Search Results (248)

This research aims to analyze the evolution of productivity and efficiency in tractors featuring varying propulsion system designs through the development of a parametric modeling approach. Recognizing that large row-crop tractors represent a significant capital investment—ranging from USD 0.4 to over 0.8 million for current-generation models—and that machinery costs constitute a substantial share of farm production expenses, this study addresses the urgent need for data-driven decision-making in agricultural enterprises. Utilizing consolidated OECD Code 2 tractor test data for all large row-crop John Deere tractors from the MFWD era to the latest generation, the study evaluates tractor performance across multiple productivity and efficiency indicators. The analysis culminates in the creation of a robust, user-friendly parametric model (R² = 0.9337, RMSE = 1.0265), designed to assist stakeholders in making informed decisions regarding tractor replacement or upgrading. By enabling the optimization of productivity and efficiency while accounting for agronomic and timeliness constraints, this model supports sustainable and profitable management practices in modern agriculture. Full article

Compression–ignition engines emit particulate matter (PM) (soot), prompting the widespread use of diesel particulate filters (DPFs) in the automotive sector. An alternative method for PM reduction involves the use of ultrasonic waves to disperse and modify the structure of exhaust particles. This article presents experimental results of the effects of ultrasonic emitter parameters, including the number, arrangement, and power, along with the engine speed, on the exhaust smoke density. Tests were conducted on a laboratory prototype equipped with six ultrasonic emitters spaced 0.17 m apart. The exhaust source was a diesel engine from a construction excavator, based on the MTZ-80 tractor design, delivering 80 HP and a displacement of 4750 cm³. A regression model was developed to describe the relationship between the engine speed, emitter power and spacing, and smoke density. The optimal configuration was found to involve an emitter power of 319.35 W and a spacing of 1.361 m for a given engine speed. Under the most effective conditions—an engine speed of 1500 rpm, six active emitters, and a total power of 600 W—smoke emissions were reduced by 18%. These findings support the feasibility of using ultrasonic methods as complementary or alternative exhaust gas filtration techniques for non-road diesel engines. Full article

The objective of this study was to improve measurement accuracy in the evaluation of autonomous agricultural tractor performance by addressing external disturbances, such as sensor installation errors, vibrations, and heading-induced bias that occur during the measurement of the conventional rear three-point hitch (Rear 3-Point) system. To mitigate these disturbances, the measurement point was relocated to the cab, where external interference is comparatively minimal. However, in compliance with the ISO 12188 standard, the Rear 3-Point system must be used as the reference measurement point. Therefore, its coordinates were indirectly estimated using an extended Kalman filter (EKF) and artificial intelligence (AI)-based techniques. A hybrid model was developed in which a transformer-based AI model was trained using the Rear 3-Point coordinates predicted by EKF as the ground truth. While traditional time-series models, such as LSTM and GRU, show limitations in predicting nonlinear data, the application of an attention mechanism was found to enhance prediction performance by effectively learning temporal dependencies and vibration patterns. The experimental results show that the EKF-based estimation achieved a precision of RMSE 1.6 mm, a maximum error of 12.6 mm, and a maximum standard deviation of 3.9 mm compared to actual measurements. From the perspective of experimental design, the proposed hybrid model was able to predict the trajectory of the autonomous agricultural tractor with significantly reduced external disturbances when compared to the actual measured Rear 3-Point coordinates, while also complying with the ISO 12188 standard. These findings suggest that the proposed approach provides an effective and integrated solution for developing high-precision autonomous agricultural systems. Full article

To investigate the relationship between the seeding performance of a novel pre-cut sugarcane planter designed by South China Agricultural University and operational settings, field seeding tests was conducted with the following protocol: First, the John Deere M1654 tractor’s forward velocity was calibrated, and the planter’s safe loading capacity was determined. Subsequently, eight experimental treatments (A–H) were designed to quantify the relationships between the three performance indicators: seeding density N, the seeding efficiency E and seeding uniformity (coefficient of variation, CV), and three key operational parameters: forward speed of planter v, the discharging sprocket rotational speed n, and the hopper outlet size w. Mathematical models (R20.979) between three key operational parameters with two performance indicators (N, E) was developed through analysis of variance (ANOVA) and regression analysis. The seeding rate per meter was confirmed to follow a Poisson distribution based on Kolmogorov–Smirnov (K–S) tests. When the CV was below 40%, the mean relative error remained within 3%. These findings provide a theoretical foundation for seeding performance prediction under field conditions. Full article

This study investigated the application of Finite Element Analysis (FEA) to optimize the design and material selection for the construction of the telescopic arm of an elevating work platform (EWP) used in agricultural environments. By comparing the structural performance of four materials—Aluminum Alloy (EN-AW 1200), Aluminum Alloy (EN-AW 2014), High-Strength Low-Alloy (HSLA) Steel Fe275JR, and HSLA Steel S700—under simulated operational conditions, this research identified the most suitable material for robust yet lightweight platforms. The results revealed that HSLA Steel S700 provides superior performance in terms of strength, low deformation, and high safety factors, making it ideal for scenarios requiring maximum durability and load-bearing capacity. Conversely, Aluminum Alloy (EN-AW 2014), while exhibiting lower strength compared with HSLA Steel S700, significantly reduces platform weight by approximately 60% and lowers the center of gravity, enhancing maneuverability and compatibility with smaller, less powerful tractors. These findings highlight the potential of FEA in optimizing EWP design by enabling precise adjustments to material selection and structural geometry. The outcomes of this research contribute to the development of safer, more efficient, and cost-effective EWPs, with a specific focus on improving productivity and safety in agricultural operations such as pruning and harvesting. Future work will explore advanced geometries and hybrid materials to further enhance the performance and versatility of these platforms. Full article

In this study, the effects of different heat sink designs on the cold side of the modules in a thermoelectric generator (TEG) system placed between the compressor and the intercooler of a turbocharged tractor on the system performance were numerically analyzed. In the current literature, heat sinks used in TEG modules generally consist of plate fins. In this study, by using perforated and slotted fins, the thermal boundary layer behaviors were changed and there was an attempt to increase the heat transfer from the cold surface compared to plate fins. Thus, the performance of the TEG system was also increased. When looking at the literature, it is seen that there are studies which aim to increase the performance of TEG modules by changing the dimensions of p and n type semiconductors. However, there is no study aiming to increase the performance of TEG modules by making changes on the plate fins of the heat sinks used in these modules and thus increasing the heat transfer amount. In this respect, this study offers important results for the literature. According to the numerical analysis results, the total TEG output power, output voltage, and thermal efficiency obtained for S0.5H15 were 6.2%, about 3%, and about 5% higher than those for PF, respectively. In addition, the pressure drop values obtained for different heat sinks, except for aluminum foam, were approximately close to each other. In cases with TEG systems where different heat sinks were used, the intercooler inlet air temperatures decreased by approximately 3.4–3.5% compared to the case without the TEG system. This indicates that the use of TEG will positively affect the improvement in engine efficiency. Full article

The agricultural sector faces growing pressure to enhance productivity and sustainability, prompting innovation in machinery design. Traditional materials such as steel still dominate but are a cause of increased weight, soil compaction, increased fuel consumption, and corrosion. Composite materials—and, more specifically, fiber-reinforced polymers (FRPs)—offer appealing alternatives due to their high specific strength and stiffness, corrosion resistance, and design flexibility. Meanwhile, increasing environmental awareness has triggered interest in biocomposites, which contain natural fibers (e.g., flax, hemp, straw) and/or bio-based resins (e.g., PLA, biopolyesters), aligned with circular economy principles. This review offers a comprehensive overview of synthetic composites and biocomposites for agricultural machinery and equipment (AME). It briefly presents their fundamental constituents—fibers, matrices, and fillers—and recapitulates relevant mechanical and environmental properties. Key manufacturing processes such as hand lay-up, compression molding, resin transfer molding (RTM), pultrusion, and injection molding are discussed in terms of their applicability, benefits, and limits for the manufacture of AME. Current applications in tractors, sprayers, harvesters, and planters are covered in the article, with advantages such as lightweighting, corrosion resistance, flexibility and sustainability. Challenges are also reviewed, including the cost, repairability of damage, and end-of-life (EoL) issues for composites and the moisture sensitivity, performance variation, and standardization for biocomposites. Finally, principal research needs are outlined, including material development, long-term performance testing, sustainable and scalable production, recycling, and the development of industry-specific standards. This synthesis is a practical guide for researchers, engineers, and manufacturers who want to introduce innovative material solutions for more efficient, longer lasting, and more sustainable agricultural machinery. Full article

The body roll of the tractor propagates through its rigid hitch system to the mounted implement, causing asymmetrical soil penetration depths between the implement’s lateral working elements, which affects the operational effectiveness of the implement. To address this issue, this study developed an automatic leveling system based on a dual closed-loop fuzzy Proportional-Integral-Derivative (PID) algorithm for tractor-mounted implements. The system employed an attitude angle sensor to detect implement posture in real time and utilized two double-acting hydraulic cylinders to provide a compensating torque for the implement that is opposite to the direction of the body’s roll. The relationship model between the implement’s roll angle and the actuator’s response time was established. The controller performed implement leveling by regulating the spool position and holding time of the solenoid directional valve. Simulink simulations showed that under the control of the dual closed-loop fuzzy PID algorithm, the implement’s roll angle adjusted from 10° to 0° in 1.72 s, which was 56.89% shorter than the time required by the fuzzy PID algorithm, with almost no overshoot. This demonstrates that the dual closed-loop fuzzy PID algorithm outperforms the traditional fuzzy PID algorithm. Static tests showed the system adjusted the implement roll angle from ±10° to 0° within 1.3 s. Field experiments demonstrated that the automatic leveling system achieved a maximum absolute error (MaxAE) of 0.91°, a mean absolute error (MAE) of 0.19°, and a root mean square error (RMSE) of 0.28°, with errors within 0.5° for 92.52% of the time. Results from terrain mutation tests indicate that under a sudden 5° vehicle roll angle change, the system confines implement deviation to ±1.5°. The system exhibits high control precision, stability, and robustness, fulfilling the demands of tractor-mounted implement leveling. Full article

With the acceleration of agricultural intelligent transformation, deep reinforcement learning (DRL), leveraging its adaptive perception and decision-making capabilities in complex environments, has emerged as a pivotal technology in advancing the intelligent upgrade of agricultural machinery and equipment. For example, in UAV path optimization, DRL can help UAVs plan more efficient flight paths to cover more areas in less time. To enhance the systematicity and credibility of this review, this paper systematically examines the application status, key issues, and development trends of DRL in agricultural scenarios, based on the research literature from mainstream Chinese and English databases spanning from 2018 to 2024. From the perspective of algorithm–hardware synergy, the article provides an in-depth analysis of DRL’s specific applications in agricultural ground platform navigation, path planning for intelligent agricultural end-effectors, and autonomous operations of low-altitude unmanned aerial vehicles. It highlights the technical advantages of DRL by integrating typical experimental outcomes, such as improved path-tracking accuracy and optimized spraying coverage. Meanwhile, this paper identifies three major challenges facing DRL in agricultural contexts: the difficulty of dynamic path planning in unstructured environments, constraints imposed by edge computing resources on algorithmic real-time performance, and risks to policy reliability and safety under human–machine collaboration conditions. Looking forward, the DRL-driven smart transformation of agricultural machinery will focus on three key aspects: (1) The first aspect is developing a hybrid decision-making architecture based on model predictive control (MPC). This aims to enhance the strategic stability and decision-making interpretability of agricultural machinery (like unmanned tractors, harvesters, and drones) in complex and dynamic field environments. This is essential for ensuring the safe and reliable autonomous operation of machinery. (2) The second aspect is designing lightweight models that support edge-cloud collaborative deployment. This can meet the requirements of low-latency responses and low-power operation in edge computing scenarios during field operations, providing computational power for the real-time intelligent decision-making of machinery. (3) The third aspect is integrating meta-learning with self-supervised mechanisms. This helps improve the algorithm’s fast generalization ability across different crop types, climates, and geographical regions, ensuring the smart agricultural machinery system has broad adaptability and robustness and accelerating its application in various agricultural settings. This paper proposes research directions from three key dimensions-“algorithm capability enhancement, deployment architecture optimization, and generalization ability improvement”-offering theoretical references and practical pathways for the continuous evolution of intelligent agricultural equipment. Full article

The advancement of precision agriculture increasingly depends on innovative technological solutions that optimize resource utilization and minimize environmental impact. This paper introduces a novel heterogeneous federated learning architecture specifically designed for intelligent agricultural systems, with a focus on combine tractors equipped with advanced nutrient and crop health sensors. Unlike conventional FL applications, our architecture uniquely addresses the challenges of communication efficiency, dynamic network conditions, and resource allocation in rural farming environments. By adopting a decentralized approach, we ensure that sensitive data remain localized, thereby enhancing security while facilitating effective collaboration among devices. The architecture promotes the formation of adaptive clusters based on operational capabilities and geographical proximity, optimizing communication between edge devices and a global server. Furthermore, we implement a robust checkpointing mechanism and a dynamic data transmission strategy, ensuring efficient model updates in the face of fluctuating network conditions. Through a comprehensive assessment of computational power, energy efficiency, and latency, our system intelligently classifies devices, significantly enhancing the overall efficiency of federated learning processes. This paper details the architecture, operational procedures, and evaluation methodologies, demonstrating how our approach has the potential to transform agricultural practices through data-driven decision-making and promote sustainable farming practices tailored to the unique challenges of the agricultural sector. Full article

Binary logic transmission (BLT), a stepped transmission system, has been utilized in military vehicles and heavy-duty commercial vehicles due to its high transmission efficiency, strong load-bearing capacity, and compact structure. Its adaptability to agricultural tractor operations is notable. This study modularizes BLT into a binary logic transmission unit (BLT-U) for application in agricultural tractor Hydro-Mechanical Continuously Variable Transmission (HMCVT), optimizing its wet clutch to enhance HMCVT shifting performance. This provides a basis for BLT-U’s application in other transmission systems and subsequent optimization. A wet clutch test bench was employed to validate the modeling approach. The optimal BLT-U configuration was selected using both light/heavy load conditions and subjective–objective evaluation criteria. The WOA improved the spread value in the GRNN algorithm, establishing a GRNN to predict the optimal range for wet clutch design values in BLT-U; the model validation showed an average correlation coefficient of 0.92 for speed curves and an average relative error of 5.58% for dynamic loads. Under light-load conditions, the optimal configuration improved average and maximum scores by 13.38% and 11.53%, respectively, while under heavy-load conditions, the corresponding improvements were 9.38% and 5.86%. Under light-load conditions, the optimized GRNN reduced total relative error by 39.6%, while under heavy-load conditions, it achieved a 61% reduction. This study confirms the rationality of the modeling method, identifies Configuration 1 as optimal, and determines the optimal range for clutch design values under light-load and heavy-load conditions, respectively. Full article

As agricultural tractors function under various soil and environmental conditions, optimising their design and paraameter settings for enhanced traction performance is essential for maximising their operational efficiency. This study aimed to assess the traction capabilities of standard tractors, ensuring effective operations even under highly demanding conditions. A traction load measurement system was refined to collect performance data, and standardised tests were conducted on a concrete track to evaluate key traction metrics. The analysis considered drawbar pull, traction force, travel reduction (slip), and net traction ratio. Two tractors from the same model series, featuring similar design characteristics but differing in engine power, were compared. Three primary parameters—tractor mass, tyre pressure, and engine power—were evaluated across six distinct operating conditions. Results recorded at forward speeds below 6 km/h indicated that lower tyre pressure led to an increased net traction ratio due to the enhanced drawbar pull. Additionally, an increase in tractor mass contributed to a higher drawbar pull, which, in turn, improved traction force across all speed ranges. The maximum traction force was not significantly affected between 66 kW and 86 kW tractors at speeds up to 4 km/h. Meanwhile, the traction force remained high up to velocities of 6 km/h in the 86 kW tractor. The efficiency (i.e., the ratio between measured and declared power) varied between 64% and 70% for the 66 kW tractor and between 70% and 74% for the 86 kW tractor. The travel reduction was mainly affected by the power of the tractor. The slip caused a reduction close to 4 and 6 km/h for the 66 kW and 86 kW agricultural tractors, respectively. Overall, the proper adjustment of tractor parameters significantly impacted their traction performance, and the findings provide valuable insights for improving tractor designs and field applications. Full article

The fifth wheel of the semi-trailer tractor is a key component connecting the tractor and the semi-trailer. During operation, the fifth wheel experiences frequent irregular and repetitive loading conditions. This leads to a decline in its durability and fatigue life, which can significantly impact the efficiency of cargo transport. The lightweight design enhances both the transport efficiency and fuel economy of the semi-trailer tractor. In this research, to achieve weight reduction while maintaining the wear-resistant failure protection performance in semi-trailer tractors, we selected a new material—special steel for saddles (SD600). Its stress-strain and fatigue life were analyzed under static compression, uphill lifting, and steering rollover conditions. These findings confirm the necessity of implementing lightweighting measures. Using a multi-objective genetic algorithm, we established an optimization model aimed at balancing weight reduction and fatigue life enhancement. As a result, the optimized fifth wheel achieved a 24.11% reduction in mass, while its fatigue life increased by 15 times, thus realizing the synergistic optimization of weight and fatigue life. We proposed a prediction model combining a random forest algorithm with an optimized back propagation (BP) neural network. Compared to the traditional BP approach, this model improved the mean absolute percentage error (MAPE) by 47.62%. Quadratic optimization was conducted based on the optimal design option set, using data analysis to determine the range of values of each variable under specific constraints and to verify the stress-strain and fatigue life for very small values in the range. Full article

Currently, the entire agricultural sector is under significant pressure. The causes that may explain this are different, such as climate change, market instability, and the decline in the population of agricultural workers. As a result, the agricultural tractor and machinery field is at the center of an intense technological revolution. One of the possible solutions to the aforementioned problems can be represented by agricultural vehicles equipped with autonomous driving systems. The key pillar of an autonomous driven vehicle is its autonomous driving algorithm which represents the link between the information coming from the vehicle’s sensor systems and the success of the vehicle’s operative mission. In this paper, an experimental assessment of the motion strategy for a four-wheel differential drive agricultural rover was conducted. This work is structured in three parts. First, the description of the working principles of the autonomous driving algorithm is proposed. Then, the case study and the scaled prototype designed for this purpose are described. In the end, the result obtained by the virtual model, which acts as reference case, is compared with the results that came out of the field test campaign. The outcomes show the overlap between the virtual and real results. Full article

Countries around the world have developed standards for ultra-low energy consumption building design and future plans. Unfortunately, these standards lack specific requirements for industrial heritage. As an important carrier of urban context, history, and the transmission of residents’ memories, industrial heritage cannot be overlooked in urban development. This study uses DesignBuilder energy simulation software to model industrial heritage (taking the Changchun Tractor Factory as an example) and compares the energy consumption before and after renovation strategies. The results show that in the Case 4 plan, after implementing the renovation strategy, heating energy consumption can be reduced by about 11,648 (kWh/m²) over the heating season, the total primary energy was reduced by about 4 million (kgce/tce), and total energy consumption decreases by approximately 95%. This demonstrates the effectiveness of the industrial heritage reuse design strategy proposed in this paper. It provides a new direction for reuse design under ultra-low energy consumption requirements in related case studies. Full article