Abstract: Autonomous agricultural machinery has been emerged as one of the core strategies in recent years. It is ever pressing from the aging population, shrinking agricultural workforce, large-scale rural migration, and agricultural labor costs in China. Autonomous agricultural machinery can be expected to alleviate the labor shortages for the high efficiency and precision. Fully functional unmanned farm can also enhance the agricultural productivity and sustainability. In this study, a comprehensive review was presented on the technical requirements of autonomous agricultural machinery. An emphasis was also put on the key research advancements in China, including the environmental perception, high-precision agricultural mapping, autonomous positioning and navigation, path planning, and tracking control. One of the hallmark features of unmanned farms was attributed to the seamless and fully automated transfer of machinery among storage facilities and fields. Specifically, the autonomous machinery was automatically departed from its designated storage location, thus navigating the roads to perform field operations, and finally returning to park precisely at its original position in the shed after task completion. As such, the autonomous machinery was used in fields, farm roads, and agricultural sheds, indicating the great potential to farming. Nevertheless, the unmanned farm system was remained underdeveloped within the storage shed. Machinery parking, implement attachment, and departure still heavily depended on the manual intervention. Full automation over all scenarios was required the advanced positioning and navigation. Precise perception to shed environment was also necessary for the safety and efficiency during operations. Storage sheds were often densely populated with machinery, rather than the open and relatively predictable environments of fields and farm roads. The maneuvering space was limited to the more rapid and accurate perception and navigation, compared with the open fields. While the significant progress was found in the autonomous machinery under the scenarios of fields and road. The perception and autonomous parking technologies were less developed for shed operations so far. These challenges were also addressed to bridge the automation gap, and then unlock the full potential of unmanned farms. Much effort was focused mainly on the automatic parking of autonomous agricultural machinery, particularly on returning to the shed after field operations. Indoor positioning and precise navigation were also explored with the perception during automatic parking. Some procedures were involved in the indoor navigation from the shed entrance to the parking spot without Global Navigation Satellite System (GNSS) signals. The safe and efficient routes were determined for the final docking after environmental perception and machinery position. The fixed- and non-fixed routes were categorized for the positioning and navigation. Fixed-route navigation offered the simplicity, precision and stability, including the visual navigation (lane tracking and visual marker localization), rail guidance, and magnetic navigation. Safe zones of shed were also delineated to enhance the accuracy of position and parking. In contrast, the non-fixed-route navigation was provided for the superior flexibility, scalability, and adaptability to the complex and dynamic indoor environments, such as the external source-based positioning (e.g., radio frequency identification, WiFi, bluetooth, and ultra-wideband) and simultaneous localization and mapping (SLAM). Finally, the technical and practical challenges were summarized for the autonomous agricultural machinery. Specifically, the diverse technologies were integrated for the dynamic environments, particularly for the robust safety mechanisms. Future directions were outlined to integrate the multiple technologies for all-weather operation in the complex environments. Autonomous agricultural machinery can be expected to serve as the critical technology in unmanned farming. In turn, the operational quality and efficiency can also be enhanced in the modern agriculture.