Advances in Crop Row Detection for Agricultural Robots: Methods, Performance Indicators, and Scene Adaptability

Crop row detection technology, as one of the key technologies for agricultural robots to achieve autonomous navigation and precise operations, is related to the precision and stability of agricultural machinery operations. Its research and development will also significantly determine the development process of intelligent agriculture. The paper first summarizes the mainstream technical methods, performance evaluation systems, and adaptability analysis of typical agricultural scenes for crop row detection. The paper also summarizes and explains the technical principles and characteristics of traditional methods based on visual sensors, point cloud preprocessing based on LiDAR, line structure extraction and 3D feature calculation methods, and multi-sensor fusion methods. Secondly, a review was conducted on performance evaluation criteria such as accuracy, efficiency, robustness, and practicality, analyzing and comparing the applicability of different methods in typical scenarios such as open fields, facility agriculture, orchards, and special terrains. Based on the multidimensional analysis above, it is concluded that a single technology has specific environmental adaptability limitations. Multi-sensor fusion can help improve robustness in complex scenarios, and the fusion advantage will gradually increase with the increase in the number of sensors. Suggestions on the development of agricultural robot navigation technology are made based on the current status of technological applications in the past five years and the needs for future development. This review systematically summarizes crop row detection technology, providing a clear technical framework and scenario adaptation reference for research in this field, and striving to promote the development of precision and efficiency in agricultural production.