Quantitative measurement is the basic work of biological research and breeding management, and its results are of great significance to the production efficiency, cost control of animal breeding and assessment of economic benefits. In recent years, with the development of image acquisition equipment, image processing technology and computer vision algorithms, the research on animal counting based on computer vision has also made great progress. Artificial counting often needs to rely on breeding personnel to observe and count the animals one by one, which is not only prone to omissions and errors, but also requires a lot of time and human resources. Computer vision-based counting methods can realize automated counting, which to a certain extent reduces the workload of breeding personnel and improves the breeding efficiency. The research related to farm animal counting in the past ten years was counted, and the farm animal counting algorithms were analyzed and discussed from both traditional machine learning and deep learning. Among them, the traditional machine learning method mainly relied on manually extracted features for recognition and counting, with fast computation speed and small resource consumption, but lacked the understanding of the global semantic information of the image;counting algorithms based on deep learning had a stronger generalization ability to complex scenes, and achieved better results in the counting task for farmed animals, which was the mainstream direction of the current research. In addition, the applications of farmed animal counting in the fields of aquaculture, livestock and poultry farming and special animal farming were sorted out and summarized. At the same time, the current publicly released farmed animal counting datasets were summarized. Finally, the main challenges of farmed animal counting research were analyzed and discussed in terms of datasets, application scenarios and counting methods, and the future development trend was outlooked. Specifically, by constructing larger and richer public datasets, improving the accuracy and generalization ability of counting algorithms, and expanding the counting models in specific scenarios to a wider range of application scenarios, the research on farmed animal counting would make greater progress and development, so as to truly play its role in supporting agricultural production.