## 1 Introduction  
Smart agriculture has rapidly developed in recent years, which not only makes agricultural production more convenient and efficient but also reduces the labor intensity of agricultural activity [1; 2; 3]. Since hands-free farmland practices started in Britain in 2017, unmanned farms have become the main form of smart agriculture implementation. It is commonly known that hands-free farmland systems are closely tied to efficient and intelligent agricultural machinery navigation systems. Recently, due to the continuous reductions in costs for high-accuracy global navigation satellite system (GNSS) equipment, many farmers have sought to integrate self-driving systems into their agricultural machinery. The existing works on automatic navigation systems (ANSs) have promoted agricultural automation significantly, wherein the vehicle is controlled by automation and drives along a predefined straight or curved path [4; 5]. For adequate unmanned farm construction, agricultural machinery should be able to conduct unmanned work with whole-field coverage [6; 7]. However, research on how to design a suitable ANS for whole-field autonomous work is rarely reported in the existing references, especially regarding navigation decisions and path-tracking algorithms that can easily be implemented, which form the main focus of this work. There has been a large number of studies on self-driving system designs for agricultural machinery. To achieve fully unmanned operations, a prerequisite is that the ANS should be able to perform tasks such as perception, planning, and motion control [8]. [PERSON] et al. designed an automatic navigation system for a fruit-picking platform based on the Beidou navigation system and the Stanley model, and the maximum lateral deviation was 10.2 cm for straight-line path tracking [9]. [PERSON] et al. proposed a variable-domain fuzzy controller to enhance the adaptability of a preview control model, which can control unmanned drive sprayers on different road conditions [10]. [PERSON] et al. developed an unmanned weeding robot by integrating a self-driving system with the chassis of a high-clearance rod sprayer and reported on the required automatic path-planning and path-tracking algorithm; the maximum lateral tracking error reached 20 cm [11]. The above-mentioned ANSs were developed for specific tasks and tested under simplified conditions, and most authors did not provide detailed information on how to solve the position offset problem in navigation decisions. Recently, [PERSON] et al. designed an unmanned high-clearance sprayer by considering the position offset coming from chassis inclination and also provided a specific sensor calibration method for real-time kinematic (RTK) antennas [12]. In order to solve the problem of inconsistent trajectories between the design of a transplanter and its implementation, [PERSON] et al. proposed a positioning correction method for improving the navigation decision accuracy of a rice transplanter [13]. In order to enhance the reliability of a GNSS under RTK-degraded scenarios, [PERSON] et al. developed an improved information fusion method by importing the geometric characteristics of a predefined path and a path-tracking error into the Kalman filter, improving the robustness of the GNSS by constructing pseudo-measurements [14]. In the implementation of ANSs for different working scenarios, researchers have recently paid attention to reliable and accurate path-tracking algorithms. Traditional path-tracking algorithms are generally divided into two categories. One category is tracking control algorithms that integrate vehicle models and control theory, such as proportion-integration-differentiation (PID) [15], model predictive control (MPC) [16], and sliding-mode control (SMC) [17]; the other category is based on geometric models, such as pure pursuit (PP) [18] and the Stanley model (SM) [19]. [PERSON] et al.