In the transition from traditional agriculture to smart agriculture, the demand for large-scale, high-precision information perception in agricultural scenarios is increasing. Traditional monocular and binocular vision, limited by their field of view, struggle to meet the requirements of all-round monitoring in complex environments. Panoramic vision technology, characterized by 360° unobstructed field of view, fast and complete information acquisition, and strong real-time performance, has become an irreplaceable key technical means in the intelligent upgrading of agriculture. This paper aims to systematically review the application status and development progress of panoramic vision systems in the agricultural field, providing theoretical reference for the in-depth integration of panoramic vision technology and agricultural equipment. Based on imaging principles and hardware structures, the systems are classified into four architectural types: pan-tilt rotating type, multi-camera array type, fisheye lens type, and catadioptric type. Combined with typical cases, the application evolution in fields such as crop monitoring, phenotypic investigation, and agricultural machinery navigation is analyzed. The research shows that panoramic vision has formed a multi-level application pattern: pan-tilt rotating systems have small distortion but poor real-time performance, suitable for static growth monitoring; multi-camera systems have high frame rates and small pose errors, capable of supporting dynamic high-dimensional phenotypic reconstruction and positioning; fisheye systems have a simple structure, and edge distortion can be suppressed through calibration to achieve wide-area monitoring; catadioptric systems are compact with no delay and small deviations in navigation and obstacle avoidance. Currently, panoramic vision in agricultural applications still faces four major issues: bottlenecks in adaptability to complex environments, shortcomings in real-time processing of dynamic scenes, insufficient efficiency and accuracy of 3D information reconstruction, and lack of technical standardization and system compatibility. Therefore, in the future, panoramic vision technology should focus on multi-modal sensor fusion perception, use deep learning-driven adaptive model optimization to improve generalization ability, and deeply collaborate with the agricultural Internet of Things to build an integrated perception-decision-regulation system. Meanwhile, strengthening technology open-sourcing and industry standardization will accelerate the large-scale application of panoramic vision technology in precision agriculture.