A review of vision-based crop row detection method: Focusing on field ground autonomous navigation operations.

• A comprehensive overview of crop row detection method in crop management and canopy phenotyping. • Focus on visual perception methods for seedling stage crop row in field ground operation scenarios. • Representative devices, models, and algorithms for the crop row detection process are analyzed. • Discussed challenges and possible solutions to crop row detection. Crop row detection technology is widely used in field management operations, crop phenotyping, and other fields. The crop row detection method obtains guidance information from field roads through relative positioning. This approach can effectively mitigate the constraints associated with absolute positioning methods, such as GNSS guidance, and ultimately improve the autonomous positioning ability and navigation accuracy of agricultural equipment. Researchers have extensively explored methods for quickly, accurately, and robustly identifying the orientation features of crop rows within field environments. As a result, many algorithms and models have been established based on vision technology. The primary aim of this paper is to provide a comprehensive overview of the status of crop row detection methods, with a specific emphasis on visual perception techniques for seedling crops utilized in field ground autonomous navigation operations. The typical process of the crop row detection method is summarized in detail, including crop image data acquisition, crop canopy feature extraction, and crop row centerline detection. The representative devices, models and algorithms involved in each process are analyzed. The challenge of crop row detection and the restrictive issues of current research methods are discussed. The future research direction and potential solutions are proposed. [ABSTRACT FROM AUTHOR]