The expansion of high-end products in the tobacco industry and the increasing demand for product quality from consumers have created significant challenges for online tobacco testing technology. In response to problems such as the difficult removal of foreign objects from tobacco production affecting cigarette taste, various complex diseases from tobacco leaves, and difficulty in identifying cigarette packaging defects, traditional manual online detection methods are inefficient and it is difficult to ensure accuracy, which cannot adapt to the high-quality development of China's tobacco industry. From the perspective of elucidating the principle of tobacco online detection based on machine vision, this study systematically elaborates on the research status and latest progress of tobacco online detection technology based on two key aspects: the visual detection principle and deep learning models. Combined with current typical applications, this study analyzes the advantages and limitations of different visual models and deep learning detection methods, and further explores the development trend and prospects of tobacco online detection technology based on machine vision.