Agricultural soil fertility is a key determinant of crop productivity and long-term sustainability. However, intensive farming practices often require repeated passes of heavy machinery, which can lead to soil compaction. This study examines the interplay between tractor traffic, tire inflation pressure, and their effects on soil physical properties and fertility indicators. Tire pressure management emerges as a crucial mitigation strategy: high inflation pressures concentrate the load and exacerbate subsoil compaction, whereas reduced pressures (within safe limits) enlarge the tire–soil contact area, distributing the vehicle’s weight more evenly. This in turn improves traction, lowers ground pressure, and reduces energy losses. As a result, both the depth and severity of soil compaction are reduced. Further advances may be achieved through innovative tires manufactured with eco-sustainable materials and tread patterns specifically designed to enhance traction and minimize slippage-related energy loss. In this context, CREA conducted comparative field tests on two tractor tire models from the same manufacturer: a conventional design and an evolved version featuring an innovative tread and larger footprint. The trials assessed the impact of each tire on soil compaction, traction performance, and energy efficiency. Tests were performed on a silty-clay agricultural soil naturally settled for a year, using a dynamometric vehicle to apply different controlled traction force levels, combined with two inflation pressure settings. To highlight performance differences between the two models, the tractor was rear-ballasted, and the study focused on the rear axle, which carried most of the traction stress. Results indicated that, under the specific test conditions, at high inflation pressure both tires performed similarly (with the innovative model slightly reducing fuel use and the conventional yielding marginally higher maximum tractive force), whereas at low pressure the innovative tire clearly outperformed the traditional model in traction efficiency and caused less soil compaction. The extent of the benefits associated with using the innovative tire model across various soil conditions, moisture levels, and in the absence of rear ballasting will be evaluated in further tests based on traction force control using the proposed testing system.

This study develops and evaluates a semi-active seat suspension system for tractors using magnetorheological damper (MRD) and fuzzy logic-based control strategies. A five-degree-of-freedom (5-DOF) half-car tractor model is constructed, integrating human-seat dynamics, cab vibration, chassis motion, tire flexibility, and MRD nonlinear hysteresis represented by the Bouc-Wen model. To address uncertainties caused by road roughness, vehicle speed, and human-seat mass variation, a Type-1 fuzzy logic controller (T1FLC) and an interval Type-2 fuzzy logic controller (IT2FLC) are designed. The stability of both controllers is analyzed through phase-plane trajectories. Simulations are performed under random C-F class road excitations, bump inputs at different speeds, and human-seat massranging from 50 to 150kg. Results show that semi-active control substantially improves ride comfort and reduces suspension dynamic deflection compared with passive suspension. Under random road excitations, the IT2FLC reduces the root mean square (RMS) vertical acceleration by approximately 60% and dynamic deflection by over 50%, effectively mitigating suspension bottoming-out. Under bump excitations, improvements reach 61.27% and 55.94%, respectively. The IT2FLC consistently demonstrates stronger robustness than the T1FLC, especially under highly uncertain or severe road conditions. These findings provide theoretical and engineering support for intelligent vibration-control strategies in agricultural tractor seat suspension systems.

ABSTRACT Background Noise from agricultural tractors is a critical occupational health hazard, often leading to Noise Induced Hearing Loss (NIHL). This study aims to assess the risk of NIHL among Indian Agricultural Tractor Drivers (ATD) and enrich the understanding of such risks using unsupervised data mining techniques. Materials and Methods A cross-sectional study was conducted on audiometric profile of Indian ATD with driving experience ranging from 5 to 43 years, and audiometry testing was done with 0.125 kHz to 8 kHz of frequencies. Participants were selected from Bhopal, Madhya Pradesh, India. The k-means clustering techniques, an unsupervised learning method, was applied to classify the audiometric data. Z-scores were used to evaluate cluster homogeneity and separation, while ANOVA was performed to determine the significance of various factors, including age, experience, and weight, on hearing impairment. Results The mean hearing threshold levels were the lowest for drivers with less than 10 years of experience and highest for those with over 10 years of experience. The mean age of the drivers at the time of testing was 39.5 (±10.2) years. The audiometric data did not follow a normal distribution, necessitating the use of k-means clustering for analysis of both the ears. All audiometric frequencies showed statistically significant between-cluster differences, though with notably lower F-values compared to the right ear, ranging from 4.946 at 8 kHz (p = .011) to 19.461 at 1 kHz (p < .001). While age, experience, and weight were not significant for some parameters, other factors showed significant impacts on hearing impairment. Notably, the effect of different frequencies on both right and left ears was significant, highlighting the potential risks associated with prolonged tractor operation. Conclusion The study demonstrates the feasibility of using k-means clustering to analyze audiometric data effectively. This method could play a vital role in hearing conservation programs for individuals exposed to occupational noise at agricultural workplaces. Raising legislative awareness and implementing customized safety programs to promote tractors with reduced noise levels are recommended.

The fuel quality is being regarded in this article as the crucial impact factor, which determines the following technical properties of the mobile agricultural vehicles as the reliability and the overall efficiency. Going on in their application alongside with the traditional fossil types of fuels, the bio-ones, being derived from the renewable sources, are being regarded as more and more popular. However, the biofuels are exhibiting the distinct chemical compositions, regarding the main properties and characteristics in the comparison to the petroleum-based ones, making it necessary to continue the further development of the modern storage, transportation and processing materials. The scientific article provides the solid proof basis for the application of the highly porous polymeric materials of the spherical forms for the process of the purification of various fuel types. These materials were synthesized via copolymerization of multiple monomers, including resorcinol, formaldehyde, styrene, divinylbenzene, and carbamide. The polymeric materials demonstrated remarkable inertness towards biofuel, with no discernible changes observed in either the filters or the fuel samples after prolonged contact. The tensile strength of PGS polymer samples ranged from 15.5 to 21 MPa, while their compressive strength ranged from 2.5 to 6 MPa. Fine fuel filtration achieved with these polymer materials had an absolute precision of 10 microns, with a purification efficiency ranging from 92% to 95%. The proposed model for the design of the self-cleaning filter, being produced from the PGS polymers allows in its` turn the simultaneous purification of fuel from any mechanical impurities and provides the step-by-step filter regeneration, so enabling the prolonged service life without the maintenance.

The efficiency of Unmanned Aerial Vehicles (UAVs) in precision agriculture significantly depends on their design and functional adaptability. This paper presents a focused study on the lateral-directional dynamics of the HyVprop UAV, featuring a distinctive V-tail inverted configuration, tailored for advanced crop imaging tasks. Building prior wavelet transform-based methodologies that addressed sensor quality issues, this research adapts these techniques to the unique aerodynamic characteristics and challenges posed by the HyVprop’s design. The research uses multi-level wavelet decomposition to reduce sensor noise and synchronize lateral-directional signals accurately. This process is essential for precise system identification, which plays a key role in the UAV’s navigation and operational effectiveness within agricultural settings. Enhanced by the Output Error Method, which is refined to improve signal correlation specifically for the V-tail configuration, this approach is tested with simulated sensor data. The findings demonstrate significant improvements in signal quality and correlation coefficients, establishing a comprehensive framework for UAV system identification that enhances reliability and precision in crop monitoring. The paper not only confirms the methodology’s effectiveness but also highlights the specific advantages of adapting system identification techniques to unconventional UAV designs like the V-tail inverted HyVprop.

Time-varying disturbances arising from complex terrain and the lack of rigorous constraint-handling mechanisms significantly degrade the path-tracking performance of unmanned agricultural tractors (UATs). To address these issues, this paper proposes a generalized extended-state-observer-based prescribed-performance sliding-mode (GESO-PPSM) control method. First, a homeomorphic mapping-based prescribed performance function is employed to impose hard performance constraints, guaranteeing that the preview error remains within predefined bounds throughout the entire process. Second, a generalized super-twisting extended-state observer (GESO) is developed to compensate for lumped uncertainties, enabling finite-time and high-accuracy disturbance estimation compared with that of conventional observers. Furthermore, a switching sliding mode surface is designed to achieve fast convergence far from equilibrium while effectively suppressing overshoot near the origin. Unlike traditional sliding mode control, a continuous path-tracking control law based on a power function is formulated to ensure robustness while avoiding discontinuities. Comparative co-simulations based on a high-fidelity UAT model demonstrate that the proposed control method achieves superior steady-state accuracy, with significant reductions in preview error standard deviations of up to 92.52%, 84.33%, and 80.44% compared to PID, model predictive control (MPC), and GESO-based conventional sliding mode (GESO-SM) control, respectively. These results validate the superiority of the GESO-PPSM method in terms of accuracy, robustness, and strict constraint satisfaction in complex agricultural environments.

ABSTRACT Objectives Agricultural injuries are known to be under-reported in existing surveillance systems. The Occupational Injury and Illness Classification System (OIICS) codes are a standardized classification system developed by the Bureau of Labor Statistics (BLS) which ensures consistency in reporting and analysis of workplace incidents over time across industry sectors. Our study examines OIICS coded injuries obtained via mining emergency response (Pre-Hospital Care Report) records (PCRs) to improve tracking, documentation, and understanding of agricultural injury trends. Methods We analyzed frequencies of OIICS subcodes for Primary Injury Source, Event/Exposure, Nature of Injury, and Body Part classifications for 1583 injuries among agricultural workers in Maine, spanning January 2008 to December 2022. To streamline the dataset and subsequent analysis, subcodes within each category were thematically grouped. We summarized and visualized grouped code frequencies by subject sex, age category, season of injury, and study subperiod. Chi-square tests were used to assess differences in injury patterns by sex and age group. Results Reported injuries increased over time from 420 in 2008–2011 to 631 in 2019–2022. The most frequently reported classifications were: “Tractors/power take off (PTO)s” (Injury Source), “Fall” (Event), “Multiple parts” (Body Part), and “Pain” (Nature of Injury). A marked increase in “Nonclassifiable” Source subcodes and “Fall” Event subcodes was observed in 2019–2022 relative to earlier periods. Significant differences by sex were found for injury Event subcodes: The most frequent source of injuries for females were animals, versus objects and equipment being the most frequent source for males. Nature of Injury also varied significantly by sex. All four OIICS categories (Source, Event, Nature, Body Part) showed significant variation by age group. Older subjects reported more injuries due to falls and overexertion, while younger were more frequently subject to exposure, intentional self-injury, injury in fires, and injuries involving farm vehicles and equipment. Conclusion Injury counts rose across each successive study period. All injury subcodes differed significantly by age category, while injury Event and Body Part codes also varied significantly by sex. This suggests that injury risks are not uniform across demographics, and tailored safety interventions by sex and age group may be more effective in reducing agricultural injuries.

Accurate modelling of agricultural vehicles is essential for optimizing drivetrain performance and energy efficiency, particularly as hybrid systems become more prevalent in sustainable farming. This study presents an experimental validation of a vehicle physical model using the Claas Xerion 3800 tractor. Coast-down tests were conducted to determine the rolling resistance coefficient, while GPS and diagnostic data were used to capture real-world vehicle dynamics and fuel consumption. The rolling resistance coefficient was calculated using two-stage aggregation method of multiple run data, yielding a statistically robust result. Simulation outputs showed close agreement with measured longitudinal responses, including vehicle acceleration, traction force, and fuel usage, with a 2.1% deviation in total fuel consumption. These findings demonstrate that the proposed modelling approach reliably replicates the vehicle’s macroscopic longitudinal dynamics and support its application in drivetrain optimization, hybrid system integration, and energy-efficient vehicle design studies. The validated framework contributes to the development of context-aware simulations capable of reflecting real-world off-road conditions and operational variability.

Abstract Purpose This study compared the agricultural tractor emission inventory systems in Korea, the United States, and Europe to identify discrepancies between inventory reference values (CAPSS, EPA, EEA) and field measurements obtained using a Portable Emission Measurement System (PEMS) under real-world operating conditions. Methods Emission data derived from PEMS-based studies were compiled to summarize emission factors (EFs) and load factors (LFs) across various agricultural tasks. These values were systematically compared with the default parameters used in each national inventory. Additionally, four estimation scenarios were developed—inventory defaults, measured LF, measured EF, and combined measured EF and LF—to calculate and contrast emissions during idling, driving, plowing, and rotary tillage. Results Measured LFs ranged from 0.2 to 0.9, varying significantly by task, even among tractors with similar rated power. Relative to inventory defaults, Korea (CAPSS = 0.48) underestimated LF by approximately 25%, the United States (EPA = 0.59) overestimated by 27%, while Europe (EEA = 0.50) closely matched the measured average. For EF, Europe exhibited a progressive decline in CO and NO x emissions in line with tightening regulations, whereas several Tier 4 tractors in the United States showed higher CO and NO x levels than inventory assumptions. Across scenarios, the inventory-default case consistently underestimated total emissions. The measured LF scenario elevated emissions in high-load tasks (plowing and rotary tillage), while the measured EF scenario tended to overestimate emissions in low-load and underestimate in high-load operations. Conclusions Fixed EF and LF values fail to capture the variability of agricultural tractor emissions under actual working conditions. These findings underscore the need to revise inventory parameters using PEMS-derived field data to improve accuracy and representativeness.

The study analyses trends in the development of agricultural tractor engines in the context of technological and environmental transformation between 2015 and 2024, with forecasts up to 2035. Based on catalog data of over 150 tractor models and technical documentation from major manufacturers, changes in displacement, cylinder number, and specific power were evaluated. The aim of this study was to identify and quantitatively assess the key technological shifts in agricultural tractor engine design between 2015 and 2024, and to forecast their development pathways and potential impact on energy efficiency and sustainability up to 2035. The results indicate a continued transition from conventional downsizing to the rightsizing concept, with a simultaneous increase in average engine power by approximately 25% and a 10% reduction in displacement. Modular engine platforms have become dominant, enabling flexible configuration of four- and six-cylinder units and improving design unification. In the high-power segment, a renaissance of large-displacement engines optimized for low-speed efficiency was observed. Hybridization and electrification of powertrains are expected to increase their share to approximately 15% and 8%, respectively, by 2035, leading to a potential 10–20% reduction in fuel consumption and CO₂ emissions. The implementation of Smart Engine Management systems and advanced thermal control strategies contributes to improving thermal efficiency to approximately 43–45%. The obtained results provide a comprehensive overview of current and future engine development trends and may support decision-making processes related to sustainable and resource-efficient agricultural machinery design.

Accurate path planning was the prerequisite for autonomous navigation of agricultural vehicles. An Unmanned Aerial Vehicle (UAV)-based coverage path planning was developed in this research for automating guidance of agricultural vehicles and reducing the operator maneuver in the creation of navigation maps. High-resolution orthophoto maps of the field were constructed by using low-altitude UAV photogrammetry to obtain spatial information. Travel paths and working paths were automatically generated from anchor points selected by the operator under the image coordinate domain. The navigation path for unmanned agricultural vehicles was generated by Mercator projection-based conversion for the anchor pixel coordinates into latitude and longitude geographic coordinates. A Graphical User Interface (GUI) was developed for path generation, visualization, and performance evaluation, through which the proposed path planning method was implemented for autonomous agricultural vehicle navigation. Calculation accuracy tests demonstrated the mean planar coordinate error was 2.23 cm and the maximum error was 3.37 cm for path planning. Field tests showed that lateral navigation errors remained within ±5.5 cm for the unmanned high-clearance sprayer, which indicated that the developed UAV-based coverage path planning method was feasible and featured high accuracy. It provided an effective solution for achieving fully autonomous agricultural vehicle operations.

Mudguard stamping springback control for agricultural tractors: Collaborative multi-strategy approach and experimental verification

Assessing non-pneumatic tires for agricultural tractors: A study on soil compaction and tractive performance

Fast convergence time is crucial for the path tracking control of autonomous agricultural vehicles (AAVs), enabling them to swiftly adapt to complex environments. In this work, we present a novel generalized proportional integral observer (GPIO)-based faster fixed-time control scheme, enhancing the rapid responsiveness of AAVs while effectively compensating for time-varying disturbances. Then, an auxiliary system is developed to mitigate the adverse effects of actuator saturation that may occur due to rapid response. Subsequently, a Lyapunov-based stability analysis is provided to demonstrate the fixed-time convergence of the AAV path tracking system. Finally, comparative experiments validate the superiority of the proposed control scheme.

The glymphatic system is a clearance pathway that facilitates convective exchange between cerebrospinal fluid (CSF) and interstitial fluid, and is increasingly implicated in the pathophysiology of neurodegenerative and neuroinflammatory disorders. However, translation of glymphatic physiology into intentional, noninvasive biomechanical interventions remains limited. Building on a previously introduced biomechanical framework that proposed axial spinal traction as a method to modulate CSF and glymphatic circulation, practitioner-applied pelvis-stabilized axial spinal traction (PSAST) has been characterized as a controlled prototype, although its reliance on professional administration and structured setup may constrain deployment in population-level or preventive contexts. This report introduces active craniospinal tensioning (ACT) as a translational, participant-driven extension of the established axial traction-glymphatic modulation framework, specifically designed for ambulatory populations. ACT employs a voluntary squat maneuver combined with a standardized overhead anchor system to generate controlled axial tension along the craniospinal axis, while preserving the same dural tensioning and craniospinal coupling principles described in prior axial traction models. By shifting force generation from practitioner-applied loading to participant-regulated loading, ACT presents a scalable approach for exploratory preventive application under supervised conditions. ACT is theoretically framed for individuals exhibiting reduced perivascular water diffusivity, such as those with lower diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) indices. This report provides a practical implementation framework for ACT and supports future hypothesis-driven investigation of biomechanical modulation of CSF and glymphatic circulation using advanced neuroimaging biomarkers.

The traction behavior of lubricant films forms the foundation of dynamic modeling for aeroengine mainshaft ball bearings. Its accuracy directly determines the reliability of predicted dynamic responses and the available design safety margins. Existing traction models produce artificial friction in the zero slip region and exhibit strong sensitivity to ball size effects, which leads to significant deviations from experimental observations. These limitations make them unsuitable for high-fidelity analyses of aeroengine mainshaft bearings. In this study, a self-developed high-speed traction test rig was used to systematically measure the traction–slip responses of three aviation lubricants, including the newly developed 4102 (7 cSt) and the inservice 4050 (5 cSt) and 4010 (3 cSt). The tests covered a wide range of operating conditions, including maximum Hertzian pressures of 1.0 to 1.5 GPa, oil supply temperatures of 25 to 120 °C, entrainment speeds of 25 to 40 m/s, and slide–roll ratios (SRR) of 0 to 0.3. The evolution of lubricant traction characteristics was examined in detail. Based on the experimental data, a four-parameter and three-coefficient traction model was proposed. This model eliminates the non-physical traction outputs at zero slip observed in previous formulations. When embedded into the bearing dynamic simulations, the maximum deviation between the predicted friction torque and the measured values is only 3.79%. On the basis of typical operating conditions of aeroengine bearings, lubricant selection guidelines were established. Under combined high-speed, light-load, and high-temperature conditions, the high-viscosity lubricant 4102 is preferred because it suppresses cage sliding and enhances film stiffness. When the cage slip ratio is below 15% and lubrication is sufficient, the low-viscosity lubricant 4010 is recommended, followed by 4050, in order to reduce frictional heating. This study provides a theoretical basis for high-accuracy dynamic design and lubricant selection for aeroengine ball bearings.

IntroductionTracked agricultural vehicles operating in complex farmland environments are prone to track slip, which degrades path-tracking accuracy and may lead to unstable motion. To address the limitations of conventional geometric tracking algorithms under slip conditions, this study proposes a slip-aware look-ahead point offset path-tracking control method for tracked agricultural machinery.MethodsAn extended Kalman filter (EKF) is developed to fuse RTK–IMU pose measurements with track wheel-speed feedback, enabling real-time estimation of left and right track slip ratios. Based on the estimated slip difference, a target-point offset compensation mechanism is constructed, and the offset angle is optimized online using an improved particle swarm optimization (PSO) algorithm with a Chebyshev-window-based inertia weight strategy. In addition, a fuzzy controller is employed to adaptively adjust the look-ahead distance according to vehicle speed and path curvature, while a first-order low-pass filter is applied to smooth the commanded velocities.ResultsSimulation results demonstrate that the proposed method significantly reduces lateral tracking errors and maintains smooth trajectories under severe slip conditions. Field experiments conducted at speeds of 0.35 m/s and 0.75 m/s show that the proposed method reduces the maximum lateral deviation by 78.1% and the average deviation by 50.6% compared with the traditional fuzzy pure pursuit algorithm. At 0.75 m/s, the maximum and average deviations are further reduced by 63.1% and 57.6%, respectively.DiscussionThe results confirm that incorporating slip estimation and slip-aware target-point offset compensation effectively enhances path-tracking accuracy and robustness for tracked agricultural vehicles operating on soft and high-slip terrain. The proposed lightweight control framework provides a practical and reliable solution for autonomous navigation and plant-protection operations in complex farmland environments.

Highlights UAVs enable high-throughput, high-resolution agricultural data collection. UAV image data present unique technical challenges and requirements. UAV technology is rapidly expanding beyond data collection in agriculture. ABSTRACT. The use of unmanned aerial vehicles (UAVs) in agriculture is a relatively new phenomenon, with a clear role in high-throughput phenotyping and precision agriculture through rapid collection of high-resolution crop data. This special collection provides a snapshot of the state of the art, highlighting agricultural applications, advanced sensor technologies, and emerging uses of artificial intelligence in UAV-based image analysis. Keywords: Drones, Precision agriculture, Remote sensing, UAS.

The analysis of critical driving maneuvers, the development of control systems, and the introduction of autonomous driving functions rely on virtual simulation environments. Vehicles are commonly modeled using multibody systems supported by commercial software that enables efficient setup, testing, and simulation. A functional vehicle model consists of rigid or flexible bodies, joints, and force elements. Passenger cars and trucks typically follow standardized model structures composed of predefined submodels such as drivetrain, steering, and suspension. Special-purpose vehicles, however, represent a much broader range of multibody systems, from simple rigid models to complex configurations with flexible components and moving contacts. The RecurDyn software package addresses this diversity and includes an integrated finite element solver, making it suitable for advanced vehicle simulations. In all cases, the vehicle model must be complemented by an appropriate tire model. Tire models are commonly divided into comfort and handling models. Comfort tire models are highly complex and require extensive measurements, while handling tire models use semi-physical approaches to approximate steady-state tire behavior with analytical functions and simplified dynamics. Most established handling models are tailored to passenger car tires. The TMeasy tire model, originally developed for agricultural tractor tires, uses physical parameters that can be derived from measurements or estimated by experienced engineers, which is especially beneficial when data are scarce. Its threedimensional slip formulation ensures smooth transitions between standstill and motion. Combined with RecurDyn, TMeasy enables the simulation of a wide range of vehicles, particularly special-purpose vehicles with unconventional tires.

Human-following control of an autonomous agricultural vehicle using an adaptive navigation point time elastic band method