Search Results (440)

Prolonged exposure to high sound pressure levels (SPLs) in agricultural tractors, especially when the power take-off (PTO) is engaged, poses serious health risks to operators. While machine learning has been applied in agriculture, little progress has been made in predicting SPLs in agricultural tractors or detecting patterns that may indicate early mechanical issues. The main goal of this study was to spatially predict SPLs and identify observations whose SPLs departed from the model-expected level for normally operating tractors under different stationary pre-check conditions, as a preliminary basis for future condition-monitoring applications. SPLs were measured at the operator’s seat and at distances up to 15 m in three tractors at PTO speeds of 540, 750, and 1000 rpm. Statistical tests and machine learning models were used to model spatial noise distribution. The RF model performed best (R² = 0.93, RMSE = 1.65 dB(A)), with spatial position being the key predictor of SPL. In this sample, open-station tractors exceeded 100 dB(A) at and near the operator’s position under the loudest tested conditions, far above the 85 dB(A) screening criterion, whereas no statistically significant effect of PTO speed was detected under the tested conditions. Because the reported values are short-duration source measurements rather than full-shift personal exposures, they are interpreted as screening indicators of potentially hazardous conditions rather than as a formal daily noise-dose assessment. In addition, an out-of-range analysis identified observations exceeding the 99th percentile of positive residuals as atypical, indicating SPLs substantially higher than expected for a normally operating tractor. The proposed approach demonstrates a novel way to identify screening distances and conditions, providing a practical tool to protect workers’ health, monitor equipment, and inform maintenance strategies. Full article

Accurate operation-specific assessment of fuel consumption is essential for energy planning, machinery management, and direct emission accounting in mechanized agriculture. However, generalized reference-based fuel consumption values may not adequately represent variations in machinery load, operating conditions, and field characteristics. This study developed and evaluated a telemetry-based framework for quantifying fuel consumption and energy performance of tractor–machine combinations under real agricultural production conditions and for supporting the future development of adaptive regional fuel-consumption standards. Field experiments were conducted using DFM electronic fuel-flow meters integrated with Controller Area Network (CAN) communication and Bluetooth Low Energy (BLE) wireless data transmission. Continuous telemetry measurements were used to evaluate major mechanized agricultural operations and to compare conventional and minimum-tillage production systems under actual field conditions. For the monitored production cycles, cumulative measured fuel consumption decreased from 108.82 kg/ha under conventional tillage to 93.40 kg/ha under minimum tillage, corresponding to a 14.2% reduction. Useful specific energy consumption decreased from 576.55 to 543.50 kWh/ha (5.7%), while total specific energy consumption decreased from 1300.36 to 1078.30 kWh/ha (17.1%). Based directly on measured cumulative diesel consumption and an emission factor of 3.14 kg CO₂/kg fuel, direct fuel-related CO₂ emissions decreased from approximately 341.7 to 293.3 kg CO₂/ha. A second-order polynomial model showed a close relationship between operating speed and specific energy consumption within the investigated experimental range (R² = 0.985). The results demonstrate that continuous telemetry provides an objective basis for operation-specific fuel assessment, energy-performance analysis, and comparison of resource-saving production systems. The proposed framework can support the future development and refinement of regionally adaptive fuel-consumption standards; however, additional validation under different soil, machinery, and operating conditions is required before broader application. Full article

Aerodynamic interference between multiple propulsion systems can strongly affect the performance of unconventional Unmanned Aerial Vehicles (UAVs). This study investigates the interaction between a front tractor propeller, an upper lifting rotor, and the airframe of a dual-propulsion gyrodyne UAV using unsteady Reynolds-averaged Navier–Stokes simulations with moving meshes. Two representative operating conditions are considered: powered forward flight, with the upper rotor operating in autorotation, and hovering, with the upper rotor providing lift. In forward flight, the propeller slipstream modifies the local incidence and dynamic pressure over the wing, producing an asymmetric spanwise lift distribution. The addition of the autorotating upper rotor further redistributes the wing loading through its interaction with the propeller slipstream and increases the total drag coefficient by approximately 3.7% relative to the propeller-only configuration. In hovering conditions, the rotor downwash strongly modifies the aerodynamic loading of the wing, with the interaction becoming negligible for rotor–wing separations, $h/R\ge 0.6$, for the investigated configuration. Overall, the results show that the aerodynamic loads of the complete configuration cannot be directly inferred from those of the individual propulsion systems because of the nonlinear interaction between their wakes. The study identifies the main flow mechanisms responsible for these interference effects and provides quantitative information relevant to the aerodynamic analysis of dual-propulsion gyrodyne UAVs. Full article

Self-cleaning is an important characteristic of agricultural tires because it can affect tire–soil interaction and traction under wet soil conditions. However, studies quantifying the effects of tire tread design and inflation pressure on this process remain limited. The objective of this study was to evaluate the distance traveled under field capacity soil conditions and the self-cleaning performance of four radial agricultural tires subjected to three inflation pressures. The experiments were conducted at the Federal University of Mato Grosso under controlled soil moisture conditions. A New Holland T7.205 tractor equipped with a four-disc plow was used to apply load and promote tire saturation with wet soil. The evaluated variables were the distance traveled until tread saturation and the number of tire revolutions required for soil removal. The lowest inflation pressure (1.2 bar) resulted in the greatest distance traveled before tread saturation, whereas higher inflation pressures generally reduced the distance traveled before saturation. Tire P exhibited the best results in terms of the greatest distance traveled before soil saturation in the tread at inflation pressures of 1.2, 1.6 and 2.0 bar. At an inflation pressure of 1.2 bar, tire M required the lowest number of tire revolutions for soil removal from the tread, averaging 9 revolutions. At 2.0 bar, tires P and F required fewer tire revolutions for soil removal than tires G and M, averaging 18 and 19 revolutions, respectively. Full article

Peanut harvesting remains labor-intensive and costly due to the limited availability of locally developed mechanized harvesting technologies. Thus, this study presents the design, optimization, and evaluation of a four-wheel tractor peanut digger to advance harvesting. Structural integrity was evaluated using Finite Element Analysis (FEA) in ANSYS under a draft load of 2008.2 N, yielding a maximum von Mises stress of 33.012 MPa, a peak strain of 0.00015, a maximum deformation of 1.72 mm, and a factor of safety of 5.74, confirming adequate structural reliability. Field performance was optimized using a Box–Behnken Design (BBD) with tractor speed, digger blade lift angle, and clearance angle as factors, while material capacity and digging efficiency served as response variables. Regression analysis showed that the main effects significantly influenced both responses, whereas interaction effects were insignificant. Response Surface Methodology (RSM) optimization identified optimal operating conditions of a 14.95° lift angle, 15.70° clearance angle, and 1.74 km·h⁻¹ forward speed, achieving a predicted material capacity of 0.56 t·h⁻¹ and digging efficiency of 96.69%. Validation tests improved these values to 0.59 t·h⁻¹ and 97.42%, demonstrating that integrating numerical methods provides an effective approach for developing adoptable and structurally reliable peanut harvesting machinery. Full article

Rice–wheat combine harvesters face coupled traction and material-processing loads whose variability complicates energy saving without reducing throughput or harvest quality. This review connects five perspectives: intelligent sensing, electrified actuation, hybrid powertrain architecture, field-condition prediction, and energy management. It examines interfaces under common quality and battery energy boundaries. Forward crop sensing, feed-rate monitoring, and component-load observation provide complementary information, while independent electric drives make drum, fan, and sieve settings more controllable. Series, parallel, and power-split architectures offer different compromises between controllability, conversion losses, packaging, and sustained-load performance. Direct evidence remains limited: one field prototype reported 19.5% lower fuel consumption than a conventional harvester, a study-specific result that does not establish a general saving range. Evidence from tractors and other vehicles supports transferable methods but cannot replace rice–wheat validation. A comparison matrix examines rule-based, instantaneous, optimal, predictive, and learning-based energy management. The framework couples fuel use and clean-grain throughput with loss, breakage, impurity, and state-of-charge constraints, and outlines life-cycle cost and carbon accounting. It is methodological rather than a new numerical assessment. The priorities are synchronized field duty cycles, uncertainty-aware coordination, thermal and fault validation, and comparisons across sites and seasons. The synthesis supports powertrain design and controller development for more energy-efficient and intelligent harvesting. Full article

Efficient tractor coverage planning requires suitable swath orientations and kinematically feasible connections. In two-pass diagonal harrowing, sequential boustrophedon planning treats the two swath families independently and may introduce avoidable connection travel. This study proposes a Field-Geometry-Aware Bidirectional Coverage and Connection Path Co-Optimization algorithm (FG-BCO) for a single tractor operating in convex, obstacle-free polygonal fields. FG-BCO clips two parallel swath families to the field boundary, constructs feasible cross-pass links between oriented endpoints, assembles alternating route paths, and applies subset dynamic programming to optimize their visiting order and traversal orientation. The resulting route is converted into a forward-only trajectory and checked against boundary-containment and minimum-turning-radius constraints. FG-BCO was compared with Sequential Boustrophedon Coverage Path Planning (SB-CPP) in rectangular, regular pentagonal, regular hexagonal, and irregular fields using 648 matched angle pairs per field. When the minimum path length of each algorithm was selected independently from the tested angle pairs, FG-BCO achieved reductions of 18.65%, 9.65%, 15.20%, and 17.11% in the four fields, respectively. However, FG-BCO produced shorter routes in only 57.87%, 41.82%, 40.43%, and 40.59% of the matched cases. The corresponding mean paired saving rates were 0.646%, −0.484%, −0.144%, and −0.833%, with medians of 0.939%, −1.060%, −0.923%, and −1.094%. These results distinguish the potential reduction obtainable through angle selection from performance under an arbitrary prescribed angle pair. FG-BCO is advantageous when the two swath families produce favorable endpoint proximity, heading compatibility, and turning space, but its improvement is not universal. Preliminary evaluation of the working-angle pair in relation to the target field geometry is therefore recommended before application. Full article

Autonomous agricultural vehicles (AAVs) are important technologies for advancing intelligent and precision agriculture. This paper reviews the latest research progress in multi-source fusion positioning and robust state estimation technologies for autonomous agricultural vehicles in complex agricultural environments, with a focus on analyzing the typical application requirements and technical characteristics in open farmland, dense orchards, enclosed greenhouses, and GNSS-degraded and GNSS-denied scenarios. This review summarizes the key environmental factors that affect the autonomous navigation performance of agricultural vehicles, including GNSS signal blockage and multipath effects, mechanical vibration, wheel and track slip caused by complex terrain, and interference from dynamic vegetation. Subsequently, classification and comparison were conducted based on multi-sensor hardware configurations and data fusion architectures. On this basis, robust state estimation methods for complex agricultural environments were further discussed, including Adaptive Kalman filtering, Factor Graph Optimization (FGO), learning-assisted multi-sensor fusion, and motion-constrained slip compensation methods. This review analyzes the practical applications of multi-source fusion positioning technologies in autonomous tractor path tracking, orchard robot navigation, greenhouse inspection, and intelligent agricultural operation systems, and analyzes the current main bottlenecks faced by these technologies. This review discusses future development directions and provides technical insights into the development of highly reliable autonomous agricultural equipment. Full article

Achieving stable seed placement under No-Till conditions remains challenging because the seeding units operate directly in undisturbed soil with spatially variable properties and surface residues. This study evaluated seed-placement quality and compliance with predefined seeder settings during field-scale No-Till sunflower sowing under three operating regimes. A CASE IH Quadtrac 620 tractor coupled with an AMAZONE Primera DMC 9001-2C pneumatic seeder was evaluated in three separate field areas. Each area was associated with one nominal operating regime: 8, 10, or 12 km h⁻¹, designated as S8, S10, and S12, respectively. A total of 135 sampling positions were evaluated for displayed operating speed, sowing depth, seed spacing, missing placements, double placements, and soil moisture. The mean sowing depths recorded for the S8, S10, and S12 zone–regime combinations were 5.97, 9.42, and 6.02 cm, respectively. The S8 field area showed the highest compliance with the target depth, with 92.1% of the measured seeds positioned within 4–6 cm. The proportions of seed-spacing observations within the target range of 20–25 cm were 0% for S8 and S10 and 17.8% for S12. Missing and double placements were numerically more frequent in the areas associated with the higher nominal speeds. However, because each operating regime was evaluated in a separate field area, the observed differences cannot be attributed separately to operating speed, soil moisture, or spatial field variability. The findings are therefore limited to the sampled field areas and provide a descriptive assessment of seeder performance under the specific operating conditions. Full article

Conventional tractor hitch systems employing mechanically controlled multi-way valves suffer from low control precision and sluggish dynamic response, which render them inadequate for field operations characterized by highly variable soil resistance. To address these limitations, this paper proposes an electro-hydraulic hitch control system based on digital hydraulic valves for high-horsepower tractors. The proposed system integrates a load-sensing pump with pressure-compensated proportional valves to achieve supply-demand flow matching for the hitch cylinder, improving the smoothness of implement attitude regulation. Furthermore, an adaptive fuzzy Proportional-Integral-Derivative (PID) control strategy is developed for simultaneous tillage depth and traction force control, enhancing the accuracy of both controlled variables. A tractor simulation model and an experimental test rig are established to evaluate the hitch control performance under various operating modes. Experimental results demonstrate that the proposed digital-valve-based hitch system exhibits favorable control performance. Compared with conventional PID control, the proposed approach improves the depth stability coefficient and the load stability coefficient by 7.5% and 7.4%, respectively, under depth and traction control modes. Under force-position combined control, the depth and load stability coefficients reach 92.1% and 89.1%, respectively, confirming that the proposed method maintains both tillage depth consistency and load regulation effectiveness. Full article

The use of engine oil additives is a widespread approach to improve the performance of internal combustion engines. Among them, anamegators are applied in order to optimize tribological processes in the lubrication system and increase the energy efficiency of the engine. This study examines the impact of the Gold Ozirol MP-8 anamegator on the main operational and environmental performance of the diesel engine of the Kubota GL-23 tractor under real operating conditions. Changes in specific fuel consumption, effective power, noise levels and smoke of exhaust gases within a busy operating cycle are monitored. The analysis of the results obtained shows a trend towards improving energy efficiency and reducing harmful emissions due to the application of the anamegator. The data obtained make it possible to evaluate the applicability of the product as a means of optimizing the performance and environmental characteristics of tractor engines in the conditions of intensive agricultural operation. Full article

The aerodynamic characterization of propeller-driven UAVs is often constrained by the unfeasibility of testing the complete airframe–propeller assembly in a wind tunnel, since geometric scaling prevents simultaneous similarity of both the airframe and the propeller. To address this limitation, this work presents an integrated experimental–numerical methodology that reconstructs the full-scale free-air aerodynamic behaviour of a tractor-propeller UAV combining wind-tunnel measurements of the scaled airframe (without the propeller) and the full-scale propeller. Computational fluid dynamics (CFD) is not used to predict the full-scale UAV directly; it is used to predict differences between matched configurations, while the absolute aerodynamic level remains anchored to experiments. Dedicated CFD simulations are carried out to isolate three distinct physical contributions: scale effects, wind-tunnel blockage, and propeller installation effects. In the developed methodology, numerical simulations complement the experimental data to obtain corrected full-scale aerodynamic coefficients and propulsive maps together with a longitudinal force-equilibrium model used to determine the longitudinal force-equilibrium operating point. The reconstruction shows that scale and wind-tunnel blockage effects primarily alter the airframe aerodynamic characteristics, with a minor influence on equilibrium incidence, while propeller installation produces a substantial thrust augmentation due to airframe-induced inflow modification. Accounting for these effects leads to an overprediction of the propeller rotational speed by approximately $23\%$ when installation effects are neglected, demonstrating that the installed performance cannot be obtained by a linear superposition of isolated airframe and isolated propeller data. Full article

The application and performance of Nonlinear Model Predictive Control (NMPC) are critically limited by the computational efficiency of solving nonlinear combinatorial optimization problems. To address this challenge, this study proposes an efficient optimization strategy that employs a neural network to solve the constrained L-1 norm minimization problem within the NMPC framework, thereby enhancing motion control performance. Inspired by the flexible representational capacity and powerful optimization capabilities of neural networks, we explicitly encode the NMPC objective function into a network architecture. The optimal control solution is then obtained efficiently through network training. We validate the proposed strategy in a tractor path-tracking control task, detailing the processes of network construction and optimization. Benefiting from the inherent parallelism and computational efficiency of neural networks, the resulting controller demonstrates excellent real-time performance. Specifically, with prediction horizons set to 5, 10, and 20 steps, the solution times are reduced to less than 0.12, 0.28, and 0.84 s, respectively, under typical operating constraints. Full article

Autonomous agricultural robots require accurate trajectory tracking to perform precision field operations such as seeding, fertilization, and pruning. Classical kinematic models fail to capture the nonlinear dynamics inherent to real field conditions, limiting the performance of model-based control strategies. This work proposes an optimal hybrid methodology integrating Takagi–Sugeno (T–S) fuzzy model identification and Pure Pursuit (PP) control within a Particle Swarm Optimization (PSO) framework for a simulated pruning tractor. Data-driven T–S fuzzy models for incremental displacements ${\mathcal{M}}_{\Delta x}$ and ${\mathcal{M}}_{\Delta y}$ are identified using Fuzzy C-Means and parameterized via PSO. These fuzzy models are embedded in a PP feedback control scheme with discrete-time PI velocity and PD steering controllers, whose four gains are tuned by a second PSO instance. The fuzzy models achieve identification Root-Mean-Square Errors (RMSEs) of 10.598 × 10⁻³ m and 8.125 × 10⁻³ m. Integrated into the control loop, the system yields a lateral RMSE of 6.6 × 10⁻³ m on the training path and generalizes effectively across twelve complex agricultural coverage trajectories, maintaining a lateral RMSE below 12 × 10⁻³ m and heading RMSE under 1 degree. This interpretable, fuzzy rule-based approach provides an accurate and replicable simulation baseline for future experimental implementation on physical platforms. Full article

This paper investigates the system modeling and trajectory tracking control problem of an off-axis tractor-trailer wheeled mobile system and proposes a control strategy based on an integral sliding surface and a super-twisting algorithm. First, the motion relationship between the tractor and trailer is derived based on their geometric configuration, and the kinematic and dynamic models of the off-axis tractor-trailer wheeled mobile system with a passive steering angle are established. Then, a composite controller is designed by integrating the integral sliding surface with the super-twisting algorithm to achieve accurate tracking of the desired trajectory. During the controller design process, the desired trajectory is reconstructed as a curvature-consistent dynamic tracking target. Under this dynamic tracking target, the curvature deviation depends only on the yaw-rate error and is decoupled from longitudinal velocity disturbances, thereby improving trajectory tracking accuracy. Finally, numerical simulations are conducted to validate the effectiveness and generality of the proposed control strategy under three typical reference trajectories, including cycloidal, S-shaped, and circular trajectories. Comparisons with PID and feedback linearization control methods are also performed. The simulation results demonstrate that the off-axis tractor-trailer wheeled mobile system can closely follow the desired trajectories and exhibits satisfactory robustness. Full article