## 1 Introduction  
Agricultural machinery navigation technology has been widely used in the field of intelligent agriculture [1, 2]. Compared with dry fields, the stability and precision of rice transplanter navigation control in paddy fields are poor [3]. The ground structure of paddy fields is more complex, such as the mixture of mud and water, which causes the steering resistance and the poor steering control precision of agricultural machinery. Inconsistencies in the hardness of the paddy clay substrate can lead to bumpy bodywork and problems such as misalignment and sideslip. In order to improve the stability and accuracy of navigation of agricultural machines, many researchers have focused on path-tracking control algorithms, and the preview model is the basis of traditional control algorithms. With a simple geometric algorithm for setting the preview point [4], the tracking control of different paths can be realized efficiently and flexibly. Compared with the classical control algorithms, Pure Pursuit has better trackingcontrol performance at low speeds through simulation and comparative analysis [5]. The preview distance regulation strategy using PID [6] can minimize the preview error, which is characterized by a simple structure and a wide range of applications, but under transient conditions, the fixed PID weighting coefficients will cause the system to fail to respond in time, leading to an increase in the error. These methods are only applicable to navigation control under steady-state conditions. Currently, control methods based on optimal control theory, such as linear quadratic regulator (LQR) [7] and model predictive control (MPC) [8], are widely used in various path-tracking scenarios. The cost function of the optimal control is composed of the errors of the variables within the preview model and has excellent performance for error prediction in dynamic environments. However, in nonlinear scenarios and complex environments, it can be computationally time-consuming or even without usable solutions [9]. Adaptive control methods incorporating deep learning are current research breakthroughs. In the application of system modeling, the method of the hybrid model can be used to correct the modeling error and obtain a high-accuracy system model [10]. In multi-degree-of-freedom complex systems, the use of radial basis neural networks (RBNNs) to adjust the state variables can effectively improve the stability of the system [11]. Many studies have shown that control methods incorporating deep learning can effectively improve the robustness, accuracy, and adaptive capability of control systems. Deep learning methods are widely used in various fields due to their feature learning and identification capabilities. In the field of farmland environment sensing, farmland boundary information can be accurately and efficiently recognized using the Attention Gate (AG) mechanism and MultiResUnet [12]. However, it is difficult to improve the inference speed of the model due to the complex model structure and high computation. In the medical field [13] and environmental monitoring field [14], Deep Belief Network (DBN) has demonstrated strong generalization and efficient training capabilities. In practical production studies, the main manifestation pattern of rice transplanter navigation instability is oscillation, which is motivated by sideslip [15; 16; 17] and systematic multi-error coupling. Therefore, realizing deviation pattern identification and adaptive regulation is an important way to improve the stability of rice transplanter navigation. For this reason, this study utilizes a deep learning approach to design an adaptive control method for suppressing deviation patterns. The main elements of this study include:  
1. A rice transplanter navigation control simulation platform is designed. Simulating the state interference error during rice transplanter navigation operations provides a reliable and real experimental platform for subsequent research. 2. A deviation pattern identification method was designed.