Lycium barbarum L. (L. barbarum) is a continuous-flowering and continuous-fruiting crop with thin and delicate fruit skin, necessitating multiple harvesting rounds. Currently, the harvesting of L. barbarum primarily relies on manual labor, leading to high labor demands and underscoring the urgent need for efficient, low-damage mechanized harvesting solutions. In this study, an air-suction double-roller harvesting device was designed. The harvesting mechanism operates by guiding L. barbarum into the picking zone via an airflow-induced suction stream, followed by gentle separation through a flexible double-roller structure. The fruit guiding process was modeled based on pneumatic attraction and analyzed through high-speed photography experiments. The results indicate that fruit velocity gradually increases during the guiding phase, with peak acceleration occurring at a specific location within this stage. A response-surface experiment was conducted to evaluate the effects of preloading force and roller speed on the picking rate of ripe fruit, the picking rate of unripe fruit, and the mechanical damage rate of ripe fruit. Parameter optimization resulted in optimal operating conditions with a preloading force of 0.903 N and a roller speed of 49.172 r/min. Under these conditions, validation experiments achieved a picking rate of ripe fruit of 93.7%, a picking rate of unripe fruit of 2.8%, and a damage rate of ripe fruit of 1.1%. This study provides a valuable reference for the design of vacuum-based harvesting devices for L. barbarum.
Vertical container-type smart farms offer an effective way to improve productivity in space-limited environments, but their multi-layer arrangement often creates stagnant airflow and nonuniform microclimates. In this study, computational fluid dynamics (CFD) and the Taguchi method were combined to optimize the layout of air-conditioning components in a container-type strawberry smart farm. Three spatial design factors were considered: inlet position, outlet position, and carbon dioxide (CO2) nozzle position. Nine cases were constructed using a three-level L9 orthogonal array, and spatial standard deviation was used as the uniformity index for temperature, CO2 concentration, and local mean age of air (LMA). The baseline model showed low-velocity and recirculation regions around the cultivation beds and structural frame, producing substantial nonuniformity in CO2 distribution. The middle layer had the highest mean CO2 concentration, whereas the lower layer had the lowest concentration, with a maximum interlayer difference of 53.7 ppm. The Taguchi analysis showed that outlet position was the most influential factor, with a delta value of 8.74 and a contribution ratio of 57.78%. The optimal configuration placed the inlet at 1,200 mm, the outlet at 1,500 mm, and the CO2 nozzle at 1,200 mm. Compared with the baseline model, this configuration reduced the standard deviation of CO2 concentration from 75.85 to 7.08 ppm and improved the standard deviation of LMA from 5.63 to 4.98 s. However, temperature uniformity slightly decreased as the analysis used steady-state conditions, fixed operating flow rates, and a simplified crop geometry; the optimal layout should be revalidated when the container size, crop canopy density, or operating strategy changes. These results demonstrate that spatial microclimate uniformity in multi-layer container-type strawberry smart farms can be improved efficiently by optimizing component layout.
Building information modelling (BIM) has progressively transformed professional practice in building design, landscape analysis, and spatial planning. As architectural and engineering firms have widely embraced BIM over the past two decades, higher education institutions have been pushed to rethink curricula to train future specialists accordingly. Agricultural Sciences, however, represent a discipline where this transition remains largely unexplored, despite its direct relevance to rural building design and agroforestry territory management. This paper presents a cross-sectional analysis of the attitudes and readiness toward BIM integration among Italian university departments of Agricultural Sciences. Data were collected through questionnaires completed by professors affiliated with the scientific sector AGRI-04/C (Rural Buildings and Agroforestry Territory), covering individual familiarity with BIM, perceived curricular relevance, and institutional support for educational innovation. The findings reveal a situation broadly comparable to that of emerging countries in the early stages of BIM adoption: widespread enthusiasm among individual academics is rarely matched by institutional commitment, specialized awareness, or concrete implementation strategies.
This study presents a comparative Life Cycle Assessment (LCA) of ice cream cups made from polystyrene (PS), polylactic acid (PLA), and paperboard (PAP), based on primary industrial data and a cradle-to-gate approach. The analysis was performed using the CML v4.8 (2016) method and complemented by the Environmental Footprint (EF 3.1) methodology to assess the robustness of the results. The results show that PLA exhibits the highest environmental impacts across most categories, mainly due to upstream processes associated with agricultural production and polymer synthesis. In contrast, PS is characterized by the highest fossil resource depletion and non-renewable energy demand, while PAP shows lower impacts in climate change but significantly higher land use. The comparison highlights the presence of trade-offs among materials, indicating that no single option is environmentally preferable across all impact categories. The consistency between the CML and EF 3.1 results confirms the robustness of the comparative assessment. These findings emphasize the importance of adopting a full life cycle perspective when evaluating alternative materials, particularly for bio-based products. The study provides a methodological basis for Environmental Product Declarations (EPDs) and supports informed decision-making in the food packaging sector.
Particle size is a critical parameter influencing the quality of biomass briquettes, particularly in char-based systems where bonding mechanisms differ from raw biomass briquetting. This study investigates the effect of particle size on the physical, mechanical, and proximate properties of hazelnut shell (Corylus avellana) char briquettes produced under controlled compaction conditions. Briquettes were prepared from three particle size fractions (0.84, 0.42, and 0.25 mm) using a constant binder content, moisture level, compaction pressure, and dwell time. The results show that reducing particle size led to improved bulk density and compressive strength, with finer briquettes exhibiting higher mechanical stability. Proximate analysis revealed a decrease in ash and volatile matter contents and an increase in fixed carbon as particle size decreased, indicating enhanced fuel quality for finer particles. Empirical relationships between particle size and briquette properties exhibited strong goodness-of-fit, with coefficients of determination (R²) exceeding 0.95 within the tested range. These findings demonstrate that particle size significantly influences both the mechanical integrity and fuel characteristics of hazelnut shell char briquettes. The results provide feedstock-specific experimental insights that can support process optimization in char-based briquetting systems, while emphasizing that the derived relationships are valid only within the investigated particle size range.
Soil organic carbon stock (SOCS) is a key indicator of soil fertility, ecosystem functioning, and climate change mitigation, yet its direct measurement remains labor-intensive, time-consuming, and costly. Visible, near-infrared, and short-wave infrared (VNIR-SWIR) soil reflectance spectroscopy offers a rapid and cost-effective alternative for SOCS estimation, although its predictive performance may strongly depend on sampling design and landscape heterogeneity. This study investigated the extent to which sampling density, spatial scale and spacing affect the accuracy of spectroscopy-based SOCS prediction. An ensemble modeling framework integrating partial least squares regression (PLSR), random forest (RF), and artificial neural networks (ANN), combined with five spectral pre-processing techniques, was applied to two contrasting datasets collected in southern Italy. The first dataset represented the entire heterogeneous region of Campania (CAM), comprising 2,957 topsoil samples collected on a sparse irregular grid with spacing ranging from 1 to 4 km. The second dataset corresponded to a local experimental field (MFC2, 8 ha), where 135 topsoil samples were collected on a dense regular grid with 25 m spacing. Model performance was evaluated using independent validation datasets through the coefficient of determination (R²), root mean square error (RMSE), and residual predictive deviation (RPD). Marked differences emerged between spatial scales. A fair predictive performance was achieved at MFC2 (best model: R² = 0.65; RPD = 1.71), whereas all models performed poorly at the regional CAM scale (best model: R² = 0.24; RPD = 1.14). Variogram analysis showed greater unresolved spatial variability (nugget effect) in the CAM dataset, indicating that the sparse regional sampling design failed to adequately capture fine-scale SOCS heterogeneity. To further investigate the role of sampling density, a denser subset of 130 CAM soil samples, with approximately 1 km spacing, was analyzed. Although model performance improved moderately (best R² = 0.35), predictive accuracy remained unsatisfactory (RPD <1.5), confirming that increased sampling density alone can be insufficient in highly heterogeneous regional landscapes. Overall, these findings demonstrate that sampling design and spatial heterogeneity are dominant factors controlling the reliability of spectroscopy-based SOCS estimation, emphasizing the adoption of spatially optimized sampling strategies to support robust regional soil carbon monitoring and assessment.
To address the challenges of excessive fruit damage and low success rates in densely clustered fruit harvesting requiring planar vector sequential extraction (a vector detachment strategy that projects 3D fruit positions onto the optimal operation plane for collision-free path planning, without restricting the end-effector to a fixed height plane), this study proposes a picking sequence optimization method based on multi-objective optimization. First, a geometric constraint model of critical tangent directions is established to determine collision-free detachment orientations for individual fruits. Subsequently, an Improved Non-dominated Sorting Genetic Algorithm II (I-NSGA-II) is developed by integrating multiple mechanisms: PSO-based extremum point injection for initial population generation, elitist selection for solution refinement, two-stage optimization (2-opt) for path smoothing, and cyclic crowding distance sorting for population diversity maintenance. This effectively resolves spatial constraints in dense fruit cluster separation while improving damage-free harvesting success rates. Experimental results demonstrate that compared with standard NSGA-II, our method achieves a significant 2.5% reduction in collision failure rates across various fruit density clusters, with picking path lengths reduced to 55% of those obtained through single-objective optimization. The proposed approach effectively solves low-damage harvesting challenges in densely aggregated fruit regions, demonstrating substantial practical value for advancing robotic harvesting technologies.
To address the poor adaptivity and blockage of straw round balers, a cone frustum-steel roll baling mechanism was designed. Through stress analysis in the frustum-wheat straw interaction process, the conicity of the frustrum was determined to be 32° to 63.4°. To expound the winding and axial migration law of wheat straw layers, EDEM 2024 was adopted to simulate the baling process of the cone frustum-steel roll baling mechanism. Taking the rotation speed of rolling-pressing steel rolls, frustum conicity, and axial clearance as test factors and formation time of rotating straw core as evaluation index, three-factor three-level horizontal regression response surface experiments were conducted. The regression equation was established to analyze influences of various factors on the index, the optimal parameter combination was determined: when the axial clearance, rotation speed of rolling-pressing steel rolls, and frustum conicity are 3.935 cm, 261.9 rpm, and 44.767 °, the predicted formation time is 5.237 s. The parameter combination was verified by tests, in which the formation time of a rotating straw core is 5.42 s, the formation time is 3.58 s shorter than that during operation of steel-roll baling mechanism. The research results provide a theoretical basis for the innovative design of round balers.
This study presents the development of a low-cost, portable, and AI-enhanced electronic nose (e-nose) system for quantifying ammonia (NH₃) volatilization from fertilized agricultural soils, with a specific emphasis on its implications for indirect greenhouse gas concentrations. Although NH₃ is not a greenhouse gas itself, its volatilization contributes significantly to indirect nitrous oxide (N₂O) emissions, one of the most potent GHGs regulated under IPCC guidelines. The proposed system integrates a MICS-6814 metal oxide sensor, ESP32 microcontroller, cloud-based data transfer, and machine learning algorithms to provide real-time monitoring and predictive analysis of NH₃ losses. Time-series sensor data were normalized, converted into area-under-the-curve (AUC) metrics, and modeled using eight machine learning algorithms. After preprocessing and hyperparameter tuning, Gradient Boosting achieved the highest performance (R² = 0.84; MAE=0.86). Laboratory evaluations demonstrated strong correlations between AUC values and NH₃-N measurements obtained through classical boric acid trapping, validating the system’s accuracy. The findings confirm that rapid detection of NH₃ volatilization can support digital nitrogen management strategies, reduce fertilizer-derived nitrogen losses, and ultimately help mitigate indirect N₂O emissions by minimizing surplus reactive nitrogen in agricultural fields. By enabling real-time emission monitoring through a low-cost digital platform, this research contributes to emerging precision agriculture solutions aimed at reducing the environmental footprint of nitrogen fertilization.
Agricultural machinery is constantly exposed to repeated loads under diverse operating conditions, making fatigue life evaluation essential for ensuring structural durability. This review provides a systematic review of the literature from the last decade (2015-2025), focusing key methodologies and recent advanced in fatigue life evaluation for agricultural machinery. Relevant literature was analyzed with a focus on stress and strain data acquisition, signal preprocessing techniques, repeated load classification, mean stress correction, damage accumulation models, and FEA–MBD and DEM–MBD integrated simulation approaches. This review highlights the accuracy of in-field stress and strain measurements, the appropriateness of fatigue evaluation methods under variable mean stress, the quality of load characterization, and the feasibility of simulation-coupled fatigue analysis. Furthermore, the review presents a comparison of the applicability and effectiveness of each method based on case studies. Several limitations in current research are also identified, such as inconsistency in evaluation standards, discrepancies between experimental and simulation results, and challenges in reproducing complex operating conditions. Ultimately, this comprehensive review establishes a systematic foundation for improving structural durability evaluation, early-stage design safety, and maintenance planning for agricultural machinery.
Climate change influences streamflow availability and dependability in watersheds due to its effect on the hydrologic cycle. This study assessed the quantitative impacts of climate change on dependable flow in the Lasang River watershed in southern Philippines using the Soil and Water Assessment Tool (SWAT) model. Three climate change scenarios including the baseline conditions and moderate and extreme conditions were formulated based on CMIP6 climate projections in the Philippines. The SWAT model was adequately calibrated and validated (NSE of 0.54 to 0.56) and was subjected to sensitivity and uncertainty analyses to ensure accurate representation of the watershed's hydrological behavior. Results of model simulation and flow duration analysis showed that moderate increases in precipitation and temperature due to climate change have negligible effects on dependable flow (1.1% increase), while extreme climate change conditions would result in relatively greater impacts on the watershed's dependable flow at a 14% increase. Results suggest that the river system may remain practically normal under a moderate climate change scenario, while greater water availability and dependability could be expected from the watershed under an extreme climate change scenario, particularly during the dry season for irrigation and other purposes. However, increased streamflow may still cause seasonal variability and potential dry-season water availability constraints under future climate conditions. Results obtained in this study could be used for proper irrigation system planning, design, and management and at the same time could serve as a basis for policy formulation geared towards sustainable water resources management under changing climatic conditions in the Lasang River watershed.
With the growing demand for automation in greenhouse logistics, ensuring both operational safety and motion smoothness has become a key challenge for composite mobile manipulators working in confined agricultural environments. This study proposes a safety-refined and smoothness-enhanced path-planning algorithm, termed SR-RRT-APF, to improve path feasibility and collision avoidance for agricultural robotic systems. The method integrates scheduled goal biasing, curvature-aware parent-node selection, density-adaptive step sizing, and potential-field-based soft guidance into an improved RRT framework. By incorporating explicit minimum-clearance constraints and lightweight post-processing, the algorithm jointly optimizes safety margins and geometric smoothness during the path generation stage. Extensive simulations and prototype-level tests were conducted on a greenhouse crate-handling robot equipped with a 6-DOF manipulator and a vision-guided mobile chassis. Ten consecutive crate-handling cycles were performed, in which the robot autonomously recognized, grasped, transported, and placed vegetable crates within narrow greenhouse aisles. Results from the simulation benchmarks show that SR-RRT-APF achieves superior path quality, larger safety margins, and improved smoothness compared with the benchmark algorithms in dense and constrained workspaces. Prototype-level experiments on a greenhouse crate-handling robot further support the system-level feasibility of the associated perception–manipulation workflow, indicating the practical relevance of the proposed method in greenhouse operations while also suggesting its applicability to a broader class of constrained-space planning problems.
In this study, we developed a simulation model for the internal environment of greenhouses using artificial intelligence techniques. This study focuses on the practical application of machine learning (ML)-based forecasting for greenhouse temperature control and its potential to improve energy efficiency under real-world operating conditions. This study was conducted to optimize energy consumption through smart agriculture applications. A prediction model was established using a time-series ML approach, followed by the development of forecasting models through gap-labeling feature analysis. Data were collected from a greenhouse equipped with a pellet boiler-based heating system over 55 d under real operating conditions. Iterative learning was conducted using 7 d of training data and 1 d of validation data, resulting in 48 models (average r2 = 0.77, RMSE = 1.43). Based on the forecasting results, a data-driven control strategy was applied to adjust the heating operation in advance. The reduction in heating operation time demonstrates improved energy efficiency of the greenhouse system under practical operating conditions by minimizing unnecessary heating. Implementing data-driven predictive control using the developed forecasting model can save 5 %–15% of the energy. Statistical analysis using the Wilcoxon signed-rank test indicated that the performance differences among the models were not statistically significant (p > 0.05). Therefore, this study emphasizes the practical implementation of machine learning-based forecasting for real-time greenhouse control under actual operating conditions.
Climate change has had profound impacts on agricultural systems, altering crop productivity, changing precipitation patterns, spreading pests and diseases, reducing soil quality, displacing agricultural areas, and increasing the use of inputs such as fertilizers and pesticides, which in turn leads to an increase in atmospheric emissions. To address this issue, this research proposes the use of a multi-agent system-based model to analyze the vulnerability of sugarcane production, representing complex systems and adapting to changing conditions by integrating dynamic and uncertain variables. The main advantage of the model is that it enables the quantification and analysis of critical variables, including the use of fuel, fertilizers, and nitrogen oxide (N₂O) emissions. The results demonstrate how the increase in operating trend negatively impacts environmental performance, highlighting the fragility of the system. Meanwhile, the validation of the model through structural tests and extreme conditions confirmed its reliability in supporting decision-making processes. Likewise, the average vulnerability value of the system (0.54) indicates a moderately unstable condition, susceptible to climatic and economic changes. Complementarily, the IMPACT 2002+ methodology was applied to conduct a life cycle assessment (LCA) of sugarcane, encompassing its cultivation and industrial processing. It was found that the resources used in sugar mills have the most significant environmental impact in the categories of climate change, human health, ecosystem quality, and resource consumption. This impact is caused by CO₂ emissions, the use of toxic pesticides and heavy metals, and high dependence on fossil fuels such as coal, natural gas, and oil, mainly. These findings underscore the need to enhance environmental management in Mexico's sugar sector by adopting cleaner technologies, establishing reliable ecological databases, and implementing assessment tools such as multi-agent modeling and life cycle analysis.
To address the need for precise thermal environment assessment in intensive broiler farming, this study proposes a dynamic apparent temperature (AT) optimization model that incorporates real-time biological feedback, overcoming the limitations of static traditional AT models. A baseline “AT-P” curve was established using the flock's minute-level panting rate (P) as a core biological feedback indicator. This curve is based on 7,138 records collected from a commercial farm, which include temperature, humidity, air velocity, age, and synchronously captured panting rate data. This curve maps observed panting to an “equivalent AT” representing the actual thermal load, with the deviation from traditional AT defined as the systematic correction residual. An Enhanced Attention Gradient Boosting Machine (EAG) ensemble model is then introduced to dynamically predict this residual, taking multi-source environmental features, traditional AT, and real-time panting rate as inputs, and outputting a corrected, optimized AT. Experimental results demonstrate that the EAG model achieves optimal performance in residual prediction, with an R² of 0.8329 and a mean absolute error (MAE) of 0.6503, significantly outperforming single base models and other mainstream algorithms. By integrating a static physical model with dynamic group behavioral feedback for online self-calibration, this study provides a methodological foundation for developing an animal-centric “perception-response-optimization” intelligent environmental control system in smart farming.
Water scarcity and improper nitrogen management are major constraints affecting the sustainability of olive cultivation in the hilly regions of Southwest China. This study investigated the effects of different levels of supplemental irrigation and nitrogen application on olive yield, oil quality, and water–nitrogen use efficiency. A two-year field experiment (2020-2021) was conducted in Jintang County (Sichuan, China) using a randomized complete block design with three irrigation levels (W1: 60% FC, W2: 75% FC, W3: 90% FC) and three nitrogen rates (2020: 180, 360, and 540 kg ha–1; 2021: 150, 300, and 450 kg ha–1). A multi-objective optimization model was developed integrating yield, irrigation water use efficiency (IWUE), nitrogen partial factor productivity (NPFP), and olive oil quality indicators. The NSGA-II algorithm was applied to identify Pareto-optimal solutions. Results showed that nitrogen application significantly increased olive yield under all irrigation conditions, with yield following the trend N1 < N2 < N3. Palmitic acid content initially increased and then decreased with increasing nitrogen rates, whereas oleic acid showed an opposite trend. IWUE increased with nitrogen application, while NPFP decreased. Comprehensive quality evaluation using the VIKOR method indicated that excessive nitrogen reduced oil quality under higher irrigation levels. The optimal treatment identified by the NSGA-II model was W2N2, corresponding to moderate irrigation and nitrogen input. Under these conditions, the predicted yield reached 12,045 kg ha–1, with a quality index (Qi) of 0.209, IWUE of 39.89 kg m–3, and NPFP of 47.48 kg kg–1. These findings demonstrate that balanced water–nitrogen management can simultaneously improve yield, oil quality, and resource use efficiency. The study provides a theoretical and practical basis for optimizing irrigation and fertilization strategies in olive orchards in mountainous regions of Southwest China.
Water management and farming operations are inseparable endeavors in effective farming, particularly in irrigated rice schemes. The farmer's response to general protocols, such as the cropping calendar, greatly determines the effectiveness of existing farming protocols. This study was conducted solely to determine farmers' adherence to the proposed calendar, which is important for water and agricultural operations management. This study makes use of the AboveGround Biomass Production (AGBP) estimates derived from the water productivity framework algorithm based on the PySEBAL model. Landsat data and other remote sensing products were assimilated into the model. The model somehow underestimated the AGBP values due to cloud contamination. The AGBP values were valid in terms of AGBP progression, which represents the general phenology of irrigated rice planted over the area. This study found that the AGBP is highest during February and September (times when most of the area is between the late vegetative and the harvesting stage), and the low monthly AGBP values during April and May represent the transition period from dry to wet season, where the rice fields are generally harvested, hence the low AGBP values. The results support the conformity of MARIIS Division IV irrigation scheme to the general cropping calendar for the area, which implies that the farmers mainly support management recommendations and protocols.
Heat stress poses a critical challenge to animal welfare and productivity in intensive livestock production systems, particularly for broilers and swine with limited thermoregulatory capacity. The temperature-humidity index (THI) is widely applied to assess thermal stress, yet conventional monitoring methods remain constrained by limited spatial coverage and scalability. This study introduces a novel satellite-based framework for estimating indoor heat stress in livestock facilities using machine learning and GEO-KOMPSAT-2A (GK2A) satellite data. The proposed outside-in approach integrates satellite-derived temperature, humidity, and solar irradiance to infer indoor environmental conditions without reliance on on-site sensors or detailed building specifications. Unlike computational fluid dynamics models, which are resource-intensive and difficult to scale, this data-driven method captures nonlinear relationships between outdoor meteorological variables and indoor microclimates. The XGBoost model demonstrated superior accuracy in estimating indoor temperature and humidity across multiple farms. When converted to THI, relative root mean square errors (rRMSE) ranged from 0.623% to 0.693% in swine farms and 0.827% to 1.332% in broiler farms, demonstrating robust performance in heat stress assessment. By leveraging geostationary satellite observations with high temporal and spatial resolution, this framework enables continuous, large-scale monitoring of thermal conditions in livestock facilities. The approach provides a practical and scalable tool to support ventilation management, cooling strategies, and animal welfare decisions under dynamic weather conditions.
The turbidity of wines needs to be reduced through filtration, with the other advantage of performing wine stabilization. Among the available techniques, cross-flow filtration is largely applied today because it does not require conventional filter media or filter aids, it can be applied during all the stages of winemaking and shows very high efficiency. Electrodialysis consists of separating differently charged ions, by the use of selective permeable membranes under the action of an electric field. It is used to apply tartrate stabilization on wines. These two techniques were applied to two red and two white wines, respectively cv. Nero d’Avola and Syrah and Catarrato and Grillo in an experimentation performed in Sicily (Italy) to evaluate the influence of these procedures on the quality of the wines ready to be bottled. Samples of wines were analyzed to evaluate the most important quality parameters (alcohol, pH, total acidity, volatile acidity, malic acid, lactic acid, citric acid, tartaric acid, ashes, color intensity and Hue, absorbance at 420, 520 and 620 nm, polyphenols, catechins, free sulfur dioxide, total sulfur dioxide, conductivity) and the aromatic profile by gas chromatography. Red wines showed greater sensitivity to quality change after the treatments, with particular reference to color.
Soil resistance encountered by the sweet potato seedling transplanting mechanism during soil penetration is a critical factor influencing the dynamic characteristics of the mechanism. However, the intricate mechanism-soil interaction makes the fluctuation patterns of soil resistance analytically intractable. Therefore, this paper conducts a dynamic analysis and experimental research on the transplanting mechanism with the non-circular gear planetary train for sweet potato seedlings via ADAMS-EDEM co-simulation. ADAMS and EDEM software were used to establish simulation models of the sweet potato seedling transplanting mechanism and soil discrete element models, respectively, to conduct joint simulation analysis of the interaction process between the transplanting mechanism and the soil, obtaining the resistance and resistance torque curves exerted by the soil on the transplanting arm. A kineto-static analysis was employed to establish a dynamic model of the transplanting mechanism considering soil resistance, followed by an analysis to derive the loading and driving torque profiles for each component. A dedicated dynamic test bench was developed to conduct experimental evaluations and capture the dynamic characteristics of the prototype. The experimental dynamic curves exhibited high consistency with theoretical predictions in terms of mean, variance, and overall trends, validating the accuracy of the proposed model and analysis. This study integrates ADAMS–EDEM co-simulation into the dynamic analysis of a non-circular gear planetary transplanting mechanism for sweet potato seedlings. Unlike traditional research that treats soil resistance as a constant or simplified value, this work establishes the nonlinear, time-varying loading characteristics model during the soil-entry phase. By incorporating the simulated force and torque curves as direct inputs into the dynamic equations, this approach effectively resolves the discrepancy between external loads and actual working conditions inherent in previous models. The proposed approach offers a robust methodology for the dynamic modeling and analysis of complex soil-engaging mechanisms.
