Abstract: Potato is one of the most significant food crops worldwide. Its production scale and quality are directly related to national food security, agricultural economic benefits, and the stability of the industrial chain. However, the conventional potato production model can rely heavily on manual labor and empirical assessment, particularly with the rapid acceleration in modern agriculture. These limitations are attributed to the suboptimal production efficiency, labor costs, subjective and inconsistent quality inspection criteria, as well as substantial resource misallocation during cultivation and post-harvesting. Consequently, the potato production is often required to shift toward intelligent and precision practices and then overcome these industrial constraints for global market competitiveness. Among them, machine vision can be expected to serve as a promising potential for the potato machinery and sorting facility, due to non-contact measurement, rapid high-throughput data acquisition, high-precision feature extraction, and objective analysis. Physiological constraints of manual inspection can be circumvented effectively, such as visual fatigue and cognitive bias. Machine vision can also integrate advanced optical sensors, digital image processing frameworks, and pattern recognition. Processing throughput can be realized to effectively reduce the expenditure for the high product quality and efficiency in the entire cycle of potato production, due to non-contact measurement, efficient data collection, high-precision feature recognition, as well as objective and fair judgment. This review aims to focus on the applications of machine vision in the key stages of potato production. Research progress and trends were summarized on mainstream techniques, such as internal and external quality inspection of potato tubers and in-field plant monitoring, using defect detection and deep learning models. It was found that current techniques of machine vision achieved high-precision target recognition and detection in these key stages. Technical challenges remained, including complex field background interference, dynamic illumination, target occlusion, disease recognition under small samples, and insufficient real-time performance for online detection. Finally, future research directions were also proposed, including multimodal information fusion of visible light with multispectral/hyperspectral data, the lightweight deep learning models for edge computing, robust algorithms resistant to complex environmental interference, as well as the intelligent inspection equipment and hardware systems in the potato field. The findings can provide valuable references to develop the machine vision in the potato industry, particularly for the machine vision and potato production. Challenges and solutions were identified to accelerate the intelligent production in planting practices for the overall productivity, thereby contributing to the sustainable potato industry in precision agriculture.