To address the lack of contact parameters for seed ginger and the insufficient accuracy of existing discrete element models used in simulations of seed ginger machinery, a discrete element modeling and contact parameter calibration method for irregularly shaped seed ginger was proposed. A discrete element model of seed ginger was constructed using 3D laser scanning and an adaptive multi-sphere filling method. The contact parameters between seed ginger and steel were calibrated using free-fall collision tests, inclined-plane tests, and combined inclined-plane–horizontal-plane tests. Using the angle of repose as the response variable, the optimal parameter combination was determined through steepest ascent tests and response surface methodology. The relative error between the simulated angle of repose and the value measured in the bench test was 2.24%. The results provide a modeling basis for the optimization and design of key components of seed ginger machinery and for investigating the dynamic interaction mechanisms among seed ginger particles.

IntroductionRapid advances in smart agriculture have highlighted the potential of large language models (LLMs), while practical applications remain limited by privacy risks, high training costs, and hallucinations. Focusing on garlic cultivation, this study proposes a knowledge-graph-enhanced framework that improves domain-specific LLMs through graph-based retrieval.MethodsA hybrid clustering algorithm with intra-cluster multidimensional ranking is employed to identify ten core entity types from garlic-related corpora. Few-shot learning and chain-of-thought prompting are further integrated to optimize entity and relation extraction using Qwen 2.5:32B, leading to the construction of a structured garlic cultivation knowledge graph. The knowledge graph is integrated with LLMs via GraphRAG, while prompt reconstruction strategies are adopted to enrich contextual information and constrain generation, thereby improving domain grounding and reducing hallucinations.ResultsExperimental results demonstrate absolute improvements of 13.0, 37.0, and 28.2 percentage points in precision, recall, and F1-score, respectively, for triple extraction compared with the baseline LLM, along with a 37.5 percentage point increase in retrieval-based question-answering accuracy.DiscussionThe proposed approach is interpretable and locally deployable without dependence on commercial APIs, offering a transferable methodological reference for domain-specific knowledge graph construction and intelligent question answering across diverse crop systems.

This study aims to address the major challenges encountered during peanut harvesting and digging in saline–alkali soils, including high digging resistance, poor soil fragmentation, and difficulty separating pods from the soil. A combined cutting–extrusion excavation device was optimized based on soil dynamics. A mechanical model for soil fragmentation via cutting and extrusion was constructed, alongside a mechanical model for separating soil–nut aggregates. Key factors influencing the device’s operational performance were identified, and parameter ranges were determined. Through theoretical analysis and discrete element simulation, the force distribution on the device during operation and the soil fragmentation mechanism were elucidated. Single-factor experiments determined the influence of each factor on evaluation metrics. Through orthogonal experimental analysis, the optimal parameter combination was determined as a blade angle of 21°, a rake angle of 50°, and a forward speed of 4 km/h. At this setting, the number of bonds broken was 172,652, and the working resistance was 14,673.5 N. Field test results indicate that the cutting–extrusion combined excavation device achieved 75.29% soil fragmentation and 16,965.4 N of working resistance. Compared to the original excavation device, soil fragmentation increased by 20.68% and working resistance decreased by 8.06%. The cutting–extrusion combined excavation device outperforms the original excavation device.

In response to the pressing issues of unclear adhesion mechanisms during the soil-removal process in peanut harvesting, poor soil fragmentation quality, and difficulties in separating the pods from the soil. Based on TRIZ theory, this study has innovatively designed a separation device that relies on external forces, such as kneading and squeezing. A mechanical model of soil fragmentation and separation was developed. The key factors affecting the device’s operational performance were identified. Through theoretical analysis and discrete element simulation, this study elucidates the working principle by which the device crushes and separates soil particles using kneading and squeezing forces. Through analysis of one-factor and orthogonal experiments, the optimal operating parameter combination for the device was determined to be: a drum installation clearance of 104.7 mm, a rotational speed difference of 75.2 rpm, and a pattern roughness of Grade III (reticulated). The system’s performance metrics are a soil removal rate of 96.59% and a pod damage rate of 2.48%. Field tests have confirmed that the deviation from simulation results is minimal. The device’s performance meets the requirements of actual production.

Consistent shoot orientation and uniform plant spacing are essential for high-yield, mechanized ginger production. However, current seeding systems still rely heavily on manual ginger seed placement, which limits the level of automation, particularly under stacked and occluded conditions. In this study, we present an integrated vision-guided robotic system for automatic ginger seed picking, shoot orientation adjustment and spacing control. For visual perception, we develop an enhanced YOLO11-seg detector with three architectural modifications: an asymmetric dual-stream RGB-D backbone for joint appearance and geometric feature extraction, a lightweight depth branch built with GhostConv and a proposed C3Ghost-SS module integrating SimAM and SGE attention, and an additional small-object detection head to improve shoot recognition in stacked and occluded scenes. To support reliable 3D localization, we further develop a rapid board-free hand–eye calibration procedure and a training-free biharmonic depth completion method. These perception and localization modules are integrated with a low-cost robotic arm to form a closed-loop system for seed picking, reorientation, and in-row spacing control. Across model scales (n → x), the improved network raises mAP@0.5 by 7.2 %, 7.6 %, 6.8 %, 7.0 %, and 6.5 % over the YOLO11 baseline. The calibration achieves 90th-percentile absolute errors of 1.85 mm (X), 2.07 mm (Y), and 2.91 mm (Z). For depth completion, the biharmonic equation method reduces MAE and RMSE by ∼ 62 % and 67 %, and increases PSNR and SSIM by ∼ 28 % and 5 %, respectively, while maintaining high computational efficiency. System-level tests yield 76.5 % seed-picking success, 86.3 % reorientation success, and spacing accuracy within ± 2.3 cm. These results demonstrate the feasibility of the proposed closed-loop robotic system for automated ginger seed manipulation under complex planting conditions, and provide a practical step toward more autonomous ginger planting.

Accurate discrete element method (DEM) modelling of mature peanut plants is essential for simulating peanut harvesting, pod detachment, and harvest-loss formation. However, existing peanut DEM models are usually simplified as isolated pods, rigid cylindrical particles, or partial stem–pod structures, which limits their ability to represent the flexible deformation of vines and pod stalks and the fracture behaviors at the pod–pod stalk junction. In this study, a DEM-based flexible plant model was developed for mature peanut plants. The geometric dimensions, contact parameters, and mechanical properties of peanut pods, pod stalks, and stems were measured through physical experiments. The Hertz–Mindlin model was used for non-bonded contacts, whereas the Hertz–Mindlin with Bonding model was adopted to represent the flexible connections among plant organs and the fracture behaviors of the pod–pod stalk junction. The main DEM parameters were calibrated using Plackett–Burman screening, steepest ascent experiments, and central composite design. The results showed that the tangential stiffness per unit area and tangential critical stress at the pod–pod stalk junction were the dominant factors affecting pod detachment force. The optimized parameter combination was a tangential stiffness per unit area of 4.738 × 105 N/m3 and a tangential critical stress of 9.350 × 105 Pa, corresponding to a simulated tensile force of 6.73 N. Model validation was performed by comparing peanut harvesting simulations with field trials. The relative error of pod loss rate between simulation and field measurement was less than 7.55%, and the t-test result indicated no significant difference between the two datasets (p > 0.05). These results demonstrate that the proposed flexible peanut plant model can effectively characterize pod–pod stalk separation and can provide a reliable DEM modelling basis for peanut harvesting process analysis and equipment optimization.

The layered soil crushing rotary tillage machine with L-shaped reclamation rotary blades and rotary-reclamation rotary blades combination was designed to deal with the problems of a low soil fragmentation rate, low straw mulching rate, and poor surface leveling after plowing in the traditional rotary tiller tillage mode in the coastal saline land of the Yellow River Delta. A dual active layered soil fragmentation tillage mode was proposed, and the key structural parameters, blade axis arrangement, and spatial layout of L-shaped reclamation rotary blades and rotary-reclamation rotary blades were determined based on the sliding cutting principle analysis. A discrete element model of soil straw tillage component aggregates suitable for coastal saline alkali land was constructed using EDEM, and the influence of L-shaped reclamation rotary blades and rotary-reclamation rotary blades on the soil tillage layer displacement performance and straw burial performance of saline alkali land was comprehensively analyzed from a microscopic perspective. Taking the rotation speed of the L-shaped reclamation rotary blades, the rotation speed of the rotary-reclamation rotary blades, and the forward speed as experimental factors, and using soil fragmentation rate and straw burial rate as evaluation indicators for experimental optimization analysis, the optimal parameters were obtained: the rotation speed of the L-shaped reclamation rotary blades was 295.04 r/min, the rotation speed of the rotary-reclamation rotary blades was 359.06 r/min, and the forward speed was 3.12 km/h. At this time, the theoretical soil fragmentation rate of saline alkali land was 94.67%, and the straw burial rate was 93.56%. Field experiments have shown that the average soil fragmentation rate of the L-shaped reclamation rotary blades and rotary-reclamation rotary blades combined layered soil crushing rotary tiller after cultivation is 94.37%, the straw burial rate is 95.68%, the surface flatness is 25.82 mm, and the stability of the tillage depth is 95.64%. The machine has shown increased performance in comparison to traditional single axis rotary tillers, meeting the needs of crop bed preparation in saline alkali land.

Aiming to resolve the problem of the poor peanut seed-filling effect under high-speed operation when developing high-speed peanut sowing with precision, a peanut precision seed-metering machine with an auxiliary air-suction seed-filling device was designed. Focusing on the force analysis of peanuts in the seed chamber, the peanut seed disturbance principle in the seed-metering machine for the blowing structure of an auxiliary air-suction seed-filling device was clarified. The seed-filling process was analyzed via DEM-CFD coupled simulation, and three factors affecting the seed-filling effect were identified, namely the seed-filling chamber ‘V’ angle γ, the bottom blow-air-hole cross-sectional area S, and the bottom blow-air-hole airflow velocity vq, and the ranges of values of the three factors were determined. The Box–Behnken test was conducted using the seed-filling index and leakage index as the indexes. The results show that the seed-filling chamber ‘V’ angle γ is 56.59°, the bottom blowhole cross-sectional area S is 1088.4 mm2, and the blowhole air velocity vq is 12.11 m·s−1. At this point, the peanut seed suction qualification index and leakage index are optimal, the seed suction qualification index is 96.33%, and the seed leakage index is 2.59%. At the same time, the field test shows that a sowing operation speed of 8–12 km·h−1, a qualified index > 93%, and a leakage index < 4.5% are required to meet the agronomic requirements of peanut precision sowing.

Considering the problems of a low soil fragmentation rate and low straw mulching rate in the traditional rotary tiller tillage mode in the saline-alkali land, the layered stubble and soil crushing rotary tillage knife was designed, and the key structural parameters were determined by the analysis of rotary tillage knife-soil-straw movement. The soil-straw movement behavior in saline-alkali land under different working parameters of rotary tillage was analyzed, and the discrete element modeling of soil-straw-rotary tillage in saline-alkali land was established. In addition, the dynamic process of soil-straw aggregate fragmentation in saline-alkali land from a microscopic perspective was systematically explored. Combined with experimental optimization analysis, the optimum working parameters of the saline-alkali rotary tiller were obtained with a forward speed of 2.02 km/h, a working depth of 178.83 mm, and a rotation speed of 324.48 r/min. To verify the field performance of the machine, the soil fragmentation rate, straw burial rate, and tillage depth stability were chosen as test indices for the field trial. The average soil fragmentation rate was 91.85%, the average straw returning rate was 91.09%, and the average stability of tillage depth was 91.12%, indicating that the designed rotary tiller can effectively improve soil crushing and straw burial in saline-alkali land and meet the basic requirements of high-performance seedbed preparation in saline-alkali land.

During the separation of ginger rhizomes from stems, ginger stems are prone to brittle fracture due to stress concentration. To enhance ginger harvesting efficiency and minimize breakage, this study focused on the development of a low-degree-of-freedom cutting mechanism with dual-clamping. This device primarily consists of differential-speed dual chains, a cutting blade, an electric motor, a stem disposal mechanism, a drive shaft, and a support frame. Based on comprehensive mechanical testing of ginger stems—including creep, stress relaxation, puncture, and shear tests—the key mechanical and physical properties of the primary harvesting sections were determined. Utilizing an EDEM-RecurDyn co-simulation algorithm, an interaction model between the stems and the cutting blade was established. Key operational parameters investigated were blade rotational speed, cutting angle, and stem base cutting distance (the distance from the stem-rhizome junction to the cutting point), with stem cutting completeness rate and breakage rate serving as the evaluation metrics for harvesting quality. Regression models correlating these metrics with the operational parameters were developed, enabling the determination of the optimal operating parameters for the rhizome-stem separation device. The optimized parameters are as follows: blade rotational speed of 190 r/min, cutting angle of 8.8°, and stem base cutting distance of 9.5 cm. Field trials demonstrated that the optimized separation device significantly mitigated brittle fracture incidents and markedly reduced blockages. Under optimal conditions, the cutting completeness rate reached 94.9%, while the breakage rate was reduced to 4.49%. These experimental results align closely with the predictions derived from the regression model optimization. This research provides a theoretical foundation for the design of ginger rhizome-stem separation devices.

This paper presents the design of a seed-pressing mechanism for a high-speed suction-type precision peanut planter to address the issue of poor seeding performance at high travel speeds and to reduce seed bounce within furrows. To clarify the working principle of the mechanism, a force analysis of peanut seeds in the furrow and a numerical study using discrete element analysis were conducted under high-speed operating conditions. Simulation results show that when the distance between the center of the seed-pressing wheel and the seeding-tube outlet (DCSPW-STO) is 146.11 mm, the seed-pressing wheel diameter is 198.13 mm, and the machine operating velocity is 6.45 km h−1, the plant spacing qualification index and seeding depth compliance index for peanuts planted after rolling reach their maximum values. The corresponding germination rates of 93.78% and 90.65% indicate satisfactory sowing performance. Field validation trials demonstrate that when DCSPW-STO (lfz) is 146 mm, the seed-pressing wheel diameter (dfz) is 198 mm, and the machine operating velocity (v) is 6.45 km h−1, the post-seeding plant-spacing qualification index and the seeding-depth compliance index reach 90.31% and 89.18%, respectively. Although slightly lower than the simulation results, these values meet the operational requirements for peanut seeding. Field performance comparisons with non-pressure seeding units further confirm that units equipped with the seed-pressing and soil-covering mechanisms significantly improve both the plant-spacing qualification index and the seeding-depth compliance index, satisfying agronomic requirements for high-speed peanut cultivation.

In this study, a simulation model of peanut pod particles during harvest in saline soil was tested to calibrate contact parameters. Discrete meta-fill models of peanut pods were generated by a 3D meter and EDEM software. The range of values of contact parameters for peanut pods was measured by conducting collision and other tests using a homemade test rig. The parameters that affect the significance of the simulation process of stacking angle were screened by the Plackett-Burman experiment, the steepest ascent experiment, and the Box-Behnken experiment. An optimization test determined the optimal simulation model parameters: The peanut pods had a Poisson's ratio of 0.386 and a shear modulus of 3.04 MPa. The coefficient of recovery for pods-pods collisions was 0.335, the coefficient of static friction was 0.854, and the coefficient of rolling friction was 0.346. The coefficient of recovery of collision between the pods-65Mn steel was 0.339, the coefficient of static friction was 0.589, and the coefficient of rolling friction was 0.159. The test results showed a relative error of 0.42% between the stacking angle bench and simulation tests. The results can provide data support for studying the discrete metamaterial characterization of peanut pods.

This study investigates the interaction mechanisms between the dibber, soil, and garlic during directional garlic seeding using the Discrete Element Method (DEM), aiming to improve planting uprightness and planting-depth consistency. A discrete element model of the garlic seeding process was established to analyze the dynamic interactions among the dibber, soil, and garlic from a microscopic perspective, and the reliability of the simulation results was verified through macroscopic experiments. The results show that dibbling depth, lifting height, dibbling speed ratio, and soil-particle surface energy are key factors influencing planting uprightness and depth consistency. Within a certain range, increasing the dibbling depth and lifting height significantly improves uprightness, although the effect diminishes beyond critical thresholds. The influence of the dibbling speed ratio on uprightness exhibits a nonlinear trend, in which both excessively low and excessively high ratios reduce uprightness. In addition, lower soil-particle surface energy leads to increased uprightness and improved soil backfilling performance. Experimental validation confirmed strong agreement between the simulation and physical test results, with an average relative error of less than 10%. This study provides a theoretical foundation and numerical simulation tools for optimizing directional garlic seeding technology, offering important guidance for improving planting uprightness and planting-depth consistency.

In the factory production of Lyophyllum decastes, inappropriate cultivation environments can lead to appearance quality issues, which in turn affect both yield and quality. However, the appearance characteristics of Lyophyllum decastes influenced by environmental factors share similarities, and the environmental factors that cause appearance quality problems exhibit coupling and complexity. Therefore, the identification of appearance characteristics and tracing of environmental factors present significant challenges. To address this issue, this paper proposes a multimodal learning network, DCRes-GAT, which integrates an improved Residual Neural Network (DCResNet) and a Graph Attention Network (GAT) to accurately identify the features of Lyophyllum decastes, while simultaneously tracing environmental factors and providing control recommendations. First, a knowledge graph based on the prior knowledge of quality and environmental factors is constructed, mapping this information to a point space and extracting key features. Next, DCResNet is employed to extract optical features from Lyophyllum decastes images. In addition, the receptive field is expanded through dilated convolutions, while pixel-level details are preserved, and a Convolutional Block Attention Module (CBAM) is incorporated to identify subtle visual differences. Finally, a dot product operation fuses point-space features with visual features, achieving accurate identification of characteristics and providing suggestions. Experimental results demonstrate that the DCRes-GAT model performs excellently, with a feature identification accuracy of 99.45%, and can precisely diagnose key environmental factors that cause appearance quality problems, achieving a diagnostic accuracy of 99.84%. This provides a basis for the precise control of the cultivation environment of Lyophyllum decastes.

Crops are prone to lodging with the decline of stem moisture and the intervention of other factors in the mature harvest period, such as garlic, which is difficult to harvest mechanically. To solve this problem, the plant-correcting reel for harvesting lodging garlic plants, bumped and deformed with plants many times to pull and lift them into a conveyor, was proposed in this study. This study analyzed the motion trajectory equation and key influencing factors of the reel and defined the lifting and plant-correcting stages as three processes of contact, stirring and release. For example, the contact deformation model and system energy equations were established in the contact process. Besides, in the stirring process, the garlic plantcorrecting conditions were established through the dynamic simulation test analysis of garlic seedling trajectories and the deflection model of garlic stem was constructed. Furthermore, in the release process, the expressions of rubber bars rotation and garlic plant offset bending curvature were constructed and the optimal number and distribution form of bars were determined. Meanwhile, the mechanism and key operating parameters of the auxiliary lifting mechanism of the divider were established. Through the single-factor test, the influence of reel speed, forward speed and reel height on the success feeding rate was analyzed under different bars distribution forms; Through multi-factor experiments, the interaction contour map of various factors was constructed. When reel speed, forward speed and reel height were 3 rad/s, 3.5 m/s, and 540 mm, the feeding success rate was 98.73%. The optimization factors were tested and verified, which met the operational requirements of a high feeding success rate and low loss rate of garlic harvest. This study combines laboratory virtual as well as field experiments and analyzes of trajectory of bars, contact deformation and deflection model of garlic plant, and reel rotation and garlic plant offset bending curvature to solve the problem of garlic lodging mechanized harvest and yield reduction.

Mass is one of the most common types of abnormalities in breast diseases. Identifying malignant masses in mammography prior to lesion biopsies remains a challenging task for radiologists. In this study, we aim to classify malignant and benign masses from diagnostic mammography in women who have been previously undergone ultrasound screening. In particular, we intentionally excluded lesions with well-established features that were readily identified by radiologists, and were already served as the basis for determining whether it’s suspicious or not. This framework integrates segmentation and classification tasks using a two-step architecture to segment and classify those hard cases from 220 women (40 benign cases, 180 malignant cases) with 460 digital mammograms. Our selected model generated a cross-validated AUROC of 0.75 and a dice coefficient of 83.25%. This study demonstrates the potential of classifying benign and malignant masses from ultrasound-screened diagnostic mammograms, which could be used to explore additional clinical biomarkers and risk assessment standards that would help doctors identify suspicious lesions.

In order to address the high impurity and loss rates in the cleaning process of existing peanut combine harvesters, the design basis of the cleaning sieve was explored. Tests were conducted to determine the composition of the peanut picking fruit and the characteristics of the material suspension mixture. Additionally, the mechanical relationship between the fruit and miscellaneous mixtures and the cleaning sieve was investigated. The wind speed ranged from 8 m/s to 10 m/s, the vibration frequency ranged from 6 Hz to 8 Hz, and the inclination angle of the sieve ranged from 5° to 9°. The scavenger sieve platform was constructed and tested using Box-Behnken’s central combination test method. The optimal combination of fan wind speed (9 m/s), sieve surface inclination angle (8°), and sieve vibration frequency (7 Hz) resulted in a pod impurity rate of 1.16% and a loss rate of 1.4%. The device significantly reduces impurity content and loss rate during peanut harvesting. The study's results can serve as a reference for improving the design of the peanut combine harvester cleaning mechanism and optimizing operating parameters.

Garlic is a versatile crop of high economic value, with an increase in the growth of the cultivation scale. Planting garlic with bottom-side (root) down and pointy-tip (clove) up is essential in agriculture because the orientation of the clove significantly affects garlic quality. In this study, we propose a clove orientation recognition technique based on capacitance sensing technology, where clove states are determined by utilizing characteristic differences in capacitance variations associated with different orientations of cloves. First, we applied Maxwell simulations to obtain capacitance variation during the falling process. Second, we conducted field experiments using the capacitive sensing device to obtain the capacitance variations during the falling of garlic in an unstructured environment. Third, the continuous capacitance signal collected during the field experiment was segmented into short-term feature signals containing individual garlic fallings. Finally, Long Short-Term Memory (LSTM) and Frequency-Residual-GoogLeNet (F-Res-GoogLeNet) deep models were trained to recognize garlic falling states. Our best model achieved an accuracy of 96.75%, which meets the agricultural requirements for garlic cultivation. This study demonstrates that monitoring capacitance data in field environments can identify garlic cloves’ orientation, eventually enhancing garlic’s final yield, quality, and economic benefits.

Considering the problems of poor straw mulching performance, low soil crushing rate and poor straw mulching performance of the traditional rotary tiller on saline-alkali soils, a two-axis layered rotary stubble cutter for saline-alkali soils with front-axis positive rotation of the front axle and rear-axis counter-rotation of the rear axle was developed, focusing on the kinetic properties of the straw and soil under positive and counter-rotation. In addition, the most important structural parameters and the arrangement of the front-axis stubble cutting knife and the rear-axis return knife were analyzed and determined. HertzMindlin with bonding was used to create a discrete element model of the agglomerate of implement, straw and soil. The forward speed, horizontal distance and vertical distance were used as test factors, and the straw return rate and soil fragmentation rate were used as test indexes to analyze the straw-soil transport law under different operating parameters from a microscopic point of view, and then Design-Expert was used to conduct the test 1.07 km/h, horizontal distance of 569.55 mm, vertical distance of 176.59 mm. To validate the performance of the two-axis, layered rotary tiller, a field trial was conducted and the results show that the straw return ratio was (91.59 +/- 0.41)%, soil fragmentation ratio was (91.90 +/- 0.29)% and tillage depth stability was (91.52 +/- 0.46)%, which met the requirements for peanut seedbed preparation on saline-alkali land.

A Gas–solid two-phase flow coupling simulation is widely used to study the working process of pneumatic seed dischargers. Due to the demand for deterministic particle orbit numerical calculation models, seeds are mostly modeled using the particle aggregation method, where the seed model is formed through particle aggregation bonding without overlapping. The smaller the radius and the more filled ball particles used in this method, the closer they resemble the real morphology of the seed. However, this results in the over-consumption of simulation computational resources and simulation time growth. In this study, we used wheat seeds as the research object, studied the effect of seed models with different filled ball radii on the kinetic response characteristics between the particles, and searched for the optimal number of filled ball particles for the seed model. With the help of three-dimensional scanning and inverse fitting methods to obtain the seed profile, we used different radii (0.2 mm, 0.24 mm, 0.28 mm, 0.32 mm, 0.36 mm, and 0.4 mm) to fill the ball particles, and formed a wheat particle bonding model for a gas–solid coupling simulation. We used a combination of real tests and simulation measurements of bottomless cylinder-lifting and slip-stacking. The interspecies static and dynamic friction factors in seed models with different radii of filled spherical particles were first calibrated using the angle of repose as an index. Then, the parameters were verified using bottomless cylinder lifting and slip stacking tests, which used the coefficient of variation for the simulation test’s angle of repose as an index. Our results show that the smaller the radius of the filled ball, the closer the simulation results were to the real value. Validation was conducted using a gas–solid coupling simulation of an air-blown wheat seed discharger, with the seed filling rate as an index. Our results showed that the simulation length and simulation accuracy were optimal when the radius of the filling particle was 0.32 mm.