Search Results (430)

Dynamic network conditions in precision agriculture motivate a scalable, privacy preserving federated learning architecture that tightly integrates ground-based edge intelligence with a space-assisted hierarchical aggregation layer. In Phase 1, heterogeneous tractors act as intelligent farm nodes that train local models, form capability- and task-aware clusters, and employ Network Quality Index (NQI)-driven scheduling, similarity-based check pointing, and compressed transmissions to cope with highly variable 3G/4G/5G connectivity. In Phase 2, cluster drivers synchronize with Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) satellites that perform regional and global aggregation using staleness- and fairness-aware weighting, while end-to-end Salsa20 + MAC encryption preserves the confidentiality and integrity of all model updates. Across two representative tasks—nutrient prediction and crop health assessment—our full hierarchical system matches or exceeds centralized performance (e.g., AUC 0.92 vs. 0.91 for crop health) while reducing uplink traffic by ∼90% relative to vanilla FedAvg and cutting the communication energy proxy by more than 4×. The proposed fairness-aware GEO aggregation substantially narrows regional performance gaps (standard deviation of AUC across regions reduced from 0.058 to 0.017) and delivers the largest gains in low-connectivity areas (AUC 0.74 → 0.88). These results demonstrate that coupling on-farm intelligence with multi-orbit federated aggregation enables near-centralized model quality, strong privacy guarantees, and communication efficiency suitable for large-scale, connectivity-challenged agricultural deployments. Full article

Improving fuel and energy efficiency in agricultural tillage is critical for sustainable farming and reducing environmental impacts. In this study, the effects of forward speed and tillage depth on the fuel efficiency parameters of a tractor–chisel plough combination were investigated under controlled field conditions on clay soil. Specific fuel consumption (SFC), fuel consumption per unit area (FCPA), and overall energy efficiency (OEE) were evaluated at four forward speeds (0.6, 0.95, 1.2 and 1.4 m·s⁻¹) and four tillage depths (15, 19.5, 23 and 26.5 cm). SFC ranged from 0.519 to 1.237 L·kW⁻¹·h⁻¹, while OEE varied between 7.918 and 18.854%. Higher forward speeds significantly reduced fuel consumption and improved energy efficiency, whereas deeper tillage increased fuel use and reduced efficiency. Optimal operation occurred at speeds of 1.2–1.4 m·s⁻¹ and shallow to medium depths. Five machine learning algorithms: Polynomial Regression (PL), Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), Support Vector Regression (SVR), and Decision Tree Regressor (DTR), were applied to model fuel efficiency parameters. RFR achieved the highest accuracy for predicting SFC, while PL performed best for FCPA and OEE, with the mean absolute percentage error (MAPE) below 2%. Models such as PL and RFR excel in data structures dominated by nonlinear relationships. These results highlight the potential of machine learning to guide data-driven decisions for fuel and energy optimization in tillage, promoting more sustainable mechanization strategies and resource-efficient agricultural production. Full article

To tackle the challenges faced by traditional wheeled tractors, whose steering systems have low flexibility and a large turning radius, and thus make turning hard in small fields and greenhouses, this paper proposes a differential steering control technology for battery-electric unmanned tractors. This innovative approach enables zero-radius turning while delivering environmental and economic advantages. Firstly, the system architecture and key components of the battery-electric unmanned tractor with differential steering are designed, including the mechanical structure, wheel-drive system, electrical system, and power battery. Based on the proposed system architecture, a multi-physics coupled model is established, covering the motor, reducer, battery, driver, vehicle body, and the relationship between tires and road surfaces. A multi-closed-loop control algorithm, regulating both the motor speed and yaw angular velocity of the tractor, is developed for differential steering control. The validation, conducted via a digital simulation platform, yields critical state curves for motor current, torque, speed, and vehicle rotation. This study establishes a novel theoretical framework for unmanned tractor control, with prototype development guided by the proposed methodology. Experimental validation of zero-radius steering confirms the efficacy of differential steering in battery-electric platforms. The research outcomes provide theoretical basis and technical references for advancing intelligent and electric agricultural equipment. Full article

Contemporary nurseries of fruit trees and ornamental plants constitute a key component in the production of high-quality planting material. At present, conventional technology dominates in nurseries in Poland and throughout the European Union. It is based on universal agricultural tractors working with numerous specialized machines—typically underutilized—including sprayers, inter-row cultivation equipment, fertilizer spreaders, and tree lifters. This concept entails several limitations and high investment costs. Because of the considerable size and turning radius of such machinery, a dense network of service roads (every 15–18 m) and wide headlands must be maintained. These areas, which constitute approximately 20% of the total surface, are effectively wasted yet require continuous agronomic maintenance. An alternative concept employs a set of implements mounted on a high-clearance portal tractor (1.6–1.8 m), forming a specialized unit capable of moving above the rows of nursery crops. The study objective of the research was to evaluate the air distribution generated by an air-jet system installed on a crop-spray boom mounted on a portal sprayer, and to assess spray deposition during treatments in nursery trees. Such a configuration enables the mechanization of a broader range of nursery operations than currently possible, while reducing investment costs compared with conventional technology. One still underutilized technology consists of sprayers with an auxiliary airflow (AA) generated by air sleeves. Mean air velocity was measured in three vertical planes, and they showed lower air velocity between 1.0 m and 5.5 m. Spray deposition on apple nursery trees was assessed using a fluorescent tracer. The experimental design consists of a comparative field experiment with and without air flow support, spraying at two standard working rates (200 and 400 L·ha⁻¹) and determining the application of the liquid to plants in the nursery. The results demonstrated a positive effect of the AA system on deposition. At a travel speed of 6.0 km·h⁻¹ and an application rate of 200 L·ha⁻¹, deposition on the upper leaf surface was 68% higher with the fan engaged. For a 400 L·ha⁻¹ rate, deposition increased by 47%, with both differences statistically significant. The study showed that the nursery sprayer mounted on a high-clearance portal tractor and equipped with an AA system achieved an increase of 58% in spray deposition on the upper leaf surface when the fan was operating at 200 L·ha⁻¹ and 28% at 400 L·ha⁻¹. Substantial differences were found between deposition on the upper and lower leaf surfaces, with the former being 20–30 times greater. Given the complexity of nursery production technology, sprayers that ensure the highest possible biological efficacy and high quality of nursery material will play a pivotal role in its development. At the current stage, AA technology fulfils these requirements. Full article

Conventional methods of nano-urea application in maize cultivation, such as tractor-operated gun sprayers, involve high water usage, labor intensity, and operator health risks due to chemical exposure. The drone spraying system ensures precise and automated application of nano-urea with minimal resource use, labor requirement, and operator intervention. However, the efficacy of the drone spraying system for nano-urea application was not evaluated and compared with traditional spraying systems in field conditions. There is a need to evaluate whether drone-based spraying systems can provide an equally effective and more resource-efficient alternative to conventional spraying techniques. Therefore, this study evaluated the agronomic efficacy of a drone-based spraying platform in comparison to conventional tractor-operated gun sprayers for the foliar spray application of nano-urea in the maize crop. Field experiments were conducted during the 2024 Kharif season to evaluate changes in SPAD, NDVI values, and grain yield due to two spray application methods. Both spraying methods showed statistically similar NDVI and SPAD values eight days after nano-urea application, indicating comparable effectiveness in nutrient delivery. Maize yield was also observed to be statistically indistinguishable between the two methods (t (8) = 0.025503, p = 0.9803), with 2912 ± 375 kg/ha (mean ± SE) for the gun sprayer and 2928 ± 503 kg/ha for the drone sprayer treatments. However, the drone system demonstrated significant operational advantages, including 95% water savings and decreased operational time. These findings support the use of drone spraying as a sustainable, safe, and scalable alternative to traditional fertilization application practices in precision agriculture. Full article

Reducing the fuel consumption of tractors has consistently been a critical challenge that the agricultural machinery industry must address. To investigate the energy consumption during the plowing process of tractors and enhance their economic efficiency, this study conducted comparative experiments under varying plowing speeds and depths. In this experiment, the CAN bus protocol was utilized for the collection of engine operational data, such as rotational speed and fuel flow. A GPS positioning system was adopted to measure the plowing speed of the tractor and combined with the data from the tractor coasting test, and then the energy consumption for operating the plow was determined. In addition, a tension sensor was installed on the three-point hitch to measure the horizontal pull force exerted by the five-share plow during plowing, thereby facilitating the calculation of the energy consumption of agricultural machinery. The findings indicate that when the tractor’s plowing speed is maintained at 5.7 km/h, both the average fuel consumption and the fuel consumption per unit area increase as the plowing depth increases. If the plowing depth is fixed at 23 cm, the average fuel consumption rises with an increase in plowing speed, whereas the fuel consumption per unit area decreases. The experimental data show that during the actual tillage operation of the tractor, the brake thermal efficiency of diesel engines ranges from 21.76% to 28.57%. The energy consumed by agricultural implements accounts for only 11.79% to 17.04% of the total fuel energy. The energy consumed in operating the tractor-drawn plow accounts for merely 7.87% to 13.66% of the diesel engine output energy. Approximately 23.24% to 38.69% of the effective power of the diesel engine is lost during the transmission process. This study provides valuable insights for optimizing the performance of tractors during operation. Full article

In recent years, the rapidly changing environment and climate have emphasized the need for sustainable development, particularly in the agricultural sector. Tractors are the most widely used machines in agriculture, making their energy efficiency crucial not only for environmental protection but also for reducing farming costs and enhancing economic sustainability. This study applies Yeo–Johnson data transformation to normalize the discretized data of 111 tractor models, enabling the classification of agricultural tractors based on energy efficiency. Tractors were categorized into five classes according to energy efficiency, and the upper limit of each class was used to quantify the rate of improvement in energy efficiency. Furthermore, a comparative analysis between the classification model from 2006 to 2010 and that from 2016 to 2020 demonstrated that the latter exhibits superior energy consumption efficiency. Specifically, the 2016–2020 model showed an improvement in energy efficiency ranging from approximately 20.57% to 54.86% across all power categories, with higher-rated power tractors achieving greater improvements. This comparison confirms that the energy efficiency of tractors in the latest classification model is further improved, reflecting the substantial technological advancements made over the past decade. Full article

The high-precision establishment of drive motor models and “pump-motor” system models is crucial for the development of the agricultural machinery powertrain. The research of this paper studied the series combination of electric drive continuously variable transmission devices, hydraulic continuously variable transmission devices, and power shift transmission devices to form a dual continuously variable transmission system. A drive motor efficiency characteristics modeling method combining the improved sine cosine optimization algorithm and BP neural network (ISCA-BPNN) and a hydrostatic transmission device efficiency characteristics modeling method combining the partial least squares method and the idea of sampling without replacement (PLS-SWOR) were proposed. Various binary control strategies for agricultural tractors were designed and compared. The results show that the two proposed modeling methods can effectively establish the efficiency characteristics models of the motor and hydrostatic transmission device. For agricultural machinery equipped with a dual continuously variable transmission system, it is advisable to apply the comprehensive binary control strategy under medium and high loads, and the pure economic binary control strategy under medium and low loads. This study is expected to provide support for the high-level design and intelligent strategy development of continuously variable transmission agricultural machinery in the future. Full article

Task-specific off-highway vehicles are typically produced in small volumes, so limited resources must be used in their design. The fuel efficiency benefits of hybridizing an off-highway vehicle are typically in the range of 10–30%, meaning that a simulation tool should ideally be able to predict fuel usage within about ±10%, to support stage-gate design decisions. However, such simulation tools typically require significant cost, setup effort, and simulation expertise. A wheel loader and four agricultural tractors were analyzed with a new tool, “ePOP Concept (v1.0)” from ZeBeyond Ltd. of Leamington Spa, UK, to estimate the benefits of electrification. This method is quick to set up, requiring minimal data preparation and simulation expertise. The results were compared with measured fuel consumption data, and with those of commercially available analysis tools. The errors deriving from ePOP Concept’s BSFC assumptions alone were large at 17% RMS when using a generic value for engine BSFC, but could be improved to 6.7% RMS when applying a readily available minimum BSFC value in the model setup. For future development, a target accuracy of ±10% could potentially be achieved with one-dimensional loss models, requiring minimal extra setup effort, while reducing the subject BSFC errors to 3.9% RMS. Full article

The electrification of agricultural tractors is a key step toward improving energy efficiency and reducing environmental emissions. However, quantitative evaluation of drivetrain performance remains limited because workload data for electric tractors are scarce, while most available datasets originate from conventional mechanical tractors. In this study, a one-dimensional simulation model was developed to effectively utilize existing workload data by integrating the drivetrain and electrical characteristics of an actual electric tractor. The model combines an electrical subsystem based on field-oriented control (FOC) of a permanent magnet synchronous motor (PMSM) with a vehicle subsystem representing the mechanical drivetrain. Model validation was performed through dynamometer experiments using axle torque as input and motor responses as output, showing strong agreement with measured data. The validated model was applied to field-measured workloads to analyze motor performance, battery state-of-charge behavior, usable operating time, and operating points across various agricultural operations. The proposed simulation model enables quantitative evaluation of electric tractor performance under realistic load conditions and can be extended for co-simulation with higher-level control models. In future studies, the model will be utilized as a platform for testing and developing energy-efficient control algorithms for next-generation electric tractor systems. Full article

Land consolidation plays a crucial role in improving agricultural mechanization by optimizing land-use efficiency, reducing transportation distances, and enhancing the operational viability of mechanized farming. This study evaluates the effects of land consolidation on key mechanization indicators in Türkiye, focusing on Kırşehir Province over a 13-year period (2010–2022). By integrating official statistics, field data, and variance-based statistical methods, changes in tractor density, average parcel size, tractor power per hectare, and the number of implements per tractor were analyzed before and after consolidation. The results indicate that land consolidation significantly increased parcel size and contributed to the use of stronger, more modern machinery. Additionally, thematic maps were utilized to visually support the spatial aspects of consolidation, although no GIS-based quantitative analysis was performed. These findings highlight the importance of aligning land consolidation policies with mechanization strategies to foster more sustainable and efficient agricultural systems. Full article

Multi-machine collaborative navigation in agricultural machinery can significantly improve field operation efficiency. Most existing multi-machine collaborative navigation systems rely on satellite navigation systems, which is costly and cannot meet the obstacle avoidance needs of field operations. In this paper, a real-time tractor recognition and positioning method in fields based on machine vision was proposed. First, we collected tractor images, annotated them, and constructed a tractor dataset. Second, we implemented lightweight improvements to the YOLOv4 algorithm, incorporating sparse training, channel pruning, layer pruning, and knowledge distillation fine-tuning based on the baseline model training. The test results of the lightweight model show that the model size was reduced by 98.73%, the recognition speed increased by 43.74%, and the recognition accuracy remains largely comparable to that of the baseline high-precision model. Then, we proposed a tractor positioning method based on an RGB-D camera. Finally, we established a field vehicle recognition and positioning experimental platform and designed a test plan. The results indicate that when IYO-RGBD recognized and positioned the leader tractor within a 10 m range, the root mean square (RMS) of longitudinal and lateral errors during straight-line travel were 0.0687 m and 0.025 m, respectively. During S-curve travel, the RMS values of longitudinal and lateral errors were 0.1101 m and 0.0481 m, respectively. IYO-RGBD can meet the accuracy requirements for recognizing and positioning the leader tractor by the follower tractor in practical autonomous following field operations. Our research outcomes can provide a new solution and certain technical references for visual navigation in multi-machine collaborative field operations of agricultural machinery. Full article

The mechanized transplanting of sweet potato slips onto mulched raised beds in China’s Huang-Huai-Hai region faces significant challenges due to fragmented smallholder farms and the specific agronomic requirement of “boat-shaped” horizontal planting. To address this gap, this study aimed to develop a compact, cost-effective transplanter that meets the “boat-shaped” planting agronomy and adapts to small plots. We designed the 2CGX-1 mini wheel-driven transplanter coupled with a tractor. This machine features a compact chassis (<1.5 m length) for enhanced maneuverability on small plots, a novel five-bar taking-planting mechanism optimized for boat-shaped placement (achieving a stem-soil angle of 56.2° and planting depth of 110 mm), and an integrated spring buffer system. Transmission design ensures precise synchronization between the dual-chain seedling feeding mechanism and planting actions, allowing plant spacing adjustment from 18 to 30 cm. Coupled Adams–EDEM simulations demonstrated that the buffer system reduces maximum resistance on the clip fingers by 37.8% when encountering obstacles. Field validation under optimal parameters (0.55 km/h operating speed, 30 plants/min transplanting frequency) showed high consistency: average planting depth 101.3 mm (SD 1.38), plant spacing 330.3 mm (SD 11.24), seedling length under the film 185 mm (SD 3.65), and stem-soil angle 47.9° (SD 3.41), with qualification rates exceeding 91.9% for all key parameters except submerged length (82.5%). Compared with manual planting (≤0.1 ha/day per person, labor cost > ¥800/ha), this transplanter achieves a daily operational efficiency of ~0.35 ha/day (calculated by 0.55 km/h speed × 0.8 m working width × 8 h daily working time). Meanwhile, the consistency of its key planting indicators and the planting qualification rate are significantly superior to those of manual planting, while improving operational quality and significantly reducing labor cost input. Deviations in individual indicators mainly stem from planting positioning deviations induced by terrain undulations in hilly test areas, and sweet potato seedlings’ tendency to fall off during clamping due to mechanical vibration. However, these errors are within the acceptable agricultural operation range and do not compromise the machine’s overall compliance with agronomic requirements. The transplanter effectively meets agronomic requirements while offering a cost-effective, adapted solution for small-scale sweet potato production systems, significantly advancing mechanization capabilities for mulched cultivation. Full article

Artificial intelligence (AI) is driving the evolution of autonomous agriculture towards multi-agent collaborative control, breaking through the limitations of traditional isolated automation. Although existing research has focused on hierarchical control and perception-decision-making technologies for agricultural machinery, the overall integration of these elements in building a resilient physical perception collaborative system is still insufficient. This paper systematically reviews the progress of AI-driven tractor-implement cooperative control from 2018 to 2025, focusing on four major technical pillars: (1) perception-decision-execution hierarchical architecture, (2) distributed multi-agent collaborative framework, (3) physical perception modeling and adaptive control, and (4) staged operation applications (such as collaborative harvesting). The research reveals core challenges such as real-time collaborative planning, perception robustness under environmental disturbances, and collaborative control and safety assurance under operational disturbances. To this end, three solutions are proposed: an AI framework for formalizing agronomic constraints and mechanical dynamics; a disturbance-resistant adaptive tractor-implement cooperative control strategy; and a real-time collaborative ecosystem integrating neuromorphic computing and FarmOS. Finally, a research roadmap is summarized with agronomic constraint reinforcement learning, self-reconfigurable collaboration, and biomechanical mechatronic systems as the core. By integrating the scattered progress in AI, robotics and agronomy, we provide theoretical foundation and practical guidance for scalable and sustainable autonomous farm systems. Full article

This study focuses on the development and optimization of a Clutchless Automated-Manual Transmission (CLAMT) system for tractors, aiming to enhance performance and efficiency across diverse operating conditions. It explores the use of a dog clutch mechanism as a simpler, robust alternative to traditional synchronizers. The main objective is to replace complex transmission setups—often requiring up to 32 gear ratios—with a system that operates efficiently using only two gears, without sacrificing versatility. Smooth gear engagement, even under varying loads and terrains, is a key challenge addressed. To ensure this, a Vehicle Management Unit (VMU) manages gear shifts and actively synchronizes speeds. The system leverages steady torque delivery through control algorithms and modern hybrid/electric powertrain capabilities. Two algorithmic approaches are implemented, and their performance is evaluated through empirical testing. Results show improvements in system simplicity, transmission reliability, and overall operational efficiency. The proposed approach offers valuable insights for future agricultural drivetrains, highlighting the potential of dog clutch-based architectures in reducing mechanical complexity while maintaining functional performance. Full article