Search Results (333)

This review explicitly focuses on agricultural attachments and executing components that interact directly with soil and crops, rather than the tractor vehicle itself. Operating within complex and variable farmland media environments, the key components of agricultural machinery have long been constrained by bottlenecks such as high-energy draught resistance, severe solid–liquid interfacial adhesion, and intense abrasive wear. Bionic functional surfaces, based on the coupling of micro-geometric morphology and surface-interface physical chemistry, provide a scientific approach to overcoming traditional tribological limitations by reconstructing the contact mechanics and fluid dynamics boundaries at the interface. This paper presents a comprehensive review of the latest research progress regarding bionic functional surfaces in the fields of friction reduction, wear resistance, and anti-adhesion in agricultural machinery. The article systematically categorises typical biological prototypes, such as soil-burrowing animals, aquatic organisms, and plant leaves, alongside their multidimensional feature extraction methods. It provides an in-depth analysis of core interaction mechanisms, ranging from static air cushion effects and dynamic wetting evolution to active electro-osmotic soil detachment, interfacial stress redistribution, and microscopic wear debris capture. Furthermore, it evaluates the efficacy of cross-scale coupled numerical simulation technologies in resolving interfacial interactions. At the engineering application level, this review extensively discusses the field performance of bionic structures in typical operational scenarios, including draught reduction in tillage and land preparation, blockage prevention in seed-metering channels, and low-damage harvesting in agricultural machinery. Finally, countermeasures are proposed to address the fatigue degradation of bionic surfaces under alternating field loads and the barriers to the large-scale fabrication of large-sized components. The paper further highlights the development trend towards the deep integration of bionic tribology with digital twins and intelligent wear-state perception technologies, aiming to provide systematic underlying theoretical and technical references for the research and development of the next generation of intelligent agricultural equipment characterised by low energy consumption and a prolonged service life. Full article

Preparing seedbed operations in clay soils is quite difficult. As moisture content increases in these soils, the soil attains a plastic consistency, adheres to tillage implements, and, when it dries out, large clods form during tillage. Furthermore, problems with seed germination occur in clay soils with poor drainage. This study aimed to design and manufacture a sowing machine that prepares seedbeds on ridges in clay soils. This machine consists of a lister, a rotary cultivator, and a pneumatic sowing unit. A ridge was created using the lister to eliminate seed germination problems. Due to the clayey nature of the soil, only the ridge was tilled with a rotary cultivator to a width of 20 cm and a depth of 10 cm to facilitate soil fragmentation. Soybean (Glycine max.) cultivation was carried out. Field trials were conducted on plowed plots (plowed in autumn) and on unplowed plots, and the system was compared with the conventional sowing method (plowing + disc harrowing + sowing machine). The field emergence rate was 73.04% in the plots tilled in autumn with the prototype machine and 76.05% in the plots without tillage when tilled with the prototype machine. This value remained at 31.43% under conventional tillage. Fuel consumption in conventional sowing was 2.51 times higher than sowing with the prototype machine. In addition, the prototype machine saved 69.56% of compared to conventional tillage. Full article

This study conducted a comprehensive comparative assessment of the effects of site-specific tillage (SST) and uniform-depth tillage (UDT) on soil physical properties, fuel consumption and CO₂ emissions. The aim was to determine whether using different tillage depths based on variability in soil properties associated with apparent electrical conductivity (ECa) could improve the efficiency of soil management, which would be beneficial for the soil and the environment. Field experiments were conducted using a multifunctional cultivator with three SST depths (10, 14 and 18 cm), which were distributed over variable soil management zones. UDT was applied at a constant depth of 15 cm. The results of the experimental studies showed that SST affected the physical properties of the soil in different management zones with different tillage depths. Reduced tillage depths ensured adequate soil physical properties in areas of lower soil resistance, while deeper tillage was only effective in areas of higher soil resistance. Soil density in the top 0–10 cm soil layer varied within the plant-friendly range of 1.2–1.3 g cm⁻¹ in the region and 1.4–1.5 g cm⁻¹ in the deeper 10–20 cm layer, while total soil porosity responses differed in different management zones. UDT reduced total soil porosity by 3.17% and 3.5% in the top and deeper soil layers, respectively. Changes in total soil porosity due to SST in the 0–10 cm layer depended on tillage depth: it decreased slightly at 10 cm, remained unchanged at 14 cm and increased slightly at 18 cm. In addition, SST reduced fuel consumption and associated CO₂ emissions compared with UDT, with environmental impact related to fuel combustion decreasing by approximately 14%. These findings demonstrate that site-specific tillage, when guided by soil variability, can improve the efficiency and environmental sustainability of tillage operations without compromising soil physical properties. Full article

Artificial intelligence is transforming agriculture from a mechanized, labor-intensive sector into a data-driven, perception-enabled, and increasingly autonomous production system. In this review, AI serves as an umbrella term encompassing machine learning, computer vision, and robotic control, among other technologies. We synthesize recent advances across the tillage–sowing–management–harvesting (TSMH) workflow, covering intelligent tillage, precision sowing, field management, and robotic harvesting. The literature shows that AI has significantly improved agricultural perception, prediction, and task-level decision-making. However, large-scale adoption remains constrained by data heterogeneity, limited cross-scene generalization, environmental uncertainty, and insufficient integration across operational stages. Future progress will depend on multimodal data fusion, lightweight and interpretable models, cloud-edge collaboration, and full-chain decision architectures. By framing current research within the TSMH pipeline, this review highlights both technical advances and the critical bottlenecks that must be addressed to move smart agriculture from stage-specific intelligence toward system-level autonomy. Representative studies indicate that AI models can improve soil-property prediction and reduce sowing miss-detection rates to below 3% under controlled or bench-top conditions. However, field deployment may be affected by environmental variability, including illumination changes, dust, vibration, occlusion, and hardware constraints. These limitations highlight the need for robust and edge-compatible architectures. Full article

Biofertilizers, or microbial fertilizers, are designed to restore soil biological functions that have been degraded by long-term reliance on chemical fertilizers. Although their availability in the United States has increased, adoption among farmers remains limited. To assess the current adoption patterns and the factors influencing farmers’ decisions, we conducted a survey of 1119 producers across Minnesota, Nebraska, North Dakota, and South Dakota in 2022. Results show that only 15% of farmers currently use biofertilizers, yet 88% of adopters intend to continue using them. Among non-adopters, 20% expressed interest in trying biofertilizers within the next 3 years, indicating substantial potential for market growth. Regression analysis reveals that younger farmers, larger-scale operations, and those who place greater importance on workshops as a learning platform are more likely to adopt biofertilizers. Adoption is also strongly associated with the use of complementary conservation practices, including cover crops, no tillage, and soil nutrient testing. These findings suggest that biofertilizer use is embedded within broader conservation management strategies rather than being an isolated decision. To support wider adoption, we recommend integrating biofertilizers into existing cost-share programs, such as the Conservation Stewardship Program, and expanding extension efforts through workshops and field demonstrations to provide practical, experience-based learning opportunities. Full article

Removing subsoil compaction caused by agricultural traffic is energy-demanding and therefore expensive. Experimental work was undertaken on a Typic Argiudoll to quantify the energy required to remove subsoil compaction and determine the associated effects on yield and profitability. The following treatments were compared: (T1) soil under no-tillage for 20 years, which was used as a control; (T2) deep tillage performed with a paratill on soil that had had no-tillage in the 20 years prior to this study; and (T3) deep tillage performed with a chisel plow on soil that had had no-tillage in the 20 years prior to this study. The paratill and chisel plow were operated at depths of 400 and 250 mm, respectively, and the energy required to perform both (deep tillage) operations was determined. Soil cone index and maize yield were measured over three growing seasons and compared with T1. Results showed that the effect of deep tillage lasted for two years, after which the soil reconsolidated reaching soil strength values comparable to their pre-treatment condition. The reconsolidation of tilled soil over this period was due to both natural settlement and post-treatment (random) machinery traffic. The paratill treatment significantly increased maize yield compared with no-tillage, which therefore improved crop gross margins across all three seasons. The chisel plow treatment increased crop yields compared with no-tillage, but yield differences were small and therefore the average crop gross margins were not significantly different. Deep tillage with paratill costed US$76 per ha and generated an average gross income of US$1134 per ha, whereas deep tillage with chisel plow costed US$29 per ha and generated an average gross income of US$1027 per ha. These results compared with an average gross income of US$1001 per ha obtained under no-tillage. If (strategic) deep tillage needs to be performed on long-term no-tillage soil to remediate compaction, paratill may be preferred to chisel plow, but care should be exercised not to re-compact the soil after the operation has been performed. One effective way to do this is by implementing controlled traffic. Full article

This study analyzes whole-body vibration (WBV) exposure of an agricultural tractor operator during three different primary tillage systems: Standard Tillage (ST), Conservation Tillage Deep (CTD), and Conservation Tillage Shallow (CTS). Measurements were conducted in accordance with ISO 2631-1 and ISO 2631-4 along three orthogonal axes (x, y and z) at the operator’s seat. Descriptive and inferential statistical analyses indicate that while none of the mean vibration values exceeded the regulatory limit value of 1.15 m/s² defined in Directive 2002/44/EC, several measurements—particularly in the y-axis during ST (0.715 m/s²)—surpassed the exposure action value of 0.5 m/s². These findings suggest that prolonged daily exposure under similar operational conditions may pose long-term health risks for tractor operators. The highest mean WBV values were recorded in the x- and y-axes during CTS (0.354 m/s² and 0.446 m/s², respectively), whereas the z-axis exhibited the highest values during ST (0.426 m/s²). Conservation Tillage Deep (CTD) demonstrated the most favorable vibration profile in the vertical axis (0.344 m/s²), indicating its potential dual benefit for soil structure preservation and operator ergonomics. Although all measured values remained below the regulatory limit, the frequent exceedance of the action value underscores the importance of exposure time management, regular maintenance of suspension systems, and implement selection as practical mitigation strategies. This comparative assessment provides baseline WBV data for reduced-tillage systems on hydromorphic soils and offers axis-specific guidance for optimizing operator comfort in sustainable mechanization practices. Full article

Soil adhesion and abrasive wear severely degrade the performance and service life of soil-covering rollers in no-tillage seeders, particularly in the heavy clay black soil regions of Northeast China. To address the critical issues of soil adhesion and wear on soil-covering rollers used in no-tillage seeders within black soil regions, this study presents a surface engineering strategy that integrates a bionic micro-texture with a functional composite coating. Inspired by the crescent-shaped pits on the body surface of Procambarus clarkii, a bionic texture was designed and combined with a PTFE/PDMS/TiO₂ composite coating. Key parameters were optimized using response surface methodology, yielding a TiO₂ mass fraction of 6%, coating thickness of 40 μm, remaining texture depth of 50 μm, and texture spacing of 250 μm. A prototype was fabricated and evaluated through orthogonal field experiments in two distinct soil environments. In clay soil (15–25% moisture content), soil moisture and vertical load significantly influenced anti-adhesion performance, with recommended operating parameters of 600 N vertical load and a speed range of 10.8–14.4 km·h⁻¹. In sandy soil (8–18% moisture content), vertical load and operating speed had significant effects on wear resistance, with optimal parameters identified as 600 N vertical load and 10.8 km·h⁻¹. Verification tests confirmed stable low-adhesion and low-wear performance under varying moisture conditions. Compared to conventional and PTFE-coated rollers, the bionic roller reduced soil adhesion by 82.62% and 74.02%, respectively, in high-moisture clay soil, and reduced wear loss by 36.81% and 28.97%, respectively, in dry sandy soil. These results demonstrate that the synergistic “structure–material” design, which leverages stress dispersion and storage from the bionic texture alongside low surface energy and enhanced wear resistance from the composite coating, offers a promising approach for improving the durability and performance of soil-engaging agricultural components. Full article

Empirical parameter settings in steam-based soil disinfestation for Panax notoginseng (a valuable medicinal plant) often hinder the simultaneous optimization of pathogen control and energy efficiency. To address this limitation, this study aims to develop a parameter regulation framework that integrates multi-output regression with scenario-oriented intelligent decision-making. Initially, a comprehensive dataset comprising critical parameters—steam pressure (P_steam), soil compaction (C_soil), and heating time (t_heat)—was established. A random search (RS) hyperparameter optimization scheme was employed to comparatively evaluate the multi-output predictive performance of Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron (MLP) for the joint estimation of soil temperature (T_soil) and root-rot pathogen kill rate (Kill_rate). Subsequently, by integrating total energy consumption (E_total) and operating electricity cost models, a constrained search algorithm was implemented to develop three objective-oriented decision modes: “maximize Kill_rate”, “minimize C_electricity”, and “maximize Efficiency”. Results demonstrate that the RS-optimized SVR yielded superior multi-output performance, achieving R² of 0.968 for T_soil (MAE = 2.44 °C) and 0.808 for Kill_rate (MAE = 7.85%). Compared to conventional empirical configurations, the proposed decision modes exhibited significant advantages across diverse scenarios. In the “maximize Kill_rate” mode, dynamic extensions of t_heat facilitated theoretical complete inactivation even under challenging heating conditions, effectively eliminating disinfection “blind spots” inherent in fixed-duration strategies. Under the “minimize C_electricity” mode, precise regulation of P_steam reduced operational electricity costs by 18.2% while satisfying the constraint of Kill_rate ≥ 95%. Furthermore, the “maximize Efficiency” mode identified an optimal operating point at C_soil = 64 kPa (P_steam = 0.4 MPa, t_heat = 13 min), thereby mitigating performance degradation associated with excessive tillage or high media rigidity and achieving an optimized cost–benefit ratio. By synthesizing high-fidelity multi-output regression with a flexible multi-mode decision-making framework, this study provides an intelligent solution for soil disinfestation in protected agriculture, facilitating the coordinated optimization of phytosanitary efficacy, energy expenditure, and economic viability. Full article

Rising temperatures and increasing evaporative demand accelerate soil moisture loss (SML) during the sowing-to-emergence phase of cotton (Gossypium hirsutum L.), constraining crop establishment under water-limited Mediterranean conditions. Conventional tillage (CT) involves intensive tillage operations with higher fuel and energy requirements, whereas strip tillage (ST) limits tillage to the crop row while preserving inter-row residues. This study evaluated ST and CT across two consecutive growing seasons (2024 and 2025) under a wheat–cotton rotation system. A field experiment was conducted using a replicated design (n = 8), in which emergence parameters, SML (0–10 cm), yield, and fuel-derived energy use and CO₂ emissions were quantified. SML was significantly lower under ST than CT (43% in 2024 and 52% in 2025; p < 0.001), leading to earlier emergence (0.98–1.17 days) and higher emergence rate index (ERI) values. Cotton yield was slightly higher under CT (3–4%); however, this difference, although statistically significant (p = 0.001), remained limited and consistent across years. In contrast, ST resulted in a 66–69% reduction in operational fuel use, with proportional reductions in energy use and CO₂ emissions on an area basis. Yield-scaled indicators, defined as energy use (MJ kg⁻¹) and CO₂ emissions (kg CO₂ kg⁻¹) per unit yield, further revealed substantially greater resource-use efficiency under ST compared with CT. These findings demonstrate that strip tillage enhances hydrothermal conditions during crop establishment while markedly reducing energy demand and carbon intensity, providing a resource-efficient mechanization strategy for cotton production under increasing climatic stress. Full article

Strip tillage combined with plastic film mulching is widely used to improve moisture conservation and crop productivity in arid and semi-arid regions; however, the quality of a prepared strip strongly depends on the secondary leveling and compaction of the loosened soil. The aim of this study was to substantiate the design and operating parameters of a skid-type leveler–compactor integrated into a combined machine for one-pass pre-sowing soil preparation of rainfed land with the simultaneous laying of drip irrigation hoses and plastic film for melon crops. The research combined theoretical modeling of soil–tool interaction with single-factor and multifactor field experiments and a two-year agronomic evaluation under farmer field conditions. The optimal parameter ranges were 24.0–26.0° for the lower compacting element, 151–155° for the upper leveling part, and 24.3–27.0 cm for the tool height. Under these conditions, the proposed system ensured a soil fraction < 25 mm of 87.2%, surface irregularities of 1.34 cm, and reduced fuel consumption, from 38.6 to 26.3 kg ha⁻¹, compared with the conventional method. The field emergence increased to 91.6%, the total yield reached 50.7 t ha⁻¹, and the water use efficiency increased to 17.4–18.9 kg m⁻³. The results demonstrate that optimization of the leveler–compactor significantly improves strip quality, agronomic performance, and resource use efficiency in melon production under plastic film. Full article

Lime and gypsum are widely used to correct soil acidity and improve grain yields in Brazilian agricultural systems. However, limited information is available on their effectiveness and application practices in degraded sandy soils typical of older agricultural frontiers, such as those in Rio de Janeiro State. This study evaluated the effects of surface application versus the incorporation of lime and gypsum into the soil through tillage operations on soil chemical properties, nodulation, and grain yield of soybean cultivars grown in low-fertility Fluvisols. The experiment was conducted during the 2021/2022 growing season in Campos dos Goytacazes, Rio de Janeiro, using a strip-plot design with four soybean cultivars and two soil amendment placement strategies: surface application without tillage and incorporation through tillage. Soil chemical attributes, nodulation, nutrient uptake, and yield components were assessed. Incorporated application significantly increased soil pH, reduced Al³⁺ toxicity, and enhanced Ca²⁺, Mg²⁺, P, and K⁺ availability compared to surface application. Nodulation responses varied among cultivars, with incorporated treatments promoting up to 40% greater nodule biomass. Although primary root length was not affected, incorporation stimulated secondary root development and nutrient uptake, leading to approximately 50% higher pod number and grain yield. Overall, incorporating lime and gypsum through soil tillage was more effective than surface application in improving soil fertility, enhancing nodulation, and increasing soybean productivity under the conditions evaluated in this study. These findings suggest that lime and gypsum incorporation can represent an important management strategy for improving soybean production in degraded sandy soils. Full article

Soil health monitoring is critical for the sustainable management of the black soil region, a key resource for global food security. However, traditional field surveys are constrained by high operational costs, limited spatial coverage, and low temporal frequency, making them inadequate for high-resolution and time-sensitive soil monitoring. The recently launched ZY1-02E satellite, equipped with an advanced hyperspectral imager, offers a new potential data source, yet its capability for quantitative soil modelling requires rigorous cross-sensor validation. This study conducts a cross-sensor evaluation of ZY1-02E and its predecessor, ZY1-02D, for mapping soil organic matter (SOM) and soil texture (sand, silt, and clay) in Northeast China. Optimal spectral indices were constructed through exhaustive band combination and correlation screening, and quantitative inversion models were established using a hybrid framework integrating Random Frog feature selection with Gaussian Process Regression (GPR) and Boosting Trees, based on synchronous ground observations. Results demonstrate strong cross-sensor consistency, with spectral indices showing significant linear correlations (${\mathrm{R}}^2>0.65$) between ZY1-02E and ZY1-02D. Furthermore, the quantitative retrieval models applied to ZY1-02E imagery achieved robust performance, with cross-sensor retrieval consistency exceeding ${\mathrm{R}}^2=0.60$ for all parameters and SOM exhibiting the highest agreement (${\mathrm{R}}^2=0.74$). These findings confirm the radiometric stability and algorithm transferability of ZY1-02E, demonstrating its capability to generate soil parameter products comparable to ZY1-02D without extensive model recalibration. The validated interoperability of the twin-satellite constellation substantially enhances temporal observation capacity during the narrow bare-soil window, effectively mitigating cloud-induced data gaps in high-latitude agricultural regions. Importantly, the enhanced monitoring framework provides a scalable technical paradigm for high-frequency hyperspectral soil mapping, offering critical spatial decision support for precision fertilization, soil degradation mitigation, and conservation tillage management in the Mollisol belt. Full article

Topsoil features within a depth of 0–10 cm are vital for making soil management decisions that affect crop production. However, the use of these soil features to predict soil organic carbon (SOC) at 10–20 cm requires further investigation. The study aims to predict SOC at 10–20 cm using total nitrogen (total N), pH, cation exchange capacity (CEC), and SOC at 0–10 cm and select a suitable model for predicting SOC. This study was conducted using data from a long-term tillage, winter wheat (Triticum aestivum L.)-fallow experiment established in autumn 1970. Treatments included moldboard plow, stubble mulch, no-till, and native sod, each replicated three times. Soil samples were collected from each plot at depths of 0–10 cm and 10–20 cm in April of 2010 and 2011. Models were fit using ordinary least squares (OLS), least absolute shrinkage and selection operator (LASSO), random forests, and Bayesian additive regression trees (BART). Using root mean square error (RMSE), SOC was predicted with an accuracy of 1.44 g kg⁻¹ or relative RMSE (rRMSE) of 13.5%. This was achieved with the OLS model that used total N, pH, and CEC as predictors. The good performance of the OLS model over more flexible machine learning approaches suggests that the information predictors provide about the response variable (SOC) is approximately linear. As the agricultural dataset was small (n = 24), the less complex model reduced the chances of overfitting while keeping the variance relatively low. Random forests and BART had an rRMSE greater than 21%. Statistical models could be used to estimate SOC at 10–20 cm and reduce destructive soil analysis methods. Full article

Accurate estimation of axle torque is essential for performance evaluation and energy management of electric tractors. However, direct torque measurement and access to motor controller data are often limited in commercial platforms. This study proposes a machine learning-based framework for predicting axle torque in a commercial electric tractor using field-measured sensor signals. The framework incorporates a horizon-aware architecture to capture the temporal dependencies of dynamic load fluctuations. Field experiments were conducted during plow tillage operation under multiple gear–speed combinations. Several machine learning models (multiple linear regression, multilayer perceptron, and CatBoost) were evaluated for axle torque prediction. The results showed that rear axle torque exhibited a stronger relationship with traction demand under two-wheel-drive operation, resulting in higher prediction accuracy than front axle torque. Among the evaluated models, CatBoost achieved the best overall performance, with an $R^2$ of 0.83 and an RMSE of 189.35 Nm for the rear axle prediction. The proposed framework enables real-time axle torque estimation using commonly available sensor signals and provides a practical alternative to direct torque measurement for onboard load monitoring and energy management in electric tractor systems. Full article