Simulating the ear picking process using the Discrete Element Method (DEM) is of significant importance for revealing the ear detachment mechanism and guiding the structural optimisation of ear picking devices. Accurately constructing a DEM model that characterises the connection characteristics between the ear-peduncle and peduncle-stalk is a key foundation for maize ear picking simulations. To address the inconsistency in the connection characteristics between the ear-peduncle and peduncle-stalk in the maize plant DEM model, a modelling method targeting these inconsistencies is proposed. By calculating and solving the regression equations of the Box-Behnken design, the bonding parameters between the ear-peduncle and peduncle-stalk were calibrated. The connection characteristics of the ear-peduncle and peduncle-stalk were verified through both physical and simulation tests. The results showed that the maximum tensile loads in the simulation model that the peduncle-ear and peduncle-stalk can withstand were 140.55 N and 290.56 N, respectively, with relative errors of 4.5% and 4.7% compared to the average values measured in the actual tests, and they exhibited consistent mechanical trends. A maize plant model was constructed based on the calibrated bonding parameters, and the simulation model's ability to reproduce the actual ear picking test was validated. The average picking force from the simulation and actual tests were 104.9 N and 111.07 N, respectively, with a relative error of 5.6%. The results indicated that the plant model accurately reflected the real ear picking process, providing crucial model support for the design and optimisation of subsequent ear picking devices.

To enhance the cleaning effect of the grain harvester under the condition of large feeding volume, and to improve the adaptability between the airstream distribution law at the sieving interface and the dispersion pattern of mixed and extruded materials, and to efficiently utilize the airstream to blow and stratify the mixed and extruded materials at the sieving interface. This study focuses on the air current source of the cleaning device - the structure of the cleaning fan itself, and combines the physical properties of the grains themselves and their dispersion pattern at the sieving interface of the upper cleaning screen to study the distribution law of the internal airstream dynamics of the grain separation assembly under the changes of various key parameters of the parallel dual fans. A multi-factor optimization test was conducted on a parallel double fan structure, considering blade width, impeller diameter, number of blades, parallel clearance, and inclination angle of the splitter plate as objective factors. The wind speed distribution in front and behind the vibrating mesh served as the evaluation index. Response surface analysis demonstrates the interaction of critical factors on wind speed distribution both sieve windward region and leeward region. The optimal combination of operating parameters is determined through the utilization of a two-index optimization method: impeller diameter 500 mm, blade width 530 mm, number of blades 10, parallel gap 90 mm, and deflector plate Angle 24 degrees. The results under this parameter combination are as follows: The airstream velocity at the front part of the screen is 12.07 m/s, and the airstream velocity at the rear part of the screen surface is 8.13 m/s. A test bench for the parallel dual-fan cleaning device was set up, and the determination of the airflow field was carried out. The experimental data demonstrate strong correlation with numerical simulation outputs. The optimized dispersion pattern of the airflow field is compatible with the dispersion pattern of the discharged substances at the sieving interface, meeting the requirements of the cleaning operation. The optimization model of the cleaning index is reliable. The findings offer substantial guidance for refining the cleaning fan structure of the harvester.

To address the limited accuracy of biomechanical representation in Discrete Element Method (DEM) models, this study proposes a discrete element modelling and parameter calibration method for a flexible wheat plant with breakable grains. First, the shape and dimensional parameters of the wheat plant were measured, and a flexible wheat plant model with breakable grains was constructed using the Meta-particle model and the Bonding V2 model. Next, the ranges of the intrinsic parameters, interaction parameters, and bonding parameters of the wheat plant were determined based on mechanical property tests. Parameter calibration was conducted using angle of repose (AOR) tests, grain compression tests, straw three-point bending tests, and tensile tests of wheat ears, in combination with response surface methodology. Finally, drum rotation tests and simulations, as well as impact breakage tests and DEM-MBD co-simulations, were conducted. By comparing the test and simulation results, the accuracy of the DEM model was verified. In the drum rotation test, the relative error between the simulated and measured dynamic angles of repose of the grain-short straw mixture was within 6%. In the impact breakage test, both the simulated and physical wheat straw underwent bending fracture, and the simulated values of the unthreshed rate and breakage rate of the wheat grains were close to the measured values. Validation tests demonstrate that the proposed modelling and parameter calibration method was effective. This study provides a reference for developing an accurate DEM model of a flexible wheat plant that realistically reflects the breakable characteristics of the grains.

The feeding rate is a critical parameter for efficient and low-loss operation of grain combine harvesters. Current measurement technologies face challenges such as low accuracy, poor stability, and delayed results. To address these issues, this study proposes a feeding rate measurement method based on multimodal information fusion, integrating crop visual perception and harvester cutting load. An image sensor captures crop images in front of the harvester, and an improved Deeplab image segmentation algorithm is used to measure crop height. Real-time cutter load data calculates the cutting area, and combining this with the harvester's operational speed enables precise feeding rate calculation, effectively addressing interference caused by complex working conditions. To reduce the impact of vibrations and electromagnetic environments on sensor signals, an M-E fusion filter is designed to extract effective cutting resistance information, ensuring stable and accurate calculations. Using ROS communication and Python 3.6, an intelligent feeding rate measurement system was developed, supporting sensor data acquisition, multi-sensor data fusion computation, and human-computer interaction. Field tests showed that at operating speeds of 2-5 km/h, the average relative error of the feeding rate measurement is 1.64%, with a root mean square error of 0.103 & times;10(-3) m(3)& centerdot;s(-1). Under crop densities of 138-553 plants/m(2), the average relative error is 1.89%, with a root mean square error of 0.061 & times;10(-3) m(3) s(-1). The system demonstrates high accuracy and stability, providing technical support for intelligent combine harvester control and offering significant practical value in agricultural machinery research.

Aiming at the reliability of ear picking and the consistency of stalk chopping length in the process of corn ear and stalk harvesting, a new type of corn harvester with both ear and stalk harvesting based on exciting ear picking was developed. Based on the vertical cutting table, the machine realizes the excitation of the ear during the process of stalk transportation by rotating the eight-edged special-shaped pick-up roll, and the stable and orderly transportation of stalks before cutting is realized by the way of clamping and conveying with the rear rollers. By analyzing the configuration and parameter determination methods of the main working parts, the high-efficiency and low-loss harvest of the ear was realized, and the consistency of the cut length of the stalk was guaranteed. A discrete element model (DEM) of ear-bearing maize plants was established using EDEM (version 2024, Altair Engineering, Troy, MI, USA) simulation software, and a five-factor, three-level quadratic orthogonal rotation experiment was conducted based on Response Surface Methodology (RSM). The simulation results indicated that the optimal operational quality was achieved under the following parameters: a header angle of 10°, a snapping roller speed of 942 rpm, a clamping roller speed of 215 rpm, and a moving blade speed of 1450 rpm. Furthermore, multiple sets of field trials were conducted at various forward speeds to validate these findings. The mean values of seed loss rate, ear loss rate, and seed breakage rate are 0.51%, 0.55%, and 0.32%, respectively, for the harvester at operating speeds of 4 km/h, 6 km/h, 8 km/h, and 10 km/h. The σ values are 97%, 98%, 97%, and 98%. The field harvesting performance indexes meet the requirements of technical specifications for evaluating the operation quality of corn combine harvester, and meet the design requirements of low loss, high efficiency, and consistency of stem chopping length.

To address the issue in high-throughput longitudinal axial-flow grain combine harvester cleaning systems, in which the extended length of the cleaning chamber results in airflow velocity attenuation and makes it difficult to efficiently and rapidly remove light impurities, a front-blowing and rear-suction enhanced cleaning technology and device was developed. Based on the investigation of the movement characteristics of the cleaning airflow within the cleaning chamber, a theoretical model was established to describe the velocity variation of the front-blowing and rear-suction enhanced cleaning airflow. CFD simulation software was employed to conduct a comparative analysis of the airflow field structure before and after improvement, aiming to identify the influence patterns of key structural parameters on the airflow field distribution. An orthogonal experiment with three factors and three levels was conducted on the improved cleaning system, focusing on the suction fan speed, vertical installation height of the suction fan, and horizontal distance between the suction fan and the sieve surface. The influence of each factor on the airflow field was analyzed, and the optimal parameter combination was obtained. When the suction fan speed was 2275 r/min, the vertical installation height was 72.5 mm, the horizontal distance to the sieve surface was 385 mm, and the airflow non-uniformity coefficient at the rear part of the screen surface was 11.17%, with a relative error of 4.39% compared to the optimization result. Finally, bench tests were conducted to verify the accuracy of the simulation results. Compared to that before improvement, the airflow non-uniformity coefficient at the rear part of the screen surface in the cleaning chamber was reduced by 59.43%, significantly improving the uniformity of airflow distribution. These findings provide both theoretical and technical support for improving the cleaning efficiency and operational performance of high-throughput grain combine harvesters.

To reduce maize ear damage caused by ear-picking rollers during maize harvesting and to improve ear-picking efficiency, this study investigates the vibration response characteristics of maize plants and conducts comprehensive experimental research. Current research on vibration ear-picking predominantly conducts on the structural design and parameter optimization of ear-picking mechanisms, with comparatively limited attention given to the dynamic response patterns of maize plants under vibrational excitation. Therefore, it is essential to conduct an in-depth investigation into the mechanical behavior and vibration response characteristics of maize plants during the maize ear detachment process. In this study, the mechanical behavior of ear-bearing maize plants during the vibration ear-picking process was first analyzed, and a theoretical model was established. The optimal acceleration range for low-damage and high-efficiency ear-plant separation was identified, and the key parameters affecting ear-picking performance-namely amplitude, frequency, and clamping position-were determined. Based on the slope algorithm and response surface methodology, combined with finite element analysis, this study quantitatively evaluates the influence of various vibration parameters on the acceleration score Sz at the ear-bearing region. A predictive model was established using a Box-Behnken experimental design, identifying the optimal parameter combination as an amplitude of 8 mm, a frequency of 18 Hz, and a clamping position of 106 mm. This model enables the quantitative analysis of excitation forces acting on the ear during the ear-picking process and provides a theoretical foundation for optimizing the structure of the excitation waveform, thereby offering valuable guidance for the subsequent optimization and design of vibration ear-picking systems.

During the maize ear harvesting process, a reasonable selection of the picking roller's surface structure can significantly enhance stalk pulling force, reduce ear-picking losses, and improve overall harvesting efficiency. Investigating the influence of different picking roller surface structures on stalk pulling force is therefore of critical importance. In this study, a simulation model was developed based on the Discrete Element Method (DEM) and Multi-Body Dynamics (MBD) to simulate the interaction mechanisms between the ear-picking device and maize stalks. The accuracy of the simulation model was validated through bench tests, using maximum stalk pulling force and power consumption as key evaluation metrics, with relative errors of 5.4 % and 5.2 %, respectively. The study further explored the effects of picking roller surface structure (pattern shape, pattern height and pattern spacing) on stalk pulling force. The results indicate that pattern shape, pattern height, pattern spacing, and their interactions have a significant impact on stalk pulling force. The optimal surface structure of the picking roller is a inclined pattern structure with a pattern height of 2.5 mm and a pattern spacing of 8 mm. The simulation results can be used to analyse the effect of the picking roller surface structure on stalk pulling force, providing a theoretical basis for the rational selection of picking roller surface structures.

Currently, the field of intelligent corn harvesting in China lacks effective methods for detecting corn kernel breakage. This paper explores and proposes a corn kernel detection technology that utilizes deep learning and sliding window technology, combined with a specially developed quantitative model, to enable real-time detection of the corn kernel breakage rate. In this study, we quantified the corn kernel mass at various levels of crushing and proposed a quantitative model for the corn kernel breakage rate, which is suitable for real-time computation by a computer vision system. We developed a specialized corn kernel detection device to generate high-quality datasets and retrain our previously proposed corn kernel breakage detection model (BCK-YOLOv7). Subsequently, ablation experiments were conducted to assess the generalization capability of the BCK-YOLOv7 model in corn kernel detection. Furthermore, we analyzed the limitations of single-frame detection through dynamic comparison experiments. To address the instability of single-frame detection results in the corn kernels flow state, we introduced the sliding window technique, which, along with pipeline technology, significantly enhances detection efficiency. Finally, the comprehensive performance of the proposed corn kernel breakage detection technology was validated through systematic testing. The results indicate that the relative error in the detection of the breakage rate remains around 7%, and the detection rate of the technology, when deployed on edge devices, can achieve 22 frames per second (FPS), thereby meeting the requirements for real-time detection of corn kernel breakage rate.

To address the problems of corn harvesting in the Yellow Huaihai region with high moisture content, such as grain damage and high failure rate, a wider and taller ripple block threshing element was designed by combining the threshing principles of different threshing elements and analyzing the effects of the overall layout and parameters of the element on the threshing process. The threshing element can improve the collision attitude between the corn and the element and prioritize part of the corn kernels falling off during the collision, which makes the subsequent threshing smoother and realizes a low crushing rate of corn in the process of corn detachment. The different stages of the corn-threshing process were analyzed, a threshing simulation test was carried out, and the threshing force of the intact corn on the top side was measured to be 42.86 N; the closer the kernel was to the position of the dislodged kernel, the more the dislodging force was gradually reduced, with a minimum of 2.09 N, which verified that it was difficult to dislodge the kernel when the ear was intact and that the difficulty of dislodging the kernel around the kernel decreased as the corn was dislodged.

This study measures and analyzes the heterogeneity and mechanical properties of maize kernels at the microscopic scale. Through microscopic tissue analysis and mechanical property tests, it was found that there are significant differences in the mechanical properties of different tissues in maize kernels. The starch granules in the horny endosperm are regular polyhedra, closely arranged, with a high number of proteins tightly filling the gaps between starch granules. The structural characteristics of the horny endosperm give it a high maximum rupture force and elastic modulus, with a maximum rupture force of 128 N and an elastic modulus of 353 MPa. The starch granules in the farinaceous endosperm are spherical and loosely and irregularly arranged, leading to more gaps between the starch granules. As a result, the maximum rupture force and elastic modulus of the farinaceous endosperm are relatively lower. The maximum rupture force of the farinaceous endosperm is 38 N, and the elastic modulus is 136 MPa. Compression tests were conducted on maize kernels, and scanning was performed using a Micro CT system. The results showed that the farinaceous endosperm deforms and breaks more easily, with most damage beginning in the farinaceous endosperm and then extending further. The micromechanics discrete element analysis of the loading process of the farinaceous endosperm was carried out further. It was found that the deformation of the farinaceous endosperm occurs in four stages: initial, crack initiation, crack propagation, and fracture. When the farinaceous endosperm is loaded to 132 N, internal cracks begin to initiate and gradually propagate. At 292 N, the internal particles of the farinaceous endosperm start to break, followed by a drop in load and eventual fracture. During the loading process, significant differences in the velocity field of the farinaceous endosperm were observed.

To address the issues of high kernel breakage rate and poor adaptability in threshing devices for direct corn kernel harvesting, this study proposes a flexible threshing device based on a single-point hinged structure. In this design, the threshing elements are flexibly mounted via single-point hinges, allowing them to rotate upon impacting corn kernels. This rotational motion provides an impact-buffering effect during the threshing process, thereby reducing the risk of kernel breakage. By comparing the dynamic characteristics of the single-point hinged flexible structure with those of a rigid structure, the superiority of the proposed device is confirmed. A contact force model is established to analyze key parameters influencing threshing performance, identifying the mass of the threshing element, rotational speed, and structural configuration as the primary factors. A coupled simulation model was developed using RecurDyn and EDEM software to investigate the effects of threshing element mass, rotational speed, and structural configuration on the kernel breakage rate and unthreshed grain rate of the device, and to determine the optimal parameter range. Based on a three-factor, three-level orthogonal experimental design, the optimal working parameters of the device were identified as follows: threshing element mass of 200 g, rotational speed of 600 r/min, and a composite structural configuration. Under these conditions, the kernel breakage rate was 3.95% and the unthreshed grain rate was 0.88%, meeting the practical requirements for harvesting performance. This study provides theoretical support and a technical pathway for the design of high-efficiency, low-damage threshing devices for direct corn kernel harvesting.

This study addresses the issue of corn grain damage, which limits the efficiency of mechanized corn harvesting. The characteristics and distribution of corn grain surface damage resistance (CDDRS-SCK) were investigated. The "vulnerable" surface of the corn grain was selected for damage testing. The results revealed significant variations in damage resistance across different surfaces and locations of the corn grains. A regression model for damage resistance strength, based on surface position, was developed. Additionally, the impact of grain damage resistance on threshing damage was compared and analyzed for different threshing devices, in accordance with their working principles. This research provides theoretical insights and data support for the development of corn mechanized threshing technology and equipment.

ABSTRACT At present, there are serious problems in sorghum harvesting, such as lodging entanglement and the loss of broken stems due to the lack of an external dividing device for the cutting table and holding device. Based on the physical and mechanical characteristics of sorghum plants, an integrated outer divider was developed. The main structural parameters and working parameters of the outer divider were determined. A comparative test of the working quality of the outer divider was carried out. The results show that the working quality of the cutting table with the outer divider is obviously better than that without the outer divider and that the harvest loss of the cutting table is effectively reduced. The Box–Behnken experimental design method was used to investigate the effects of the forward speed, rotation speed of the grain lifter and dividing angle of the outer divider on the lodging and broken stem loss rates during sorghum harvesting. The regression mathematical model and response surface of the lodging and broken stem loss rates and the analysis factors were established, and the optimal working parameters of the outer divider were determined as follows: the dividing angle of the outer divider was 20°, the forward speed was 0.8 m/s, and the rotating speed of the grain lifter was 330 rpm. Under these parameters, the loss rate of the fall was 1.08%, and the loss rate of broken stems was 1.05%, which met the requirements of sorghum cutting tables

Traditional corn threshing devices face issues of high unthreshed grain rate and high breakage rate under conditions of high feeding rates. To address this, a high-throughput double longitudinal axial flow corn threshing device was designed in this study. Based on a stress analysis of the interaction between threshing components and corn ears, an arc-shaped plate tooth structure was developed to progressively increase the squeezing force between the plate teeth and the ears. A combined threshing element, integrating arc-shaped plate teeth and round-headed nail teeth, was designed to improve threshing cleanliness and minimize grain breakage under high feed capacity conditions. The crucial parameters of the threshing cylinder were determined by theoretical analysis. Threshing bench experiments were conducted to investigate the effects of feed rate, threshing cylinder speed, and guide plate angle on the device's grain breakage rate and unthreshed grain rate. Based on the findings, the optimal parameter ranges were identified. An orthogonal test involving three factors at three levels each was conducted to determine the optimal working parameters of the device. The results indicated that the ideal conditions were a feed rate of 16 kg/s, a threshing cylinder speed of 400 r/min, and a guide plate angle of 26°. Under these parameters, the grain breakage rate was 5.02%, and the unthreshed grain rate was 0.171%. The operational performance met the actual harvesting requirements. This research could offer a reference for the design of large-feed-rate threshing devices and related harvesters.

This research introduce a short time-enhanced frequency domain decomposition (ST-EFDD) method for accurate identification of modal parameters, including eigenfrequencies and damping ratios, in mass time-varying structures. The method integrates segmented processing with frequency-domain analysis to enhance local vibration characterization, improves computational efficiency via adaptive window sizing, and is validated on both a simply supported beam and a corn combine harvester under mass time-varying conditions. The results reveal a clear decay trend in the eigenfrequencies of the corn combine harvester, with defined variation ranges across 12 identified modes. Statistical analysis of the damping ratios extracted via ST-EFDD shows minimal temporal fluctuation across modal orders. The modal characteristics of the mass time-varying beam were identified using both ST-SSI and ST-EFDD methods. Comparative results indicate that ST-EFDD offers superior performance under conditions with limited excitation sources. Accordingly, ST-EFDD is applied to the corn combine harvester system to extract modal parameters during field operation, where the structure is subject to complex excitations.

In China's algae farming, ropes are widely used for seedling cultivation and suspension, requiring fast and reliable uncoupling during harvest. However, current kelp harvesting faces issues like complex rope connections, low unlocking efficiency, and poor reusability. To address this, a PLC-controlled rapid disengagement system was developed. The proposed system employs a button with self-locking functionality, coupled with a magnetically controlled disengagement device, to achieve automated unfastening of seedling ropes without damaging the suspension rope. The paper presents the components and operating principle of the locking device. To address the force characteristics of the seedling rope during the disengagement process, a dual-fingered pusher configuration was designed. The use of an involute trajectory enabled stable extraction of the rope from the locking groove. A Delta PLC was employed as the central controller, and an execution system comprising of an electric push rod, proximity sensors, magnetic adsorption module, and rope-pulling motor was developed to achieve automated control of lock correction, magnetic positioning, unlocking, and rope disengagement. Experimental results indicated that the system achieved the highest disengagement success rate of 96.67% when the guiding slot clearance was 14 mm, the haulage rope speed was 40 mm/s, and the pusher length was 45 mm, demonstrating stable and reliable device performance.

Simulation is an important technical tool in corn threshing operations, and the establishment of the corn kernel model is the core part of the simulation process. The existing modeling method is to treat the whole kernel as a rigid body, which cannot be crushed during the simulation process, and the calculation of the crushing rate needs to be considered through multiple criteria such as the contact force, the number of collisions, and so on. Aiming at the issue that kernel crushing during maize threshing cannot be accurately modeled in discrete element simulations, in this study, a sub-area crushing model was constructed; representative samples with 26%, 30% and 34% moisture content were selected from a double-season maturing region in China; based on the physical dimensions and biological structure of the maize kernel, three stress regions were defined; and mechanical property tests were conducted on each of the three stress regions using a texturometer as a way to determine the different crushing forces due to the heterogeneity of the maize structure. The correctness of the model was verified by stacking angle and mechanical property experiments. A discrete element model of corn kernels was established using the Bonding V2 method and sub-area modeling. Bonding parameters were calculated by combining stacking angle tests and mechanical property tests. The flattened corn kernel was used as a prototype, and the bonding parameters were determined through size and mechanical property tests. A 22-ball bonding model was developed using dimensional parameters, and the kernel density was recalculated. Results showed that the relative error between the stacking angle test and the measured mean value was 0.31%. The maximum deviation of axial compression simulation results from the measured mean value was 22.8 N, and the minimum deviation was 3.67 N. The errors between simulated and actual rupture forces at the three force areas were 5%, 10%, and 0.6%, respectively. The decreasing trend of the maximum rupture force for the three moisture levels in the simulation matched that of the actual rupture force. The discrete element model can accurately reflect the rupture force, energy relationship, and rupture process on both sides, top, and bottom of the grain, and it can solve the error problem caused by the contact between the threshing element and the grain line in the actual threshing process to achieve the design optimization of the threshing drum. The modeling method provided in this study can also be applied to breakable discrete element models for wheat and soybean, and it provides a reference for optimizing the design of subsequent threshing devices.

During the mechanized harvesting of Laminaria japonica, it is prone to breakage and damage, resulting in an increased loss rate. To accelerate the optimization of harvesting equipment for Laminaria japonica, this study established a simulation model based on the discrete element method. The Hertz-Mindlin with Bonding contact model was used, and parameters of Laminaria japonica were calibrated through shear tests. Using the maximum shear force (Fmax) as the test indicator, the optimal parameters were obtained through Plackett-Burman test, the steepest climb test, Box-Behnken test, and the GP-PSO-XGBoost regression prediction model. The results indicated that when the coefficient of restitution of Laminaria japonica-steel was 0.45, the normal stiffness per unit area was 303 MN/m3, the shear stiffness per unit area was 378 MN/m3, and the bonding radius was 0.70 mm, the relative error of Fmax was 0.75%. The average error of the Fmax for samples at different thicknesses was 3.09%, and the relative error of the maximum puncture force in puncture test was 5.59%. Finally, a discrete element model of the whole Laminaria japonica was established. This study offers theoretical support for the development and optimization of harvesting equipment for Laminaria japonica.

Precision soil fertilization is an important aspect of smart precision agriculture development, and the fertilization prescription map is a prerequisite for precision fertilization. Taking grapevine soil information as an example, this study explores the impact of different sampling densities on the accuracy of soil nutrient distribution. Experimental trials were conducted using sampling densities of 1m x 1m, 3m x 3m, 6m x 6m, 9m x 9m, and 12m x 12m, with the optimal sampling density determined to be 6m x 6m. Nutrient distribution maps were created using Bigemap and ArcGIS software, and based on nutrient balance calculations using ArcGIS software, fertilization prescription maps were developed. Furthermore, precise fertilization schemes for nitrogen, phosphorus, and potassium fertilizers were formulated based on the prescription maps. This study provides methodological and data support for research on precision soil fertilization.