Abstract: Abstract: Field-Road Trajectory Segmentation (FRTS) is one of the important tasks of agricultural machinery. A sequence of field-road segments of a trajectory can be automatically divided for the big data in precision agriculture. The parameter of the FRTS model can also determine the segmentation accuracy and precision. However, the traditional parameter selection cannot obtain the superior solution, limiting the segmentation performance of the model. Therefore, this study aims to investigate the performance improvement of the FRTS model from the perspective of parameter optimization. Two aspects were mainly contained as follows. Firstly, the metaheuristic algorithms were used to determine the parameter configuration of the model. The classification accuracy was considered as an objective to transform the parameter into a single-objective optimization. Specifically, the parameter structure of the model was abstracted as the searched individual of the optimization. The reasonable fitness function was set, according to the metrics of FRTS evaluation. Then, the fitness was used to evaluate the search of the individual in the solution space. The location of the searched individual was also continuously adjusted, according to the calculation rules of the optimization. As such, the global optimal parameter structure was achieved to converge. Secondly, a Multiplex Oscillation Slime Mould Algorithm (MOSMA) was proposed to realize the parameter optimization with the nonlinear characteristics and multiple locally optimal solutions. A dynamic guidance strategy was also established to adaptively change the individual movement for the better exploitation capability of the model, according to the search process of the population. Then, a strategy (called multivariate oscillation) was proposed to improve the segmentation performance and exploration capability of the model. Different search paths were utilized to produce multiple oscillations before the individual moves, and the priori rule was then to evaluate the qualities of paths. As such, the path with the highest quality was selected to move. The synergy of the two strategies enhanced the optimization capability of the model. Dynamic guidance and a multiplex oscillation strategy enhanced the oscillation contraction patterns of the slime mould and the process of the cytoplasm flows, thereby improving the optimization performance of the model. The experiments were also performed on real agricultural trajectory datasets with different sampling frequencies. A comparison was then made with the Grid Search (GS) and Particle Swarm Optimization (PSO) to validate the effectiveness of the model. The experiment results show that the new optimization effectively improved the accuracy and performance of the FRTS model using direction distribution (BDFRTS). The average accuracy of the MOSMA-BDFRTS on high-density trajectory data was increased by 25 percentage points and 28 percentage points compared with GS-BDFRTS and PSO-BDFRTS. MOSMA-BDFRTS achieved more competitive results on low-density trajectory data, whose average accuracy of segmentation was improved by 17 percentage points and 14 percentage points compared with GS-BDFRTS and PSO-BDFRTS. The proposed method provides a generalized parameter optimization solution for field-road trajectory segmentation models, and it can be applied directly to other types of model instances. The study also provides a reference for the research of the agricultural machinery movement trajectory segmentation technology.