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With the rapid development of biotechnology, the demand for phenotypic traits in crop breeding research is on the rise, and data-driven intelligent breeding is gradually emerging as a significant direction in breeding studies. High-throughput phenotyping equipment can efficiently acquire phenotypic traits throughout the entire life cycle of crops. However, it had become a key bottleneck that restricted large-scale and efficient crop breeding research. As an emerging type of agricultural robot, crop phenotyping robots became a vital direction for future crop phenotyping due to their multiple advantages. These advantages included flexible mobility, time and space-unrestricted operation, strong expandability with the capability to carry various types of sensors, high-resolution data collection from multiple perspectives close to the ground, and high degree of intelligence enabling unmanned or minimally manned operation. Currently, there were reviews on crop phenotyping technology and the development of agricultural robots, but there were relatively few reviews specifically focused on crop phenotyping robots. The current research status of crop phenotyping robots both domestically and internationally was firstly and systematically summarized. Based on this, it elaborated on the overall architecture of phenotyping robots, sorted out their system control and navigation methods, and introduced in detail the methods of obtaining and analyzing phenotypic traits based on robots. Finally, it discussed the current application status and challenges faced by phenotyping robots in agricultural production and crop breeding, and looked ahead to the future development trend of phenotyping robots.Finally, the paper discusses the current applications and challenges of phenotyping robots in agricultural production and crop breeding, while outlining future trends characterized by three key developments: Robotic diversity innovation will propel high-throughput phenotyping toward scaled implementation, artificial intelligence will reconstruct deep learning frameworks for phenotypic analysis, and next-generation phenotyping robots leveraging multimodal sensor fusion technology will spearhead paradigm shifts in phenomics research.