Search Results (150)

In modern agriculture, tractors play a crucial role in powering tools and implements. Proper operation of agricultural tractors in mechanized field operations can support sustainable agriculture and reduce emissions of pollutants such as carbon dioxide (CO₂). This has been a recurring concern associated with agricultural intensification for food production. This study aimed to evaluate the optimization of tractor gears and engine speed during crop operations to minimize CO₂ emissions and promote sustainability. The experiment was conducted using a strip plot design with subdivided sections and six replications, following a double factorial structure. The first factor evaluated was the type of agricultural implement (disc harrow, subsoiler, or sprayer), while the second factor was the engine speed setting (nominal or reduced). Operational and energy performance metrics were analyzed, including fuel consumption and CO₂ emissions, travel speed, effective working time, wheel slippage, and working depth. Optimized gear selection and engine speeds resulted in a 20 to 40% reduction in fuel consumption and CO₂ emissions. However, other evaluated parameters remain unaffected by the reduced engine speed, regardless of the implement used, ensuring the operation’s quality. Thus, optimizing operator training or configuring machines allows for environmental impact reduction, making agricultural practices more sustainable. Full article

This paper presents the design and implementation of a SLAM-based online mapping and autonomous trajectory execution system for the Nimbulus-e, a concept vehicle designed for agile maneuvering in confined spaces. The Nimbulus-e uses individual steer-by-wire corner modules with in-wheel motors at all four corners. The associated eight joint variables serve as control inputs, allowing precise trajectory following. These control inputs can be derived from the vehicle’s trajectory using nonholonomic constraints. A LiDAR sensor is used to map the environment and detect obstacles. The system processes LiDAR data in real time, continuously updating the environment map and enabling localization within the environment. The inclusion of vehicle odometry data significantly reduces computation time and improves accuracy compared to a purely visual approach. The A* and Hybrid A* algorithms are used for trajectory planning and optimization, ensuring smooth vehicle movement. The implementation is validated through both full vehicle simulations using an ADAMS Car—MATLABco-simulation and a scaled physical prototype, demonstrating the effectiveness of the system in navigating complex environments. This work contributes to the field of autonomous systems by demonstrating the potential of combining advanced sensor technologies with innovative control algorithms to achieve reliable and efficient navigation. Future developments will focus on improving the robustness of the system by implementing a robust closed-loop controller and exploring additional applications in dense urban traffic and agricultural operations. Full article

In addressing the challenges of low path tracking accuracy and poor robustness during tractor autonomous operation, this paper proposes a path tracking control method for tractors that integrates prescribed performance with sliding mode control (SMC). A key feature of this control method is its inherent immunity to system parameter perturbations and external disturbances, while ensuring path tracking errors are constrained within a predefined range. First, the tractor is simplified into a two-wheeled vehicle model, and a path tracking error model is established based on the reference operation trajectory. By defining a prescribed performance function, the constrained tracking control problem is transformed into an unconstrained stability control problem, guaranteeing the boundedness of tracking errors. Then, by incorporating SMC theory, a prescribed performance sliding mode path tracking controller is designed to achieve robust path tracking and error constraint for the tractor. Finally, both simulation and field experiments are conducted to validate the method. The results demonstrate that compared with the traditional SMC method, the proposed method effectively mitigates the impact of complex farmland conditions, reducing path tracking errors while enforcing strict error constraints. Field experiment data shows the proposed method achieves an average absolute error of 0.02435 m and a standard deviation of 0.02795 m, confirming its effectiveness and superiority. This research lays a foundation for the intelligent development of agricultural machinery. Full article

Soil compaction from mechanized agriculture is a major concern, as frequent machinery use degrades soil structure, reduces porosity, and ultimately impairs crop productivity. Among potential mitigation strategies to enhance soil resilience to machinery-induced compaction, biochar has shown promise in laboratory settings but remains untested under real field conditions. To address this, we monitored soil in a Tuscan vineyard where biochar was applied at 16 and 32 Mg ha⁻¹, compared to control, on both flat and sloped plots. Soil compaction was induced by 20 passes of a wheeled orchard tractor. Soil bulk density (BD) was measured before, immediately after, and one year following the initial passes, during which 19 additional machine passes occurred as part of the vineyard’s routine agronomic management. Initial results showed a significant BD increase (up to 12.8%) across all treatments, though biochar significantly limited soil compaction, regardless of the applied dose. After one year, in which the soil underwent further compaction, BD further increased across all treatments (up to 20.2%), with the steepest increase observed on the sloped terrain. At this stage, the mitigating effect of biochar on soil compaction was no longer evident. Our findings suggest that biochar may offer some short-term relief from compaction, but further investigations are needed to clarify its long-term effectiveness under field conditions. Full article

This study investigates the operational performance of fruit-picking robots under varying terrain slopes and soil moisture conditions, with a focus on comparing wheeled and tracked locomotion systems. A modular robot platform was designed and tested in both controlled environments and actual mountainous orchards in Shandong, China. The experiments assessed key performance metrics—average speed, slip rate, and path deviation—under combinations of four slope levels (0°, 8°, 18°, 28°) and three soil moisture levels (dry 10%, moderate 20%, wet 35%). Results reveal that wheeled robots perform optimally on dry and flat terrain but experience significant slippage and path deviation under steep and wet conditions. In contrast, tracked robots maintain better stability and terrain adaptability, demonstrating lower slip rates and more consistent trajectories across a wide range of conditions. A synergistic deterioration effect was observed when high slope and high soil moisture co-occur, significantly degrading the performance of wheeled systems, while tracked systems mitigated these effects. Complementary semi-structured interviews with 20 orchard stakeholders—including farmers, growers, and hired pickers—highlighted key user expectations: robust traction, terrain adaptability, reduced physical labor, and operational safety. The findings suggest that future agricultural robots should adopt adaptive hybrid mobility systems and integrate environmental perception capabilities to enhance performance in complex agricultural scenarios. These insights contribute practical and theoretical guidance for the design and deployment of intelligent fruit-picking robots in diverse field environments. Full article

The disadvantages of traditional strip fertilization technology for corn planting in China include low fertilizer utilization rates, unstable operation quality, and environmental pollution. Therefore, in this study, a synchronous hole fertilization device for corn planting based on real-time intelligent control is designed, aiming to reduce fertilizer application and increase efficiency through the precise alignment technology of the seed and fertilizer. This device integrates an electric drive precision seeding unit, a slot wheel hole fertilization unit, and a multi-sensor coordinated closed-loop control system. An STM32 single-chip micro-computer is used to dynamically analyze the seed–fertilizer timing signal, and a double closed-loop control strategy (the position loop priority is higher than the speed loop) is used to correct the spatial phase difference between the seed and fertilizer in real time to ensure the precise control of the longitudinal distance (40~70 mm) and the lateral distance (50~80 mm) of the seed and fertilizer. Through the Box–Behnken response surface method, a field multi-factor test was carried out to analyze the mechanism of influence of the implemented forward speed (A), per-hole target fertilizing amount (B), and plant spacing (fertilizer hole interval) (C) on the seed–fertilizer alignment qualification rate (Y₁) and the coefficient of variation in the hole fertilizing amount (Y₂). The results showed that the order of primary and secondary factors affecting Y₁ was A > C > B, and that the order affecting Y₂ was C > B > A; the comprehensive performance of the device was best with the optimal parameter combination of A = 4.2 km/h, B = 4.4 g, and C = 30 cm, with Y₁ as high as 94.024 ± 0.694% and Y₂ as low as 3.147 ± 0.058%, which is significantly better than the traditional strip application method. The device realizes the precise regulation of 2~6 g/hole by optimizing the structural parameters of the outer groove wheel (arc center distance of 25 mm, cross-sectional area of 201.02 mm², effective filling length of 2.73~8.19 mm), which can meet the differentiated agronomic needs of ordinary corn, silage corn, and popcorn. Field verification shows that the device significantly improves the spatial distribution of the concentration of fertilizer, effectively reduces the amount of fertilizer applied, and improves operational stability and reliability in multiple environments. This provides technical support for the regional application of precision agricultural equipment. Full article

This study develops a real-time energy-efficient control strategy for distributed-drive electric tractors (DDETs) to minimize electrical energy consumption during traction operations. Taking a four-wheel independently driven DDET as the research object, we conduct dynamic analysis of draft operations and establish dynamic models of individual components in the tractor’s drive and transmission system. A backpropagation (BP) neural network-based operational speed prediction model is constructed to forecast operational speed within a finite prediction horizon. Within the model predictive control (MPC) framework, a real-time energy-efficient control strategy is formulated, employing a dynamic programming algorithm for receding horizon optimization of energy consumption minimization. Through plowing operation simulation with comparative analysis against a conventional equal torque distribution strategy, the results indicate that the proposed real-time energy-efficient control strategy exhibits superior performance across all evaluation metrics, providing valuable technical guidance for future research on energy-efficient control strategies in agricultural electric vehicles. Full article

Precision seeding plays a critical role in optimizing crop yield and resource efficiency. This study evaluates the application of a Seed Miss Prevention System (SMPS) integrated with a spoon-wheel precision metering device to mitigate seed misses and enhance its performance. A combination of Discrete Element Method (DEM) simulations, electrical hardware design, mechanical retrofitting, software development and laboratory experiments was employed to assess the effectiveness of the system across multiple seed cultivars and operating speeds. Experimental results demonstrated that the SMPS significantly reduced seed misses at lower operational speeds (3–10 rpm), with the implementation of a dual-sensor configuration further improving detection accuracy by filtering out false positives. At higher speeds (≥15 rpm), however, seed miss rates increased, particularly for irregularly shaped seeds like white beans ‘Great Northern’, due to the mechanical limitations of the metering device. Statistical analyses, including Tukey’s HSD test, confirmed the effectiveness of the SMPS in reducing miss rates across different seed types. Despite these improvements, complete elimination of seed misses was not achieved, highlighting the need for further optimization in seed miss detection. Future research should explore adaptations for higher-speed metering devices and field-scale validations. The findings underscore the potential of SMPS technology in advancing precision agriculture by improving seeding accuracy and operational efficiency. Full article

The terrain in hilly and mountainous areas is complex, and the level of agricultural mechanization is low. This article systematically reviews the research progress of key technologies for agricultural machinery power chassis in hilly and mountainous areas, and conducts an analysis of five aspects: the power system, walking system, steering system, leveling system, and automatic navigation and path tracking control system. In this manuscript, (1) in terms of the power system, the technical characteristics and application scenarios of mechanical, hydraulic, and electric drive systems were compared. (2) In terms of the walking system, the performance differences between wheeled, crawler, legged, and composite walking devices and the application of suspension systems in agricultural machinery chassis were discussed. (3) In terms of the steering system, the steering characteristics of wheeled chassis and crawler chassis were analyzed, respectively. (4) In terms of the leveling system, the research progress on hydraulic and electric leveling mechanisms, as well as intelligent leveling control algorithms, was summarized. (5) The technology of automatic navigation and path tracking for agricultural machinery chassis was discussed, focusing on multi-sensor fusion and advanced control algorithms. In the future, agricultural machinery chassis will develop towards the directions of intelligence, automation, greening, being lightweight, and being multi-functionality. Full article

This study evaluates the safety aspects of a prototype electric vehicle designed to enable wheelchair users to independently perform simple farm tasks in rural settings, like sample collection and crop monitoring. The vehicle, built at CREA, features four in-wheel electric motors, a pneumatic suspension system, and a secure wheelchair anchoring system. Tests at the CREA experimental farm assessed the vehicle’s whole-body vibrations on different surfaces (asphalt, headland, dirt road) using two tyre models and multiple speeds. A triaxial accelerometer on the wheelchair seat measured vibrations, which were analysed in accordance with ISO standards. Frequency analysis revealed significant vibrations in the 2–40 Hz range, with the Z-axis consistently showing the highest accelerations, which increased with the speed. Tyre A generally induced higher vibrations than Tyre B, likely due to the tread design. At high speeds, the effective accelerations exceeded safety thresholds on asphalt and headland. Statistical analysis confirmed speed as the dominant factor, with the surface type also playing a key role—headland generated the highest vibrations, followed by dirt road and asphalt. The results of these first tests highlighted the high potential of the vehicle to improve the agricultural mobility of disabled people, granting safety conditions and low vibration levels on all terrains at speeds up to 10 km h⁻¹. At higher speeds, however, the vibration levels may exceed the exposure limits, depending on the irregularities of the terrain and the tyre model. Overcoming these limitations is achievable through the optimization of the suspensions and tyres and will be the subject of the next step of this study. This technology could also support wheelchair users in construction, natural parks, and urban mobility. Full article

Changes in soil properties under mechanical stress significantly influence the emergence and growth of crops, with different crops responding differently to these changes. To explore the impact of different soil compaction states on crop growth, field experiments were conducted in 2022 and 2023 in sandy loam soil in Manitoba, Canada. The crops (canola and soybean) were planted under three compaction levels created by a seeder’s press roller: no press roller (P0), one pass of the press roller (P1), and two passes of the press roller (P2). Soil mechanical properties and plant growth were measured for each treatment. The results indicated that soil shear strength increased significantly with each level of compaction from P0 to P1 and from P1 to P2, while soil surface resistance remained largely unaffected. Interestingly, soybean and canola responded differently to soil compaction. Soybeans showed no significant changes in emergence speed or final plant population across the three treatments. Conversely, canola exhibited over a 50% increase in emergence speed and more than a 100% increase in final plant population with either one (P1) or two passes (P2) of the press roller, compared to the no press roller (P0) treatment. These findings provide valuable guidance for agricultural producers and engineers in adjusting the down pressure of seeder press wheels when planting different crops. Full article

Autonomous driving solutions for agricultural machinery have advanced rapidly; however, large-wheeled harvesters present unique challenges compared to traditional vehicles. Specifically, the 5.4 m cutting width, 9.2 m minimum turning diameter, and rear-wheel–steered configuration demand specialized path tracking and steering methods. To address these challenges, this study developed an integrated system combining feedforward PID and Look-Ahead Ackermann (LAA) algorithms with sensors, actuators, and an embedded control platform. Field experiments indicated that the system maintained an average lateral deviation of approximately 5 cm on straight-line paths, with slightly larger errors observed only during turning or alignment maneuvers. Additionally, a “three-cut” steering method was implemented, which enhanced path tracking accuracy and prevented crop damage at headland turns. Successful field tests confirmed the robustness of the developed system, highlighting its practical potential for production-level autonomous harvesting. 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

Accidents and deaths at work are a persistent problem, with numbers still worrying. The agricultural and forestry sector is among the most exposed to work risks, with particular attention to noise risk from the use of agricultural machinery and operators. This study aims to analyze the exposure to noise risk during use of wheeled and tracked tractors, with or without a cab, as well as other operating machines. The analysis takes into account the parameters Lpeak (peak sound pressure values), LAeq.T (time-weighted equivalent noise exposure levels) and LAS (maximum and minimum values weighted according to the Slow time constant) in order to assess the noise impact and define strategies for improving the safety and health of workers. This study demonstrates that in multiple cases, the regulatory thresholds for the examined variables are exceeded, regardless of the presence of a cabin. Specifically, Lpeak values approach 140 dB, dangerous to human health, while LAeq.T levels are close to or, in some instances, exceed 87 dB. It is also verified that agricultural and forestry operators who mainly use crawler tractors have greater and constant exposure to noise compared to those who use tractors with a cabin. Full article

Sustainable agriculture is central to addressing the difficulties farmers face, such as a lack of manpower, high input prices, and environmental effects from the widespread use of chemical herbicides. In farming, eliminating unwanted plants from crops is a laborious task crucial for enhancing sustainable crop yield. Traditionally, this process is carried out manually globally, utilizing tools such as wheel hoes, sickles, chris, powers, shovels, and hand forks. However, this manual approach is time-consuming, demanding in terms of labor, and imposes significant physiological strain, leading to premature operator fatigue. In response to this challenge, blade and tine-type push weeders were developed to enhance weeding efficiency for smallholder farmers. When blade and tine push weeders are pushed between the rows of crops, the front tine blade of the trolley efficiently uproots the weeds, while the straight blade at the back pushes the uprooted weeds. This dual-action mechanism ensures effective weed elimination by both uprooting and clearing the weeds without disturbing the crops. The blade and tine-type push weeders demonstrated actual and theoretical field capacities of 0.020 ha/h and 0.026 ha/h, achieving a commendable field efficiency of 85%. The weeders exhibited a cutting width ranging from 30 to 50 mm, a cutting depth between 250 and 270 mm, a draft of 1.8 kg, a weeding efficiency of 78%, and a plant damage rate of 2.7%. The cost of weeding was 2108 INR/ha for the green pea crop. Full article