Waste cooking oil biodiesel alters combustion pathways to enhance volatile organic compound emissions and reduce intermediate/semi -volatile organic compounds in agricultural machinery.

Event triggered disturbance observer based nonlinear MPC for agricultural machinery trajectory tracking control

Introduction. The efficiency of grain harvesting largely depends on the performance of the combine harvester's cleaning system, particularly the upper sieve extension. This component plays a key role in separating grain from various impurities. Poor cleaning efficiency leads to increased grain losses and reduced product quality. Therefore, studying and improving the operation of the upper sieve extension is an important direction for enhancing the overall efficiency of agricultural machinery. Materials and Methods: The study analyzed the operation of the upper sieve extension from both mechanical and mathematical perspectives. The movement of the grain mixture along the extension, its stratification, and the interactions of particles were investigated. The motion patterns of free grains and unthreshed ears, as well as their interactions with the sieve surface, were examined. Using average operational parameters and empirical coefficients, a mathematical model describing the cleaning process was developed. Results: The model allowed deriving analytical relationships that describe the distribution of free grains and unthreshed ears. In particular, the probabilities of grains being directed to the ear auger or ending up in straw residues were determined. Graphical representations visualized the main patterns of the separation process. Calculations showed that the proportion of free grains and unthreshed ears reaching the ear auger ranged from 9.9% to 13.5%, while losses to straw residues varied from 0.86% to 5.05%. Discussion and Conclusions: The results demonstrate that the effective operation of the upper sieve extension directly influences the performance of the combine harvester’s overall cleaning system. The proposed mathematical model provides a reliable tool for analyzing and optimizing the separation process, reducing grain losses, and improving harvesting quality. Future Research and Implications: Future studies should focus on experimental validation of the model under real field conditions, as well as on optimizing the design and operational parameters of the sieve extension. This will enhance the efficiency of agricultural machinery and contribute to more rational use of resources.

Rolling bearings in agricultural machinery operate under pronounced operating-condition shifts, such as speed and load fluctuations and contamination-related noise, which often induce a distribution mismatch between training and deployment signals. This work studies bearing fault diagnosis under a source-only, single-source domain generalization (DG) setting, where the model is trained and selected using only source-domain data, and samples from the target operating condition or target dataset are not used for training, validation, hyperparameter selection, band-pass selection, or early stopping. We formulate cross-condition robustness as a semantic consistency problem between two complementary representations: an analytic mechanism-oriented representation emphasizing impact-related resonance demodulation, and a data-driven temporal representation learned from raw waveforms. A dual-path framework is developed accordingly. The analytic path learns a differentiable soft band-pass mask to localize an informative resonance band and constructs an impulse-oriented descriptor from statistics of the band-pass signal, its Hilbert envelope, and squared-envelope energy. The temporal path encodes normalized raw segments using a lightweight dilated one-dimensional convolutional network with temporal-attention pooling. The two embeddings are fused by a sample-wise gate, with an entropy penalty used to discourage near-uniform averaging. This design aims to improve cross-condition generalization by using the analytic path as a mechanism-oriented semantic anchor, constraining the temporal path through cross-view agreement, and allowing the fused representation to adapt to sample-dependent reliability changes. To reduce representation drift under regime changes, the two views are aligned using a bidirectional InfoNCE objective with a learnable temperature that adapts similarity scaling across operating conditions. A mechanism-critical control is also reported: replacing the analytic path with same-dimensional non-mechanistic features consistently degrades cross-condition performance, indicating that the analytic anchor is not interchangeable with generic auxiliary branches. Experiments on CWRU, SEU, and an agricultural-machinery-relevant test-rig dataset show in-domain accuracies of 99.48%, 98.50%, and 97.53%, respectively. In strict cross-speed evaluation on the test-rig dataset, the method achieves 98.22% accuracy when trained at 1500 r/min and tested at 2000 r/min, and 98.03% accuracy in the reverse setting. In cross-dataset evaluation using the shared normal, inner-race, outer-race, and rolling-element fault classes, the proposed method achieves the best average performance among the evaluated source-only baselines, including generic DG methods and recent bearing-diagnosis generalization methods. These comparisons include DPICEN and a protocol-matched single-source adaptation of FARNet, denoted FARNet-SS, both evaluated without target-domain access during training or model selection. The model remains lightweight, with 0.1348M parameters and 127.11M FLOPs for the neural forward pass.

The development of high-clearance, electrically powered agricultural machinery is essential for improving farming efficiency and sustainability. This study presents the design and development of a high-clearance e-vehicle to support a robotic cotton picker, enabling efficient navigation through densely planted cotton fields. A Python-based computational model was used to determine significant vehicle parameters, including centre of gravity, slope stability, turning dynamics, and moment of inertia. The centre of gravity was located at 585.54 mm longitudinally, 867.47 mm laterally, and 1083.46 mm in height from reference points. Stability analysis revealed maximum upgrade and downgrade slopes of 28.39° and 43.67°, respectively, with critical turning speeds of 24.71 km/h for left turns and 27.26 km/h for right turns. The moment of inertia about the centre of gravity was calculated as 51135.31 kg-mm-s2. Additionally, the vehicle's speed performance was evaluated under different motor speeds and gear settings, with a maximum speed of 12.19 km/h achieved in motor speed mode 1 at gear 2. The results confirm that the e-vehicle maintains stability while manoeuvring slopes and turns, effectively operating in fields with row spacings of 900 mm and 675 mm. This study contributes to the advancement of energy-efficient agricultural machinery, addressing challenges in navigating complex crop geometries and supporting the transition to sustainable precision farming.

Numerous long-term studies in our country and abroad have shown a significant decrease in crop yields as a result of soil compaction caused by the running systems of tractors and mobile agricultural machinery. Soil compaction also leads to increased costs for soil cultivation. One way to reduce the negative impact of running systems on the soil is to create highly elastic tracked propulsion systems. There are two options for pneumatic tracks: tracks with longitudinal pneumatic tracks relative to the track axis and tracks with transverse pneumatic tracks on a metal traction chain.During comparative tests of propulsors with pneumatic and metal tracks, it was found that installing pneumatic tracks instead of metal tracks equalizes the distribution of specific pressures along the length of the support surface. At the same time, the coefficient of unevenness of the pressure distribution is 1.6-1.9 times lower for the propulsor with a pneumatic track.

This study aimed to determine and analyze the design loads required for the electrification of a 55‑kW agricultural tractor through field experiments. A measurement system was installed to record data from the engine, driving axles, power take‑off (PTO), and hydraulic pump during plow tillage, rotary tillage, and driving operation in a silt loam paddy field. The study specifically focused on power requirement analysis, load duration distribution (LDD), and rainflow counting (RFC)–based load spectrum generation for durability assessment of the electric tractor powertrain. Plow tillage imposed the highest loads, with total power peaking at 52.6 kW (95% of rated power) dominated by axle torque, while rotary tillage was PTO‑driven and driving operation showed low average loads with intermittent traction peaks. Compared with a previously studied 78‑kW tractor, the 55‑kW tractor exhibited lower overall power requirement and a more traction‑balanced distribution, while hydraulic requirements remained minimal. LDD and RFC analyses revealed that a few load cases and low‑amplitude cycles dominate the operating profile, and critical high‑load cycles occur primarily during tillage. These findings provide essential design data for electric powertrain components and establish a systematic measurement‑to‑spectrum methodology for deriving design loads for agricultural machinery, supporting the future development of utility electric tractors and durability‑driven powertrain design.

Stagnant oxidative potential in declining agricultural fleets: Evidence from key particulate matter composition despite fleet renewal.

Safety pictograms on agricultural machinery: A survey among Turkish users to investigate comprehension and comparison with previous studies.

Agricultural machinery-related vascular injuries represent a severe occupational hazard with potential for permanent disability and limb loss. A case series of seven consecutive patients with upper limb vascular injuries from agricultural machinery was included. All seven patients (100% male, mean age 42 ± 8 years) presented with combined neurovascular injuries. Brachial artery involvement occurred in 85.7% (6/7) of cases with universal median nerve injury. Hay balers were the most common causative machinery (42.9%), followed by rice harvesters (28.6%). Interpositional vein grafting was required in 71.4% (5/7) of cases. Despite delayed presentation and injury severity (71.4% with sensorimotor deficits), 100% limb salvage was achieved with restoration of arterial flow in all patients. Complex agricultural machine-cut injuries involving major upper limb vessels demonstrate excellent limb salvage outcomes when managed at specialised vascular centres, even with delayed presentation up to 24 hours. The high prevalence of preventable human and mechanical factors underscores the urgent need for mandatory safety training and equipment modification in agricultural settings.

• Inductive heating separates rubber-metal bonds in seconds instead of minutes, drastically reducing thermal load on the rubber. • Localized heat targets the adhesive layers while preserving bulk rubber mechanical properties. • Recovered steel inserts retain hardness and microstructure, enabling direct reuse in new components. • Process cuts separation energy by more than one order of magnitude compared to oven heating and enables higher-quality material recycling routes for the rubber fraction. Rubber-metal composites, such as rubber tracks and pads for construction and agricultural machinery, rely on robust adhesion to withstand mechanical stresses and harsh environmental conditions. However, their effective recycling is significantly hindered by the difficulty of separating strongly bonded components. Conventional recycling techniques, including mechanical milling, waterjet separation, and standard thermal processing, often result in excessive energy consumption, long processing times, or material degradation. Compared to inductive heating, these methods typically require significantly more energy, leading to higher associated emissions. This study investigates a new recycling approach based on localized inductive thermal treatment, targeting specifically the adhesive bonding layers. Specimens of rubber-metal composites were thermally treated at temperatures between 150 °C and 300 °C for durations ranging from 1 to 10 min to assess their separation efficiency and subsequent changes in mechanical and adhesive properties. Results indicated that the adhesive layer could be effectively degraded at temperatures significantly below the decomposition point of rubber, enabling clean separation of metal components while preserving the integrity of the rubber material. The metal inserts remained unaffected, allowing direct reuse. This targeted thermal separation method demonstrates substantial potential for advancing a circular economy for rubber-metal composites. By enabling clean separation, the process allows direct reuse of the metal inserts and preserves the rubber for subsequent material‑recycling routes, such as high‑quality regrinding instead of destructive thermal treatment. This approach significantly reduces energy requirements and associated CO 2 emissions compared to conventional methods, offering a viable path for resource preservation and reduced landfilling.

Optimizing a new robust location-pricing problem in agricultural economy by customized bi-level algorithm

Hilly and mountainous regions are strategically vital for national food security. However, due to complex topographical constraints, their agricultural mechanization levels remain severely underdeveloped. This creates a critical bottleneck in agricultural modernization. Conventional agricultural machinery faces multifaceted challenges in terrain adaptability, operational efficiency, and safety assurance when deployed in these environments, necessitating the urgent development of specialized chassis with enhanced trafficability and stability. Following a systematic literature review of key technologies, including power transmission systems, traveling and support mechanisms, leveling control, and navigation tracking, this study reveals that current chassis technology is advancing toward intelligentization, enhanced efficiency, environmental sustainability, and improved terrain adaptability. The analysis demonstrates that multiple technological pathways, encompassing mechanical, hydraulic, and electric drives, are exhibiting convergent and complementary trends. Future research and development should prioritize the following areas: integrated intelligent coordinated control architectures, green and sustainable power system innovation, modular and reconfigurable platform design, and the establishment of collaborative frameworks among industry, academia, research institutions, and application sectors. Comprehensive standardization systems are also needed. These strategic directions are essential for comprehensively elevating agricultural mechanization levels and maximizing developmental benefits in hilly and mountainous regions.

Accurate field boundary delineation is critical for accurate modelling on crop yields and for precision agriculture (PA), enabling site-specific management to optimize resource use and crop productivity. Traditional boundary mapping methods, such as manual digitization and semi-automated extraction from farm machinery, are labor-intensive and challenging to apply at large scales. Advances in high-resolution land cover data and satellite imagery offer scalable solutions for automated field boundary extraction. In this study, we propose a fully automated workflow that integrates a pre-trained foundation model, the Segment Anything Model - SAM [1] with time-series Sentinel-2 imagery. Seasonal composites of Red, Green, and Blue bands were generated at different phenological stages to support segmentation. The method was applied across over 32 million hectares (79 million acres) of cultivated land in the Canadian Prairies, achieving an intersection-over-union (IoU) accuracy of 0.86 compared to manual segmentation. The workflow consists of four main steps: (1) setting the python working environment, (2) seasonal image acquisition and preprocessing using Google Earth Engine via Python API; (3) field boundary segmentation using SAM; and (4) post-processing and feature cleaning using ArcGIS Pro. This approach demonstrates a scalable, efficient solution for large-scale field boundary mapping to support PA applications.•Integrates a foundation segmentation model (SAM) with Sentinel-2 seasonal imagery•Demonstrates high-accuracy, large-scale automated field boundary delineation•Provides a reproducible workflow adaptable to other regions and datasets.

Farm mechanization is expanding in Bangladesh, yet smallholders continue to face constraints such as fragmented landholdings, high machinery costs, limited access to custom hiring services, and insufficient training. This study examines these challenges using secondary data from 5053 households in the Eastern Gangetic Plains collected under the Sustainable and Resilient Farming Systems Intensification project. Although the dataset emphasizes conservation agriculture and contains few machine-specific variables, it remains appropriate for assessing technology adoption in smallholder systems. A subsample of 1761 farmers from Rajshahi and Rangpur districts of Bangladesh was analyzed to assess the joint adoption of four modern machines: the rotavator, laser land leveler, happy seeder, and combine harvester. Unlike studies that consider single technologies, this research investigates how farmers’ adoption decisions interact. The descriptive statistics reveal that 56.8 % of households adopted the rotavator, whereas adoption of the other machines remained below 2.5 %. Multivariate Probit model identified that household size, family labor, off-farm income, machinery ownership, and institutional support generally encouraged adoption, while age, education, and limited familiarity with machinery reduced uptake for some technologies. Correlation results reveal both complementarities and substitution among machines. The findings underscore the need for targeted financial support, training, custom hiring services, and awareness programs to promote inclusive, region-appropriate mechanization. The study adds new empirical evidence by jointly analyzing multiple mechanization choices and clarifying the behavioral and structural conditions needed for sustainable agricultural intensification in smallholder systems.

Additive Manufacturing and Robotics-based Post-processing for Lightweight Agricultural Machinery Production: Optical in-situ Detection and AI-driven Optimization

CramIS: a blockchain-based integrated system for cross-regional scheduling of agricultural machinery

Farmers’ perceptions and government-farmer value co-creation: The roles of artificial intelligence adoption and government support

ABSTRACT BACKGROUND: The main feature of improving the performance of diesel engines is the analytical evaluation method, as well as the analysis of operation in field conditions when using various types of fuel. The studied agricultural machinery operates at different speed load modes of their engines. Mathematical modeling of working processes that link output variables with input effects allows us to determine the emissions of harmful substances from automotive and tractor engines under operating conditions. AIM: compilation of a mathematical model of the relationship between input and output coordinates for analyzing the dynamic properties of fuel supply. METHODS: Experimental studies were carried out in accordance with the requirements of GOST 14846-2020 (Automobile engines. Bench test methods) and GOST 18509-88 (Tractor and combine diesel engines. Bench test methods). At the Irkutsk State Agrarian University named after A.A. The main operational factors influencing the power, environmental and economic performance of engines running on water-fuel emulsion were analyzed at the Ezhevsky Institute of Petroleum Engineering and the Buryat State University named after D. Banzarov. During the experiments in the field, we established the following factors: a decrease in torque, a decrease in diesel power due to fluctuations in the resistance moment on the engine by an average of about 10% for each of the above indicators. This indicates a violation of the cyclic fuel supply, mixture formation and combustion processes, which negatively affected the technical, economic and environmental indicators. RESULTS: Experimental data were obtained for various indicator powers corresponding to fluctuations in the cyclic fuel supply for operating conditions. Under these conditions, soot emissions fluctuated from 0.11 to 0.32; NOx from 2.94 to 5.62, and with the addition of a 30% methanol additive, from 0.02 to 0.32; NOx from 2.5 to 2.2 times. The calculated indicators showed satisfactory agreement with theoretical studies, within 5%. Therefore, according to the proposed method, it is possible to determine the emissions of harmful substances of diesel engines under operating conditions. An analysis of the dynamic properties of fuel supply under operating conditions is carried out and equations for the relationships of output coordinates with input ones are presented. Under field experiment conditions, a decrease in torque and diesel power with significant fluctuations in crankshaft speed was established. Emissions of harmful substances of diesel engines were determined by calculation. CONCLUSIONS: An integrated approach to the study of the influence of alternative fuels and operating conditions on emissions of harmful substances in diesel engines is key to developing effective solutions to reduce their negative impact on the environment.

ABSTRACT Using the potential spatiotemporal features in trajectory data to identify the operation mode of agricultural machinery is an important basic task in the field of precision agriculture. However, existing methods for identifying the operation mode of agricultural machinery trajectories fail to model the dependencies of agricultural machinery trajectories from different ranges, and the imbalanced data distributions of different categories of agricultural machinery trajectory data produce identification bias. To overcome the above defects, this paper proposes a multi-range spatiotemporal information capture network based on amplified feature deformation (VRPNet). First, to solve the identification bias caused by the imbalanced data distribution of the model, we design a data balancing module based on a factorized variational autoencoder (FVAE), which independently factors and encodes the features of minority class trajectory samples and then decodes and generates quasi-trajectories similar to the original trajectory points to balance the data distributions of different categories. Second, to explore the potential spatiotemporal information of trajectories fully, we propose a trajectory information multi-scale amplification module, which applies kinematic methods and statistical methods to extract multi-scale features of agricultural machinery trajectories in different spatiotemporal ranges to mine the inherent information of agricultural machinery trajectories. Finally, to comprehensively model the dependencies of agricultural machinery trajectories, we propose a spatial correlation capture module based on a low-rank approximation matrix (LRSC) and a dynamic multi-path convolution bottleneck based on feature deformation (FD-DPC) to assemble into a trajectory context encoder to explore the feature interactions of agricultural machinery trajectories in different ranges. To verify the effectiveness of the method, we conducted experiments on paddy and wheat trajectory public datasets. The results show that the accuracies of VRPNet on the paddy and wheat trajectory datasets are 94.20% and 96.06%, respectively, which are improvements of 1.72% and 1.92%, respectively, over those of the currently widely used models.