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Smart agriculture is the development direction of modern agriculture, and unmanned smart farms are an important way to achieve smart agriculture. Unmanned smart farm is an important direction for agricultural transformation and upgrading, and its precise and efficient operation quality depends on the accuracy and reliability of farmland boundary recognition technology. The technical system and workflow methods of farmland boundary recognition were systematically reviewed, with a focus on analyzing the characteristics and application scenarios of three types of data acquisition methods: satellite remote sensing, UAV remote sensing, and ground-based sensing. The advantage of satellite remote sensing lies in its wide-area, periodic monitoring capability that supports large-scale farmland change analysis, though its spatial resolution is limited. UAV high-resolution images, when deeply integrated with ground sensors ( LiDAR point clouds and RGB image registration), can achieve centimeter-level boundary segmentation, providing high-precision data support for complex farmland scenarios, but its field of view is limited. Traditional image processing algorithms (threshold segmentation, edge detection) offer real-time advantages in regular farmlands but struggle with scenarios involving objects of similar spectra and static element occlusions. Deep learning-based models such as U Net and DeepLab, through multi-scale feature fusion and attention mechanisms, significantly enhance the robustness of irregular boundary recognition. Current technologies support the construction of digital maps and agricultural machinery path planning. However, there are still three main bottlenecks: insufficient spatio- temporal alignment accuracy of multi-source data, resulting in low fusion efficiency; slow inference speeds of lightweight models on edge computing devices, which failed to meet real-time operation demands; and the lack of dynamic farmland boundary update mechanisms, restricting long-term monitoring effectiveness. Future research should focus on multi-modal spatio-temporal feature fusion, lightweight model technologies driven by edge inference, and a framework for autonomous updating of digital farmland maps supported by air space ground collaboration, to provide theoretical support for high-precision, high-response, and high-dynamic boundary recognition in farmlands.