Abstract: As one of the most significant global food crops, the production scale and quality of potatoes are directly related to food security, agricultural economic benefits, and the stability of the industrial chain. However, with the rapid acceleration of modern agricultural paradigms, the traditional potato production model which heavily relies on manual labor and empirical judgment is increasingly confronting severe operational bottlenecks. These systemic limitations manifest primarily as suboptimal production efficiency, escalating labor costs, subjective and inconsistent quality inspection criteria, and substantial resource misallocation during cultivation and post-harvest management. Consequently, driving the paradigm shift of potato production toward intelligent, automated, and precision practices has emerged as an inevitable trajectory to overcome these industrial constraints and enhance global market competitiveness. In this context, machine vision technology has demonstrated monumental potential within the domains of potato machinery optimization, field management, and automated sorting infrastructure. Characterized by non-contact measurement, rapid high-throughput data acquisition, high-precision feature extraction, and objective analytical judgment, machine vision effectively circumvents the physiological constraints of manual inspection, such as visual fatigue and cognitive bias. By systematically integrating advanced optical sensors, digital image processing frameworks, and pattern recognition algorithms, this technology significantly enhances processing throughput, minimizes operational expenditure, and guarantees stringent product quality standardization. Machine vision technology, with its unique advantages of non-contact measurement, efficient data collection, high-precision feature recognition and objective and fair judgment, has broken through the limitations of traditional manual perception and demonstrated significant potential in the field of potato machinery equipment and grading. It can effectively improve production efficiency, reduce production costs and ensure product quality, and has deeply permeated the entire cycle of potato production. Focusing on the application of machine vision technology in key stages of potato production, this paper systematically reviews domestic and international research progress and development trends concerning critical processes such as internal and external quality inspection of potato tubers and in-field plant monitoring, and summarizes mainstream technical approaches including defect detection methods and deep learning models. A comprehensive analysis reveals that while current machine vision techniques can achieve high-precision target recognition and detection in these key stages, several technical challenges persist, including complex field background interference, dynamic illumination variations, target occlusion, difficulties in disease recognition under small-sample conditions, and insufficient real-time performance for online detection. Finally, future research directions are discussed, encompassing multimodal information fusion of visible light with multispectral/hyperspectral data, the development of lightweight deep learning models tailored for edge computing, robust algorithms resistant to complex environmental interference, and the design of specialized intelligent inspection equipment and dedicated hardware systems for potatoes. The main purpose of this article is to provide valuable references for the further development of machine vision in the potato industry and promote the deep integration of machine vision technology and potato production. By addressing the identified challenges and implementing the proposed solutions, the aim is to accelerate the comprehensive mechanization and intelligence of the potato production process, promote innovation in potato planting practices, enhance overall productivity, contribute to the sustainable development of the global potato industry, and help this important crop field enter a new era of precision agriculture.