Agricultural tractors are connected with various implements such as plow, baler, rotovator, and loader for performing agricultural work. In particular, the rotovator is used to crush and uniformly spread the soil after plowing operations. However, achieving uniformly spread soil and flattened fields using rotovator can be extremely challenging, because the soil is often pushed to a particular side or remains on a slope owing to the variations in soil composition, plowing depth, and the skill level of workers, which consequently affects the transplantation work. This study aims to analyze the prediction accuracy of the implement and tractor attitudes as a reference standard by (a) developing an algorithm to predict the implement attitude based on the hitch height, support points of the lower link and lift rod, and distance between the lower links, through four-section link mathematical modeling for a three-point hitch system, and (b) using an observer to predict the tractor attitude using a Kalman filter with gyroscopic and acceleration sensors. We developed a control algorithm using the gyroscopic and acceleration information from the sensor to improve the precision and adjustment speed of the conventional tractor–implement leveling-control system. In addition, the performance improvement was verified by comparing the conventional and proposed systems. The results revealed that the error rates in the proposed system were up to 72% less than those of the conventional system, implying that the control performance of the stated system could be improved by reducing the implement attitude estimation error and tractor attitude measurement delay.

Research on dual-clutch transmissions in agricultural tractors is on the rise. Dual-clutch transmissions provide a smooth driving experience and easy operation compared to manual transmissions, as they eliminate power interruptions. Accordingly, there is ongoing research to improve the conventional synchronizer pre-selection method. The study introduces a new control strategy to enhance shifting quality by focusing on shift force and timing. Specifically, the goal is to optimize the impact of pre-selection-induced shock and delayed shift time, minimizing discomfort for the driver. According to the experimental results, it was determined that factors influencing the impact of preselect engagement on gear shift shock were absent, while factors capable of affecting shift time were identified in the form of shift force. However, it was concluded that optimizing shift force and shift timing based on the number of shifts can enhance the quality of gear shifting. Even during preselect disengagement, the factor influencing gear shift shock was identified as the shift timing, with no discernible factor affecting shift time. Nevertheless, it was judged that optimizing shift force and shift timing based on the number of shifts can lead to an improvement in gear shifting quality. In conclusion, the control strategy determined through the parameter study is expected to improve the shifting quality of tractors equipped with dual-clutch transmission. These findings have the potential to support efficient and convenient tractor operation in the agricultural sector.

This study proposes a data-efficient surrogate modeling approach for predicting hydrostatic transmission (HST) system efficiency in tractors using minimal data. Only 27 samples were selected from a dataset of 5092 measurements based on the minimum, mean, and maximum values of the input variables (input shaft speed, HST ratio, and load), which were used as the training data. A hybrid prediction model combining deep kernel learning and a residual radial basis function surrogate was developed with hyperparameters optimized via Bayesian optimization. For performance verification, the proposed model was compared with Neural Network (NN), Random Forest, XGBoost, Gaussian Process (GP), and Support Vector Regressor (SVR) models trained using 27 samples. As a result, the proposed model achieved the highest prediction accuracy (R² = 0.93, MAPE = 5.94%, RMSE = 4.05). Process, SVM (Support Vector MA). These findings indicate that the proposed approach can be effectively used to predict the overall HST efficiency using minimal data, particularly in situations where experimental data collection is limited.