperformed in terms of accuracy, while nearinfrared spectroscopy technology had lower cost and faster analysis speed. In the field of non-destructive quality detection of grapes, machine vision algorithms based on spectral analysis still faced the challenges of complex field grape growth environment and variable daytime light. Finally, in view of the difficulty of image acquisition, insufficient multidimensional image information, and weak foundation of detection instruments faced by nondestructive quality detection methods of grapes in the field, it was proposed that it was necessary to improve the intelligent equipment for data collection and analysis while improving the machine vision algorithm, thus providing efficient tools combining software and hardware for the quality detection of grapes in the field.