Aquaculture counting is an important part of the aquaculture process， and the counting results provide an important basis for feeding， breeding density adjustment， and economic efficiency estimation of aquatic animals. In response to the traditional manual counting methods， which are time-consuming， labor-intensive， and prone to large errors， a large number of methods and applications based on machine vision have been proposed， thereby greatly promoting the development of non-destructive counting of aquatic products. In order to deeply understand the research on aquaculture counting based on machine vision， the relevant domestic and international literature in the past 30 years was collated and analyzed. Firstly， a review of aquaculture counting was presented in the perspective of data acquisition， and the methods for acquiring the data required for machine vision were summed up. Secondly， the aquaculture counting methods were analyzed and summarized in terms of traditional machine vision and deep learning. Thirdly， the practical applications of counting methods in different farming environments were compared and analyzed. Finally， the difficulties in the development of aquaculture counting research were summarized in terms of data， methods， and applications， and corresponding views were presented for the future trends of aquaculture counting research and equipment applications.