In order to realize the precise control of ploughing resistance and improve the traction efficiency of tractor in ploughing operation, a prediction method of ploughing resistance was proposed. A plowing resistance perception model based on the upper pull rod force was proposed, and the experimental verification was carried out. In view of the problem that the measurement results were unstable by only sensing the ploughing resistance through the upper pull rod force in the actual field operation, a ploughing operation parameter test platform was built, and the multi-source heterogeneous sensor data based on tillage depth, upper pull rod force, vehicle speed and wheel speed were obtained and the prediction samples were constructed. The wavelet threshold denoising (WTD) and sparrow search algorithm (SSA) were introduced into the least squares support vector machine (LSSVM), and the combined prediction model of ploughing resistance based on WTD-SSA-LSSVM was established and the model performance was verified. The results showed that the model prediction method had higher accuracy than that of the upper pull rod force. In addition, different prediction methods were compared. The R², MAE, RMSE and MAPE of the test set obtained by the combined prediction model method were 0.97, 118.1 N, 151.4 N and 2.2%, respectively. Compared with prediction models of LSSVM and SSA-LSSVM, R² was increased by 8.9% and 5.4%, respectively. MAE was decreased by 49.7% and 42.2%, respectively. RMSE was decreased by 46.7% and 39.1%, respectively. MAPE was decreased by 56.8% and 48.8%, respectively. Therefore, the method proposed had better prediction performance and was more suitable for the prediction of tractor plough resistance.

Accurate tire-soil models are fundamental to optimizing the traction force control of hybrid tractors, thereby improving overall energy efficiency. Among these, precise estimation of the longitudinal and vertical forces is crucial for ensuring model identification accuracy. However, during operation, terrain variations and the unknown load changes caused by the interaction between the plow body and the soil lead to significant fluctuations in the longitudinal and vertical forces on each tire, which increases the difficulty of accurate estimation. To address this, a tire-soil interaction model identification method based on the fusion of multiple filters and joint observation was proposed, taking the distributed hybrid electric tractor (DHET) as research object. Firstly, the longitudinal force of the tire was estimated by using the Kalman filter (KF), and the vertical force was estimated by using the cubature Kalman filter (CKF) algorithm combined with the dynamic model. Then, based on the Brixius model and the vehicle's longitudinal dynamics, an online identification device for the tire-soil model was constructed by using the unscented particle filter (UPF). The unscented transformation (UT) was used to design the particle proposal distribution, preserving higher-order information in the nonlinear tire-soil model, and the particle filter (PF) algorithm was applied to update the estimated parameters, completing the model's online identification. Hardware-in-the-loop (HIL) test results showed that the method can quickly and accurately achieve identification under varying terrain conditions, with a root mean square error not exceeding 1.2. Experimental results indicated that, under two types of soil conditions, the identification model's boundary traction coefficient error was within±0.015, with the front wheel test data distribution accounting for 84.45% and 88.16%, respectively, and the rear wheel data for 86.72% and 85.38%, verifying that this method maintained high accuracy and robustness under different soil conditions. The research findings can provide theoretical support for the optimal drive force distribution strategy of tractors and contribute to improving traction efficiency and fuel economy.

The operation state of agricultural machinery is a key indicator for assessing the efficiency of agricultural mechanization and precise management. To realize the precise monitoring of the operation state of farm machinery by the data management platform, an operation state identification method was proposed based on the multidimensional features of the spatial track of farm machinery. Firstly, based on the big data management platform supported by the modern information technology of Internet of Things, the potential characteristics of the trajectory points in the operating space of agricultural machinery were studied, and the distribution laws of the characteristics such as speed, acceleration, steering rate and distribution density of trajectory points were analyzed. Secondly, based on the distribution characteristics of each feature and the demand for operation state recognition, a multi-strategy split-box processing method was used to quantitatively divide the features, and weight of evidence (WOE) and information value (IV) methods were introduced to quantify the influence weight of different features on the operation state of the farm machinery, so as to assess the impact on operation state the key features of the recognition ability were evaluated. Finally, based on the multidimensional key features of the spatial trajectory points of the farm machinery, the fusion algorithm of BP neural network and AdaBoost was combined to recognize the operation state of the farm machinery. The experimental results showed that the accuracy of the proposed algorithm model in the prediction of the operation state of agricultural machinery was as high as 97.3%, indicating that the recognition method based on the multidimensional features of agricultural machinery can accurately recognize the operation state of agricultural machinery.

Aiming to address the issues of missing accuracy evaluation for tractor dynamic models and the lack of systematic, standardized assessment methods, an accuracy evaluation approach was proposed based on time-response error and grey relational analysis, grounded in the verification, validation, and accreditation (VV&A) theory of model simulation systems. A hierarchical validation index system comprising phase error, amplitude error, and shape error was constructed, and the grey relational degree method was applied for segmental evaluation of dynamic response curves. Four sets of idealized test cases with defined error components were designed and analyzed by using both the proposed method and traditional numerical error analysis techniques. The results demonstrated that the proposed method effectively decoupled and accurately identified multiple error characteristics in each test case, precisely reflecting the sources and distribution of errors, while its comprehensive evaluation accuracy outperformed traditional methods. Furthermore, taking a self-developed longitudinal dynamic model of a power-shift tractor as an application case, real vehicle data were collected under five road conditions: tilled field, untilled field, stubble field, concrete surface, and mixed road surface. The proposed method was used to compare and analyze simulation results against real vehicle data under various working conditions. The results indicated that the mean comprehensive accuracy score of the model was 90.95. Under high-speed and complex road conditions, scores for various error characteristics decreased significantly, with comprehensive results in some extreme conditions falling below 88, reflecting certain limitations of the model under different working conditions, which aligned with objective reality and verified the effectiveness and comprehensiveness of the proposed evaluation method.

Hilly and mountainous regions are characterized by complex terrain and agricultural machinery with high centers of gravity, which often leads to instability and rollover risks during rail transport operations. To address this challenge, a hydraulic automatic leveling anti-instability system for a mountain rail transport platform was proposed. The system integrated an ADXL345 triaxial accelerometer for real-time attitude monitoring and employed a particle swarm optimization fuzzy PID (PSO Fuzzy-PID) controller, using the platform's Z-axis tilt angle as the feedback variable. A simulation model was established in Simulink to compare the performance of conventional PID, fuzzy PID, and PSO Fuzzy-PID controllers. Simulation results demonstrated that the PSO Fuzzy-PID controller achieved a rapid response time of 0.181 s with zero overshoot, and outperformed conventional PID and fuzzy PID controllers in terms of adjustment time, stability, and overall dynamic response. Static experimental tests were conducted under track inclinations of 4°, 7°, and 10°, and the PSO Fuzzy-PID controller yielded an average leveling error standard deviation of only 0.068°, with maximum error constrained within 0.45°. The average leveling time was reduced by approximately 6.3% and 15.6% compared with that of fuzzy PID and conventional PID, respectively. Dynamic experiments further revealed that the system maintained a response delay below 0.4 s, with platform attitude fluctuations confined to-0.079° to 0.497°, thereby meeting the precise leveling requirement within±10°. These results confirmed the effectiveness and reliability of the proposed PSO Fuzzy-PID based hydraulic leveling system. The system significantly enhanced the operational stability and safety of agricultural machinery transport on hilly rail platforms, providing valuable technical support for the intelligent development and practical application of agricultural transport equipment in mountainous regions.

Aiming at the problem that the artificial setting of comprehensive coefficient is difficult to adapt to the complex and changeable working environment of soil conditions in the process of tractor electro-hydraulic suspension tillage depth control, a tillage depth control method based on optimal draft-position comprehensive coefficient was proposed. Firstly, the test platform of tractor electro-hydraulic suspension draft-position integrated tillage depth control and loading system was built and the system was modeled by system identification method. Then aiming at the problem of uncertain parameters of the equivalent transfer function of the system, a global sliding mode control algorithm was designed, and an arc-tangent function was introduced to reduce the chattering of the system. The full factor sample test was designed, and the comprehensive operation evaluation index was established by compromise programming method. The expected draft-position comprehensive coefficient under different working conditions was obtained, and the optimal draft-position comprehensive model was established based on the least squares support vector machine optimized by particle swarm optimization algorithm (PSO-LSSVM). Finally, the effect of the control system was verified by using the built control system test platform. The results showed that the proposed algorithm achieved a rise time of 1.06 s without noticeable chattering. Compared with the fixed weight-depth control method with a comprehensive coefficient of 0.5, the proposed method reduced the depth deviation rate by 19.5% and 13.9% at target depths of 140 mm and 220 mm, respectively, while improving depth uniformity by 16.6% and 11.2%. This method can realize the adaptive adjustment of the comprehensive coefficient according to the soil specific resistance and tillage depth, and was more adaptable to the complex field operation environment, which provided a reference for the research of tillage depth control method.

Accurate detection of the drive wheel slip in tractor ploughing operations is fundamental to achieving slip control, which helps improve the traction efficiency and ploughing quality of a tractor. To address the issue of ensuring the accuracy and real-time performance of slip rate detection under heavy load ploughing conditions, a slip rate detection method based on multisensor data fusion was proposed. A real-time data collection platform for tractor ploughing operations was established. Field experiments were conducted to obtain tillage speed, forward acceleration, tractor pitch angle, roll angle, traction resistance, tillage depth and corresponding slip rate under different operating conditions. A datasets was constructed and subjected to statistical analysis. Four machine learning algorithms—random forest (RF), support vector regression (SVR), radial basis function neural network (RBFNN), and artificial neural network (ANN)—were employed to construct the slip rate detection models. The hyperparameters of these models were optimized by using grid search and particle swarm optimization (PSO). The performance of the models was evaluated via three metrics: the coefficient of determination (R²), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The results indicated that, except for the RF model, which exhibited overfitting, the other three models achieved satisfactory slip rate predictions (R²>0.9). Among them, the ANN model optimized with particle swarm optimization (PSO) for initial threshold weight optimization outperformed both the SVR model with an R² of 0.918, an RMSE of 2.6% and a MAPE of 8.9%, and the RBFNN model with an R² of 0.903, an RMSE of 3.0%, and a MAPE of 8.8% on the test dataset, achieving an R² of 0.937, an RMSE of 1.0%, and a MAPE of 7.6%. This method enabled reliable and accurate detection of tractor drive wheel slip rate, offering an approach for precise online detection and control of slip rate.

Due to the complex working conditions of tractors, which are influenced by the working environment, soil conditions, and traction loads, random time-varying disturbances during power shifting process could easily cause shifting jerk and even damage the transmission. Therefore, an optimal control method for tractor power shifting process considering time-varying disturbance observer was proposed. Firstly, the dynamic characteristics of the tractor shift process were analyzed to establish the shifting process dynamics model. The shift time, sliding friction work, and shock degree were taken as the shifting quality indicators, and a quadratic index function of the shifting control system was constructed. Then a high-order disturbance observer (HDO) was introduced to estimate the time-varying disturbances and their derivatives during the shifting process, and the linear quadratic regulator (LQR) was derived and solved for the optimal control law of the shifting process based on the Hamiltonian function. Finally, a hardware-in-the-loop simulation platform for tractors was built based on dSPACE and Matlab/Simulink to verify the shifting process performance under different operating conditions. The results showed that taking the shift from 1LL gear to 1L gear as an example, the shifting times under the transportation and plowing working conditions were 0.72 s and 0.85 s respectively, which were reduced by 22.58% and 38.4% compared with that of LQR. After compensation by HDO, the maximum shifting jerks were 16.11 m/s³ and 8.51 m/s³, respectively, which were 5.29% and 2.04% higher than that of LQR, the shifting jerk difference was not obvious, but the sliding friction work control effect was significant, being only 4.61 kJ and 8.82 kJ, which were reduced by 38.04% and 59.17% compared with that of LQR. The method could effectively observe and suppress the time-varying disturbances during the shifting process, and improve the shifting comfort.

Aiming to address issues with traditional mechanical adjustment methods for no-till precision planters in stubble fields, such as the ground wheel assembly's inability to adapt to terrain changes, leading to unstable residue clearing performance and increased wheel slip, a hydraulically controlled terrain-following depth-limiting ground wheel assembly was designed. Its hydraulic system enabled rapid adjustment of wheel height while maintaining a constant position, ensuring stable furrow opener depth. The cushioning spring reduced ground-induced impacts transmitted to the planter, enhancing the stability of the residue clearing device. Through theoretical analysis, the main structural parameters of the terrain-following depth-limiting ground wheel assembly were determined. Simulation tests identified the optimal stiffness of the cushioning spring as 1.2×10⁵N/m. Comparative field trials demonstrated that the hydraulically controlled terrain-following depth-limiting ground wheel assembly operated stably with lower slip rates. When the furrow opener depth qualification criterion was set at (50±10) mm, the qualification rates for opener soil penetration depth were 62% and 82% for the mechanically-adjusted ground wheelassemblyandhydraulic-controlledterrain-followingdepth-controlwheelassembly, respectively. The hydraulic assembly exhibited a 20 percentage point higher qualification rate than the mechanical assembly. Furthermore, the hydraulic terrain-following wheel demonstrated significantly lower slip rates. While both adjustment methods showed increasing slip rates with the rise of operating speeds, the hydraulic terrain-following wheel exhibited a smaller rate of increase.

In response to the fact that a large amount of straw and sticky soil of oilseed rape direct seeding in the middle and lower reaches of the Yangtze River, from the perspective of combining oilseed rape seedbed preparation and deep fertilizer application operation, a preparation method of seedbed with the whole width of the compartment shallowly rotated and the seeding belt rotated and cut for deep loosening and deep application of fertilizer was proposed, and a rotary-cutting combined deep fertilizer application device was designed. Based on the characteristics of rice root distribution in the previous crop in the tillage layer, combined with the root growth characteristics of rapeseed plants, the fertilizer depth of 100~150 mm and the deep loosening depth of 200 mm were determined; based on the kinetic analysis of the stubble cutting and breaking disc to cut off the stubble force, and the notch slip-cutting curves were qualitatively analyzed, the radius of the disc, the angle of the slip-cutting and the number of the notches were determined; based on the plowing depth, key parameters of the deep loosening and breaking shovels were determined; and the key parameters of the rotary-cutting and breaking shovel were determined through the single disturbed area, and the row spacing of rotary-cutting combined deep fertilizer application device was obtained. Using discrete element software to simulate the operational performance of a rotary-cutting combined deep fertilizer application device, the results showed that when the fertilizer depth was 150 mm, the seed bed belt was reasonably constructed, and the straw was evenly mixed and buried. The fertilizer depth was 156.60 mm, with a depth stability coefficient of 87.46%, and a uniformity coefficient of 13.18%. The field performance test showed that when the fertilizer depth was 150 mm, the stubble burying rate was 86.65%; the plowing depth of the seed bed belt and non-seed bed belt was 191.17 mm and 112.50 mm, respectively; the stability coefficient of plowing depth was 90.70% and 91.18%, respectively; the actual fertilizer depth was 149.44 mm, and the qualified rate of fertilizer depth was 85.80%. The field sowing test showed that when the fertilizer depth was 150 mm, the actual fertilizer depth was 151.56 mm, the qualified rate of fertilizer depth was 85.83%, the coefficient of variation of fertilizer uniformity was 14.33%, and the seedling emergence rate under the treatment of rotary-cutting combined deep fertilizer device was 88.97%, which was higher than that of rotary-tillage mixed fertilizer and rotary-tillage without fertilizer, and the length of the main root in the flowering stage was increased by 22.24% and 38.26%, the diameter of the main root neck was increased by 0.60% and 3.57%, and rapeseed yield was increased by 14.02% and 37.50%, respectively. The results showed that the rotary-cutting combined deep fertilization device provided a suitable seedbed strip and fertilization effect for the growth of oilseed rape, which could provide a reference for the preparation of seedbed and deep fertilization for mechanized direct seeding of oilseed rape.