The germination of castor seeds was affected by different damage forms after shelling. Traditional methods could not express the change of mechanical damage characteristics on the surface of castor seeds. In the study, an improved migration learning algorithm for castor seed damage classification was adopted. The convolution kernel size of the first convolutional layer of the AlexNet model was modified, part of the convolutional layer was divided into two layers to increase the depth of the convolution model. Then a multi-scale convolution kernel was added to extract the damage characteristics of castor seeds. The results showed that combined with the hyperparameter optimization of convolutional layer stratification and the AlexNet model,the classification effect was improved. The average test accuracy was 98.10%. After the addition of multi-scale convolution, the average test accuracy was improved by 0.57%. The results show that the classification accuracy of cracked castor seeds is 71%, and the classification accuracy of castor seeds with missing shells is 63%. The classification accuracy of whole castor seeds is 67%. The verification of damage identification device for castor seeds was developed to verify the correctness of the algorithm. This study provided a theoretical and convolutional network model supported for the development of an online real-time damage classification detection system for castor seeds.

To address the issue of the lack of suitable integrated harvesters for the soybean-corn intercropping mode in China, this paper designs a 4DYZ-4/2 model soybean-corn integrated harvester header and develops a reliable integrated header with low loss rates during harvesting. This header integrates functions such as snapping soybean stalks, separating soybean and corn, corn snapping and conveying, corn cob cutting, and soybean stem conveying, with innovative structural adjustments to the overall frame, ensuring efficient harvesting of soybeans and corn while reducing the labor intensity of operators. Based on the characteristics and requirements of the soybean-corn intercropping mode, the operational performance parameters and key parts were optimized. The main design parameters include: a header width of 1400 mm, a divider width of 400 mm, a header row spacing of 450 mm, a reel radius of 550 mm, six snapping rollers, a snap roller speed of 4.8 m/s, and a reel rotational speed of 1314 rpm. Field test results show that the header achieves a soybean loss rate of 1.28% and a corn loss rate of 1.42%. The research results confirm the reliability and practicality of this header design, providing technical support for soybean-corn intercropping integrated harvesting.

Efficient cotton stalk recycling is crucial for agricultural sustainability. Traditional manual removal methods suffer from low efficiency and high labor intensity. Existing mechanical harvesters face limitations such as low removal rates, high stalk breakage rates, and significant omission rates. To address these challenges, this study focuses on optimizing a self-developed multi-row disc-type stalk puller. The conventional flat disc was redesigned into an obliquely angled disc with a bending angle. This 3D curved design enhances stalk constraint capability, thereby improving gripping stability and lifting height. A kinematic model was developed using the ADAMS dynamics simulation platform, revealing the relationships between motion trajectories and key structural parameters. Parametric analysis quantified the effects of disc diameter and bending angle on lifting height and working width. A three-factor, three-level orthogonal experimental design was implemented to evaluate the influence of speed ratio, disc diameter, and disc inclination on three performance indicators: removal rate (Y1), breakage rate (Y2), and omission rate (Y3). The results showed that for Y1 and Y3, the primary influencing factors, in order of importance, were: disc diameter > bending angle > speed ratio; for Y2, the order was: speed ratio > bending angle > disc diameter. The optimal parameter combination was determined to be: 600 mm disc diameter, 20° bending angle, and 0.7 speed ratio. Field tests achieved a pull-out rate of 95.7%, a breakage rate of 3.2%, and an omission rate of 1.1%. These findings provide both theoretical and technical support for enhancing the efficiency of mechanized cotton stalk harvesting.

To address the lack of contact parameters between Corydalis tuber and mechanical, soil interfaces during planting, harvesting, and processing stages, this study calibrated discrete element simulation parameters for Corydalis tuber by combination of simulation tests and physical tests. 3D contour model of Corydalis tuber was obtained via 3D scanning technology, and a multi-sphere bonded particle model of Corydalis tuber was constructed by automatic filling method. Simulation tests were conducted with the restitution coefficient, static friction coefficient, and rolling friction coefficient between Corydalis tuber and Q235 steel, as well as between Corydalis tuber and soil, as independent variables. The rebound height, friction angle, and rolling distance were used as dependent variables. A second-order polynomial fitting method was applied to the experimental results. The actual test values were substituted into the polynomial equations to obtain simulation values for the contact parameters of Corydalis tuber with Q235 steel and soil, and these values were then validated against the experimental results. The findings indicate that the restitution coefficients of Corydalis tuber-Q235 steel and Corydalis tuber-soil were 0.728 and 0.44, respectively. The static friction coefficients of Corydalis tuber-Q235 steel and Corydalis tuber-soil were 0.41 and 0.76, respectively, while the rolling friction coefficients were 0.02 and 0.033, respectively. Under these conditions, the relative error between the simulation tests and physical tests was minimized. This study provides particle models and calibrated simulation contact parameters for mechanical processing tasks such as sowing, harvesting, and drying of Corydalis tuber.

To address the challenges of complex farmland environment, an intelligent obstacle avoidance control algorithm for agricultural unmanned aerial vehicles (UAVs) is developed. The objective is to solve the problem of efficient obstacle avoidance in farmland scenarios characterized by dense dynamic obstacles and variable terrain. In this article, a target detection algorithm based on improved YOLOv5 (You Only Look Once v5) is proposed, and an intelligent obstacle avoidance system is constructed by combining reinforcement learning path planning and adaptive motion control strategy. Ghost module is introduced to improve the lightweight of YOLOv5, and the design of CIOU (Complete Intersection Over Union) loss function is optimized, which improves the detection accuracy of the model for small targets and dynamic obstacles. Experiments show that the error of path planning is reduced to less than 2.1 meters, and the time consumption is reduced by about 35%. In addition, fuzzy logic controller is used to realize adaptive PID control, which further enhances the flight stability of UAV in complex environment. The results show that the improved algorithm has excellent performance in many typical farmland scenes. This study provides theoretical and technical support for autonomous flight of agricultural UAV in complex farmland environment.

To address the Autonomous navigation and operation requirements of agricultural robots in complex terrain environments of vineyards, this report proposes a point cloud Ground segmentation algorithm based on surface fitting, aiming to solve the problems of reduced segmentation accuracy and insufficient adaptability of traditional planar assumption methods in unstructured terrains such as sloped fields and ridge furrows. The core of the algorithm lies in adopting a point cloud representation method based on a non-uniform polar grid, which dynamically allocates face element sizes according to point cloud density and the width between vineyard ridges, effectively addressing the issues of point cloud sparsity and representability. Subsequently, the moving least square method is used to fit surface models. During the fitting process, strategies such as Gaussian weight function, cosine and sine basis functions, and set of orthogonal functions are introduced to shorten the algorithm’s running time and reduce computational complexity. The algorithm’s performance is evaluated on the public dataset KITTI and in real-world environments, and compared with algorithms such as RANSAC, GPF, R-GPF, and Patchwork. Experimental results show that the proposed algorithm outperforms other algorithms in both Precision and Recall. In practical environments, the algorithm can accurately and effectively segment complex vineyard environments, meeting the operational requirements of agricultural robots and providing technical support for the advancement of smart agriculture.

Accurate detection of apple stems is crucial for robotic cutting. This study proposed an improved YOLOv8-stem method for apple stem detection in overhead imagery under occlusion conditions. First, several im-provements were made to the YOLOv8 neural network: the conventional convolutional process within the in-termediate neck layer was substituted with the AK Convolution mechanism, a small object detection head was added, and ResBlock+CBAM attention mechanism was incorporated. Second, stem occlusion was determined by analyzing the positional relationship between the detected bounding boxes of stems and apples. The exper-imental results showed that compared to the original YOLOv8, this method improved apple stem detection ac-curacy by 6.0% (from 79.9% to 85.9%) and increased harvesting completeness from 84.2% to 93.2%.

To solve the problems of low precision in locating stem picking points and difficulty in recognizing occluded strawberry during the operation of strawberry picking robots, this paper proposed an improved YOLOv8-pose method for strawberry fruits identification and key points detection at the red ripe stage. Based on the YOLOv8-pose human posture estimation model, three categories (strawberry, stem, and picking points) were annotated. The acquired images were divided into training, validation, and test sets in an 8:1:1 ratio. In order to improve the feature extraction ability of the model for small targets, shuffle attention (SA) mechanism was added into the backbone network of YOLOv8-pose. Additionally, a comparative analysis was conducted to assess the impact of six attention mechanisms of CBAM (Convolutional block attention module), SimAM (Simple attention module), GAM (Global attention module), EMA (Efficient multi-scale attention), SK (Selective kernel attention), and SA on the detection results. Experimental results show that the proposed method can quickly and accurately detect strawberry fruits and key points for picking. The Precision (P), Recall (R), and mean average precision (mAP)50 values for both bounding boxes and key points based on SA mechanism were 99.7%, 100.0%, and 99.5% respectively, which were superior to the other attention mechanisms. Compared with YOLOv5-pose and YOLOv8-pose models, the improved model had the best P, R, and mAP50 values, and its memory usage was 6.4MB, which was also optimal. The improved method can provide crucial technical support for precise robotic strawberry picking.

The influence of the devices structure and operating parameters, along with the material properties of millet, on threshing and separation performance forms the theoretical basis for designing and researching a single longitudinal axial flow threshing and separation device specifically adapted to millet. Therefore, a theoretical model for grain threshing and separation in a single longitudinal axial flow threshing device was established based on variable mass theory. To validate the theoretical model, single-factor tests were conducted on the feeding rate, rotational speed, and water content of Longgu 31 millet. The error analysis between the experimental and calculated values indicates that within a moisture content range of 17.14% to 32.93%, feeding rates varying from 1 to 3 kg/s, and rotational speeds ranging from 700 to 1000 r/min, the R-squared values consistently exceed 0.97. This indicates an excellent fit of the theoretical model. The theoretical model will serve as a valuable reference for the design and investigation of the single longitudinal axial flow separation device.

Crop diseases significantly diminish agricultural production, resulting in economic losses. Early detection and species identification remain major challenges. This paper introduces a lightweight Convolutional Neural Network (LCNet) designed for the detection of corn diseases, including blight, common rust, and gray leaf spot, using an efficient, low-latency model. The suggested architecture consists of three convolutional layers, three pooling layers, and one fully linked layer. Experimental findings indicate that LCNet surpasses the pretrained architecture MobileNetV2, DenseNet201, and ResNet50, with an average accuracy of 94.65%. This method enables prompt disease identification, assisting farmers in averting significant crop losses while minimizing human labour in oversight and administration.

To improve the seed separation uniformity in the seed storage device of a wheat plot seeder, this paper investigates the key factors influencing its performance. First, the working principle of the seed storage device and the movement of seeds are analyzed, and the lifting mechanism of the seed storage tube is controlled by an electric pusher. Based on this analysis, the relevant parameter factors are identified. Next, EDEM software is used to simulate the seed storage process and determine the optimal combination of parameters. A three-factor, three-level central composite design experiment is conducted, with the inner diameter of the seed storage tube (D), the lifting height (L), and the lifting speed (V) as the test factors. The coefficient of variation of seed distribution uniformity (m1) is used as the performance index. The optimal values determined for the parameters are D = 0.06 m, L = 0.015 m, and V = 0.05 m/s. Finally, bench experiments are carried out to verify the results. The experimental findings show that the average coefficient of variation for seed distribution using the traditional lifting lever is 6.46%, while the optimized parameters reduce it to 4.12%, significantly improving seed separation uniformity and meeting the seeding requirements.

This study is based on the discrete element method to design a tracked precision solid particle pesticide applicator and establish a coupling model between the pesticide application device and particle swarm EDEM. Simulation shows that particle characteristics significantly affect the uniformity of pesticide application. The coefficients of variation for particles 1, 2, and 3 are 4.25%, 4.63%, and 5.57%, respectively, with relative deviations of 8.51%, 9.28%, and 11.33%. After response surface testing optimization, the measured coefficient of variation of the prototype was 8.25% and the relative deviation was 18.07%, both of which met industry standards.

Traditional rice seed classification methods rely on manual observation of morphological features, which are inefficient and limited in accuracy. To improve the efficiency and accuracy of rice seed classification, this paper proposes a deep learning-based rice seed classification method using the SE-ResNet network architecture. This architecture integrates SENet into ResNet, enabling the model to capture and learn sensitive differential features among rice seeds. Through comparative experiments, the classification accuracies of SE-ResNet, ResNet, and AlexNet on the rice seed dataset were 89.58%, 72.97%, and 76.35%, respectively. The results demonstrate that SE-ResNet significantly outperforms ResNet and AlexNet in classification accuracy, validating its superiority in rice seed classification tasks.

To improve the uniformity and stability of seed guidance in ordered seed flow and meet the requirements of precision sowing operations, a brush-type seed guiding device capable of constraining all degrees of freedom of the seeds was designed. Bench tests were conducted to optimize the factors affecting performance, namely the nut-bolt head spacing, the brush-to-housing gap, and the brush belt speed. Through single-factor experiments, the influence of each factor on seed guidance performance was clarified, and reasonable value ranges were determined. Combined with a response surface test, a multiple quadratic regression model was established to describe the relationship between these factors and the variation coefficients of pass rate, missed seeding rate, and seed spacing. The results show that optimal seed guidance performance is achieved when the nut-bolt head spacing is 39.15 mm, the brush-to-housing gap is 0.22 mm, and the brush belt linear velocity is 0.644 m/s. This study provides a theoretical foundation and data support for the development of precision sowing technology and its associated seed guiding devices.

The diamondback moth (Plutella xylostella) is a destructive pest that severely compromises Chinese cabbage production. Infestations caused by this pest significantly reduce both yield and quality, making efficient and accurate detection crucial for cultivation management. To address the challenges of detecting small targets and extracting phenotypic features in complex environments, this study proposes SAFF-YOLO—a YOLO11-based pest detection algorithm specifically designed for diamondback moths in Chinese cabbage fields. First, the loss function was refined to enhance the models learning capacity for pest samples, optimizing it for precision-driven bounding box regression. Second, Alterable Kernel Convolution (AKConv) was incorporated into the backbone network, strengthening feature extraction capabilities while reducing model parameters. Third, Single-Head Self-Attention (SHSA) was integrated into the C2PSA (Channel and Position Spatial Attention) module, enhancing the backbone networks feature processing efficacy. Fourth, the neck network employed Frequency-aware Feature Fusion (FreqFusion) as the upsampling operator, specifically designed for precise localization of densely distributed targets. Finally, the Feature Auxiliary Fusion Single-Stage Head (FASFFHead) detection module was implemented to boost multi-scale target detection adaptability. Experimental results demonstrate that SAFF-YOLO achieved detection metrics of 90.7% precision, 89.4% recall, and 92.4% mAP50 for diamondback moths in Chinese cabbage, representing improvements of 7.4%, 8.0%, and 8.4% respectively over YOLO11. With only 7.3 million parameters and computational cost of 12.8 GFLOPs, this corresponds to 60.1% and 40.7% reductions compared to the baseline model. These results confirm an optimal balance between model lightweighting and high detection accuracy. Under complex field conditions characterized by small and densely distributed targets, severe background interference, and intense illumination, SAFF-YOLO consistently demonstrates robust detection capabilities, effectively reducing both false negative and false positive rates while maintaining high operational robustness. This research provides a practical solution for real-time diamondback moth detection in field-grown Chinese cabbage.

This study investigates the excessive energy consumption of traditional furrowing cutters used in sticky, heavy soils of orchards located in hilly and mountainous regions. A novel furrowing cutter was designed, taking the wetland rotary bending cutter as a reference. The bending radius, bending angle, and alpha angle were selected as design variables. Quadratic orthogonal regression and discrete element method (DEM) simulations were employed to analyse tool–soil dynamic interactions and assess the influence of key parameters on furrowing power consumption. Response surface analysis revealed that at a bending radius of 30.02 mm, bending angle of 109.97°, and alpha angle of 51.74°, the optimised cutter reduced power consumption by 12.9%, from 0.85 kW to 0.74 kW.

Tiger-nut is widely cultivated in sandy soils across China, and the mechanization level for harvesting remains low. Harvesters are mainly medium- and small-sized tractor-drawn models, which exhibit weak driving capability, a large turning radius, and poor stability. This paper presents the design of a crawler-type tiger-nut harvester hydraulic-driven travel system, with the required hydraulic motor displacement of 44.65 mL·r-1 and hydraulic pump displacement of 44.58 mL·r-1. Experimental results from the harvester showed that the harvester’s linear travel deviation rate ranged from 1.19% to 2.10%, with an average travel speed of 1.08 m·s-1 and the average value of radius for forward left turn and forward right turn is 2740 mm and 2748 mm. The harvester can achieve bidirectional stepless speed regulation in the field, ensuring stable harvesting performance and meeting the design requirements for the tiger-nut harvester.

To address the low recognition accuracy and slow detection speed of cotton leaf pests and diseases in natural environments, a detection method based on an improved YOLOv5s model was proposed. The enhanced model integrates the Ghost module and the C3Faster module to increase inference speed and reduce model complexity, achieving lightweight performance without significantly compromising accuracy. To counteract the tendency of common cotton pest and disease features to be lost in complex natural scenes, a Coordinate Attention (CA) mechanism was introduced to improve the networks recognition and localization capabilities. The parameters, FLOPs, and weight file size of the improved model were reduced to 65.5%, 66.2%, and 67.1% of those of the original YOLOv5s model, respectively. On a self-built dataset, the improved YOLOv5s model achieved a mean average precision (mAP) improvement of 10.5%, 0.2%, and 0.4% compared to YOLOv4, YOLOv5s, and YOLOv7, respectively. The model was deployed on a Jetson Orin NX development board with CUDA acceleration, achieving a real-time detection speed of 76.3 frames per second.

To address the issue that existing seed-guiding devices struggle to meet the high-speed operational requirements of delta-row planters for dense maize planting, a seed-guiding device with air assistance and a guided groove was designed based on the principle of the brachistochrone. The overall structure and working principle of the device are described, and the curved segment of the seed guide tube was optimized using the brachistochrone principle while accounting for frictional effects. Computational fluid dynamics (CFD) simulations were conducted to analyse the flow field characteristics of the seed guide tube at inlet airflow velocities of 63.48, 60.64, 57.73, 54.69, 51.50, and 48.15 m/s. A multi-factor test was performed using chamber pressure and operating speed as test factors, with the qualified index of grain spacing and the coefficient of variation as evaluation metrics. Comparative tests were conducted using a traditional guided-groove seed guide tube and a brachistochrone-based seed guide tube without a guided groove. Results showed that the optimal parameter combination for the newly designed device was a chamber pressure of 3.124 kPa and an operating speed of 12.0 km/h. Under these conditions in the bench test, the qualified index reached 97.04%, and the coefficient of variation was 6.18%, outperforming the other two types of seed-guiding devices. These findings demonstrate that the seed-guiding device based on the brachistochrone principle can significantly improve the seeding quality of delta-row planters for dense maize planting under high-speed operation.

To address the lack of simulation parameters when applying discrete element simulations to guide the optimal design of grapevine burying machines, this study focused on the soil conditions in Xinjiang. The soil was divided into three layers, and the static and dynamic angle of repose characteristics of each layer were investigated using the Hertz-Mindlin with Johnson-Kendall-Roberts (JKR) contact model. First, the Plackett-Burman test was used to eliminate parameters that had no significant effect on the static and dynamic angles of repose. Then, the steepest ascent test was applied to narrow the parameter ranges, followed by the Box-Behnken test to develop regression models between the repose angles and the significant parameters. The results showed that the soil-soil restitution coefficient, soil-soil rolling friction coefficient, soil-steel static friction coefficient, and JKR surface energy were the key parameters influencing the static and dynamic repose angles. Experimental validation demonstrated that the average relative errors between the simulated and measured angles of repose using the optimized parameters were 0.92% and 0.32%, respectively, confirming the validity of the selected parameters. This study provides an important reference for the design optimization of grapevine burying machines and for discrete element simulation of other cohesive particulate materials.

Solid-liquid separation is essential for utilizing livestock and poultry manure, impacting greenhouse gas emissions and pollution control. This study aimed to address the high water content and low efficiency issues in screw extrusion solid-liquid separation technology. A screw separator was designed, and a Box-Behnken test scheme was used to optimize parameters with dairy manure as the test object. The study identified extender length, screw rotation speed, and counterweight mass as the influencing factors, with extrudate water content and solids extraction rate as evaluation indices. A quadratic regression model was established to describe the relationships between the influencing factors and evaluation indices. Optimal parameters were determined as: extender length=10 cm, screw rotation speed=55 r/min, and counterweight mass=34.73 kg. The predicted extrudate water content was 65.44 %, and solids extraction rate was 2.39 m³/h. Validation tests showed an average extrudate water content of 67.71 % and solids extraction rate of 2.10 m³/h, with relative errors of 3.3 % and 9.6 % respectively. The models reliability was confirmed, providing a reference for designing livestock and poultry manure screw extrusion separators.

For the automatic control of soybean harvester header height, this study uses Area array LiDAR for header height detection. An improved quartile range algorithm is used to dynamically remove outliers under crop residue interference. Linear, quadratic, and cubic nonlinear terrain fitting models are established based on the surface undulation characteristics of soybean fields. The Huber loss function is introduced to enhance the robustness of parameter estimation. The balance between model complexity and fitting goodness is quantified using Bayesian information criterion (BIC), and the model intercept term with the smallest BIC value is selected as the terrain reference height. Aiming at the hysteresis characteristics of valve controlled asymmetric hydraulic cylinders, a telescopic dual-mode transfer function model is established, and a Bang Bang switch lead compensation strategy with position threshold is proposed. By predicting the trend of terrain changes, the electromagnetic directional valve is triggered in advance when the height error of the header exceeds the set threshold, effectively reducing the system response delay. Field comparative experiments have shown that at a working speed of 1m/s, the automatic control mode significantly improves the uniformity of cutting compared to the manual mode. When the cutting threshold is set to 20, 25, and 30mm, the coefficient of variation of cutting height is reduced by 2.13%, 1.71%, and 0.55%, respectively. Moreover, the automatic mode maintains a gentle distribution characteristic within the threshold range of 15-35mm, verifying the strong robustness and control accuracy advantages of the designed system in complex farmland environments.

In the context of agricultural emissions in China, it is common for fields to be bordered by row windbreaks, which - when located downwind of emission sources - can complicate gas flux measurements. To address this challenge, the environmental adaptability of a laser-based detection system for quantifying gas emissions from agricultural sources was evaluated through a controlled gas emission field simulation experiment. Using methane as a representative gas, a flux measurement system based on open-path laser absorption spectroscopy was developed. The study employed an artificially simulated methane volatilization source and two measurement devices to conduct experiments under three conditions: an ideal environment, a laser path positioned downwind of the source, and a laser path set directly above the source. Results show that the standard deviations of the ratio QbLS/Q were 0.0277, 0.0283, and 0.0256, respectively. The corresponding maximum fluctuation amplitudes were 7.3%, 7.4%, and 5.9%. These findings suggest that for a row windbreak located downwind of an emission source, selecting an optimal measurement strategy - such as positioning the optical path above, across, or near the vertical downwind axis of the source - can minimize environmental interference and enhance the reliability of methane flux measurements in agricultural settings.

Due to their delicate and thin skin, ‘huangguan’ pears are very vulnerable to pressure and impact during picking, packing and transportation, which can cause bruising. Early detection of bruises allows for timely identification of affected fruits to reduce potential food safety risks. However, early bruises in ‘huangguan’ pears, particularly those that occur within the 30 minutes, often show no visible differences in external features compared to healthy tissue, making conventional techniques such as manual and machine vision sorting ineffective. Accordingly, a near-infrared (NIR) camera imaging technique combined with deep learning segmentation algorithm for early bruise ‘huangguan’ pears detection is proposed in this study. Firstly, a near-infrared camera imaging system is applied to collect early bruise images of ‘huangguan’ pears, and then a lightweight segmentation model based on the DeepLabV3+ architecture, referred to as MCC-DeepLabV3+ is presented. In the MCC-DeepLabV3+ model, MobileNetV2 is used as the backbone network, reducing the parameter size and enhancing deployment efficiency. Additionally, the coordinate attention (CA) mechanism is integrated into the shallow feature extraction and ASPP modules to improve the extraction of positional information across various features, minimizing the discrepancy between segmented areas and the actual bruised regions. Furthermore, a cascade feature fusion (CFF) strategy is incorporated into the encoder to reduce segmentation edge discontinuities and ensure effective multi-level semantic fusion, improving segmentation accuracy. The experimental results show that the proposed model has achieved a mIoU of 95.68%, and mPrecision of 97.58% on the self-built dataset of early bruising in ‘huangguan’ pears. Compared to benchmark models such as U-Net, SegNet, PSPNet and HRNet, the proposed model demonstrates superior segmentation performance, offering promising support for the development of nondestructive detection techniques for agricultural product quality.

This review paper provides a comprehensive analysis of the working principles of wind turbines, with a specific focus on experimental research studies for Vertical Axis Wind Turbines (VAWTs). Wind turbines are essential in harnessing renewable energy, and understanding their operational mechanisms is crucial for optimizing efficiency and performance. The paper outlines the basic working principles of both Horizontal Axis Wind Turbines (HAWTs) and VAWTs, with a deeper exploration of the latter due to its unique advantages in urban and low-wind conditions used in agriculture. The review critically examines recent experimental research on VAWTs, including various design modifications, aerodynamic performance studies, and energy efficiency improvements. Key parameters such as blade shape, turbine configuration, and site-specific factors are discussed, drawing on findings from experimental setups in laboratory and field conditions. The paper also highlights challenges in scaling VAWTs, including structural integrity, noise reduction, and cost-efficiency. Future trends and advancements in VAWT technology are considered, aiming to enhance their viability as a competitive solution for renewable energy generation in households, companies and agricultural farms. Cost-effectiveness, noise reduction, and structural durability remain significant barriers to the widespread adoption of VAWTs, despite promising advancements. This underscores the need for additional research into scalable designs, advanced materials, and optimal configurations for a variety of environmental conditions.

The selection of design, improvement, and utilization of seed planting, harvesting, and postharvest implements depends on the grain physical and mechanical property information of the specific crop type and variety. However, this information is lacking for wheat varieties produced in different regions of Ethiopia. The objective of the study was to generate data on the physical and mechanical properties of grains from selected wheat varieties. Three wheat varieties (Dandaa, Jalanne, and Kakaba) were selected. A factorial combination of 3 × 5 treatment levels was adapted with three replications. The variables studied were the grain size, shape, weight, bulk density, angle of repose, and coefficient of friction. The data were subjected to analysis of variance (ANOVA), and the Dunnett multiple range test was used to separate the means. Significance was accepted at a 95% confidence interval (p < 0.05). As moisture increased, the grains dimensions, shape, thousand kernel weight, coefficient of friction, and angle of repose all increased while bulk density decreased. The bulk density decreased from 0.73 to 0.54 g/cm³ as moisture increased from 8.50% to 28.50%. The study indicated large variations in the treatments used. The mean values for grain length, surface area, and volume increased from 5.786 ± 0.253 to 6.525 ± 0.361 mm, 38.514 ± 2.997 to 49.627 ± 3.201 mm2, and 22.531 ± 2.644 to 32.933 ± 3.201 mm3, respectively. Similarly, thousand kernel weight, repose angle, and friction coefficient increased from 26.70 to 42.00 g, 23.20 to 34.70°, and 0.4142 to 0.8391, respectively. Variations in grain properties indicate the necessity for diverse design and calibration criteria for seed planting, harvesting, and postharvest equipment tailored to different wheat varieties at different moisture levels.

The selection of design, improvement, and utilization of seed planting, harvesting, and postharvest implements depends on the grain physical and mechanical property information of the specific crop type and variety. However, this information is lacking for wheat varieties produced in different regions of Ethiopia. The objective of the study was to generate data on the physical and mechanical properties of grains from selected wheat varieties. Three wheat varieties (Dandaa, Jalanne, and Kakaba) were selected. A factorial combination of 3 × 5 treatment levels was adapted with three replications. The variables studied were the grain size, shape, weight, bulk density, angle of repose, and coefficient of friction. The data were subjected to analysis of variance (ANOVA), and the Dunnett multiple range test was used to separate the means. Significance was accepted at a 95% confidence interval (p < 0.05). As moisture increased, the grains dimensions, shape, thousand kernel weight, coefficient of friction, and angle of repose all increased while bulk density decreased. The bulk density decreased from 0.73 to 0.54 g/cm³ as moisture increased from 8.50% to 28.50%. The study indicated large variations in the treatments used. The mean values for grain length, surface area, and volume increased from 5.786 ± 0.253 to 6.525 ± 0.361 mm, 38.514 ± 2.997 to 49.627 ± 3.201 mm2, and 22.531 ± 2.644 to 32.933 ± 3.201 mm3, respectively. Similarly, thousand kernel weight, repose angle, and friction coefficient increased from 26.70 to 42.00 g, 23.20 to 34.70°, and 0.4142 to 0.8391, respectively. Variations in grain properties indicate the necessity for diverse design and calibration criteria for seed planting, harvesting, and postharvest equipment tailored to different wheat varieties at different moisture levels.

The selection of design, improvement, and utilization of seed planting, harvesting, and postharvest implements depends on the grain physical and mechanical property information of the specific crop type and variety. However, this information is lacking for wheat varieties produced in different regions of Ethiopia. The objective of the study was to generate data on the physical and mechanical properties of grains from selected wheat varieties. Three wheat varieties (Dandaa, Jalanne, and Kakaba) were selected. A factorial combination of 3 × 5 treatment levels was adapted with three replications. The variables studied were the grain size, shape, weight, bulk density, angle of repose, and coefficient of friction. The data were subjected to analysis of variance (ANOVA), and the Dunnett multiple range test was used to separate the means. Significance was accepted at a 95% confidence interval (p < 0.05). As moisture increased, the grains dimensions, shape, thousand kernel weight, coefficient of friction, and angle of repose all increased while bulk density decreased. The bulk density decreased from 0.73 to 0.54 g/cm³ as moisture increased from 8.50% to 28.50%. The study indicated large variations in the treatments used. The mean values for grain length, surface area, and volume increased from 5.786 ± 0.253 to 6.525 ± 0.361 mm, 38.514 ± 2.997 to 49.627 ± 3.201 mm2, and 22.531 ± 2.644 to 32.933 ± 3.201 mm3, respectively. Similarly, thousand kernel weight, repose angle, and friction coefficient increased from 26.70 to 42.00 g, 23.20 to 34.70°, and 0.4142 to 0.8391, respectively. Variations in grain properties indicate the necessity for diverse design and calibration criteria for seed planting, harvesting, and postharvest equipment tailored to different wheat varieties at different moisture levels.

Saffron is a high-value crop that requires low growing temperatures and cannot be cultivated in equatorial climates. To address this limitation, an IoT-based Intelligent Saffron Management System (IISMS) is proposed to simulate suitable growing conditions for saffron cultivation in equatorial regions. The IISMS remotely monitors key environmental parameters, and the collected data is displayed via a mobile APP over a Wi-Fi network. The cooling system is automatically activated when the temperature exceeds 10 °C, which is the critical threshold required during the flowering phase. Similarly, the irrigation system is triggered when soil moisture drops below 70%. By providing responsive and automated crop management, the IISMS optimizes growth conditions and improves yield quality. Experimental results showed that saffron cultivated under the IISMS was healthier, taller, exhibited more green shoots, and was generally stronger compared to saffron grown under standard room conditions in Malaysia.

In response to the speed control requirements of the threshing cylinder in combine harvesters, this paper analyzes the working condition characteristics of the threshing cylinder and establishes a dynamic model. A mathematical model for the electrically driven threshing cylinder is established, and an adaptive speed control strategy is proposed based on the identification of moment of inertia. Experimental results indicate that, the control accuracy and robustness of the system are enhanced. During the start-up process, the overshoot in speed and convergence time are reduced by 11.1% and 31.3%, respectively; when fluctuations occur in the feed rate of the threshing cylinder, the maximum speed fluctuation and adjustment time are reduced by 55% and 33.3%, respectively; when the moisture content of crops changes, the shortest convergence time and maximum fluctuation amplitude of the threshing cylinders speed are reduced by 57.7% and 18.8%, respectively. This research provides theoretical support and practical foundation for the intelligent control of electrically driven combine harvesters, demonstrating considerable application value.

To address the lack of contact parameters for yanhusuo during the mechanization process, its basic physical and contact parameters were derived through both theoretical calculations and practical measurements. Utilizing 3D scanning technology, a 3D model of yanhusuo was obtained. A discrete element model was then established using an automatic filling method. The key factors influencing the stacking angle of yanhusuo were identified using Plackett-Burman tests. The optimal ranges for these factors were determined through the steepest ascent experiment, and a quadratic regression model for the stacking angle was developed using response surface methodology (RSM) based on a rotational orthogonal experimental design. The optimized parameters - a restitution coefficient of 0.481, static friction coefficient of 0.508, and rolling friction coefficient of 0.029 - yielded a stacking angle of 21.23°. Experimental validation showed a relative error of just 0.86% between the simulation and physical tests. These results demonstrate that the discrete element model and calibrated parameters closely replicate real-world conditions, providing valuable insights for the mechanical design of yanhusuo planting and harvesting equipment.

This study investigates the nonlinear stability of flat-elliptical greenhouse skeletons under wind loads, taking into account initial imperfections.. A finite element model was developed in ABAQUS, and nonlinear buckling analysis was performed using the arc-length method. Eigenvalue buckling analysis was used to identify imperfection modes, and the effect of imperfection amplitude on bearing capacity was evaluated. Results showed that an increase in imperfection amplitude led to a reduction in ultimate bearing capacity, with a 461.2 N·m² capacity at L/300, and 520.26 N·m² at L/800. Initial imperfections were found to significantly influence the yielding stage. This study demonstrates that considering initial imperfections provides a more accurate assessment of the skeleton’s stability under extreme loads.

In response to the problems of low unloading height, small collection tank capacity, and high ear damage rate during grain collection in the fresh corn combine harvester, a large collection tank with buffering and shock absorption mechanism and high unloading height was designed based on the 4YX-6 fresh corn combine harvester, which is a fully hydraulic driven fresh corn combine harvester. The device mainly consists of a box body, a buffering and shock-absorbing mechanism, a grain unloading extension mechanism, an electro-hydraulic system, etc. According to the agronomic requirements for fresh corn cultivation, the volume of the grain collection tank is designed to be 10 m3. According to the height range of the grain receiving truck during the harvest of fresh corn, the unloading stroke height of the grain collection box is designed to be 2.9 m~4.4 m. To achieve synchronization of lifting and flipping actions during high-level unloading, a synchronous series hydraulic system is designed. Conduct force analysis on the drop of fruit ears to the bottom of the grain collection box and design a buffering and shock-absorbing mechanism. The designed grain collection box was installed on the 4YX-6 fresh corn combine harvester for field experiments, with ear damage rate and unloading time as performance indicators. The results showed that the ear damage rate ranged from 0.5% to 0.8%, the unloading time was from74 s to 126 s,and there were no safety issues such as deformation or tilting of the device during operation. The ear damage rate met the standards, and the working performance was stable, meeting the operational requirements.

Precision rice broadcasting technology plays a significant role in the mechanization of planting and yield enhancement, with seed friction and mechanical properties being critical factors in seeder design. In this study, experiments were conducted to evaluate the frictional and mechanical properties of different rice seed varieties. The results showed that higher seed moisture content led to reduced flowability and lower mechanical strength. When rice seeds underwent chitting treatment, their viability remained above 90% under external loads not exceeding 20 N or cutting lengths within 1.5 mm. This study provides a theoretical foundation and data support for the design and optimization of direct-seeding machinery, contributing to the advancement of rice direct-seeding technology and equipment.

Crop straw is a key feed raw material processed by straw mills, but traditional mills have been hindered bulky structures, high power consumption, and low productivity. Previous research has primarily focused on optimizing the hammer structure, often overlooking the baffle, despite its considerable weight and lack of specific design applications. This study addresses that gap by optimizing the baffle design of the HQ-800 straw mill, simplifying it using a double-baffle framework. The force analysis considered gravity, hammer centrifugal force, shaft centrifugal force, main shaft torque, and material impact. Deformation and stress nephograms revealed that the area between the hammer shaft holes required structural improvement. Two optimization schemes - flat and curved - were compared. The curved design reduced maximum deformation to 29.007 µm, compared to 52.009 µm for the flat design, making it the preferred approach. Key parameters, including cutting circle diameter, center distance, and baffle thickness, were optimized using a three-factor, three-level orthogonal test, resulting in preliminary values of 200 mm, 250 mm, and 12.5 mm, respectively. Subsequent Box–Behnken testing refined these parameters to optimal values: 209 mm, 256 mm, and 12 mm. Under these conditions, the straw mill achieved a productivity of 1673 kg/h and a power consumption of 10.42 kWh/t. Compared to the unoptimized design, the optimized mill reduced volume by 44.46%, increased productivity by 5.89%, and lowered power consumption by 14.10%, fully meeting the design objectives.

This study developed a lift-type winged chisel plow-straw-soil interaction model, and its validity was confirmed through multiple experimental indicators obtained from both soil bin tests and simulation experiments. The Discrete Element Method (DEM) was employed to analyze soil disturbance behavior and straw incorporation performance. Results showed that the plow tip of the lift-type winged chisel plow effectively sheared, loosened, and fractured the plow pan soil, while the soil-lifting plate pushed and turned the soil, promoting thorough mixing of straw and soil. Compared to traditional designs, the addition of wings increased the working width, and the soil-lifting plate significantly enhanced the frequency of straw–soil disturbance and soil fragmentation - resulting in a mixing rate of 42.98% and a uniformity rate of 92%.

During the vibration crushing of wheat bran, an impact-blocking effect occurred between the grinding media and wheat bran powder. Experimental results showed that the impact depth exhibited quadratic relationship with respect to particle size, while the acceleration and damping coefficients followed the order: fine powder > coarse powder > uncrushed bran > micro powder. Increasing the diameter of the grinding media enhanced the impact depth by 81.10% for uncrushed bran and by 40.41% for coarse powder, but reduced acceleration by 45.35% and 29.56%, respectively. Damping coefficients positively correlated with grinding media diameter but negatively with cylinder diameter. Material layer thickness demonstrated threshold-dependent bidirectional regulation, peaking at 40 mm, with maximum damping difference rates of 20.40% for uncrushed bran and 17.67% for coarse powder. Drop height linearly influenced the impact depth, while showing cubic relationships with both acceleration and damping. Both parameters were minimized at 500 mm. Sensitivity analysis identified grinding media diameter as the primary factor affecting damping coefficients, revealing the sensitivity hierarchy: grinding media diameter > drop height > material layer thickness > cylinder diameter. This study provided a theoretical foundation and experimental support for optimizing the vibration superfine crushing efficiency of wheat bran.

China annually produces over 210 million tons of rice straw; however, its utilization as feed remains low due to the scarcity of supporting equipment. In feed production, wrapping straw bales with plastic film is crucial for accelerating degradation. Given the diverse types of straw balers, a variety of wrapping machines are required. This study presents the design of an adjustable wrapping machine capable of handling straw bales with diameters ranging from 400 to 1200 mm and lengths between 500 - 1000 mm. Wrapping time, coefficient of variation, and elongation rate were selected as key evaluation indicators. Experimental results demonstrate that all performance metrics comply with industry standards for straw bale machinery, indicating the machines practical feasibility and reliability in agricultural applications.

The solar greenhouse is the main agricultural facility in northern China, providing a suitable environment for cultivating vegetables and flowers during the winter season. However, during the months of March and April, adjustments to the indoor temperature in northern Chinese solar greenhouses are necessary through the manipulation of upper and lower air vents. To investigate the impact of varying ventilation speeds on the thermal conditions within a solar greenhouse, Computational Fluid Dynamics (CFD) simulations were employed. The study assessed four different ventilation speeds on the air, soil, and wall temperatures within the greenhouse, assuming the absence of crops and disregarding humidity levels. The results showed that a wind speed of 0.5 m/s effectively reduced air temperature but had little effect on soil and wall temperatures; at wind speeds of 1.0 m/s and 1.5 m/s, the differences in air, soil, and wall temperatures inside the greenhouse were small. Considering economic benefits and crop growth conditions comprehensively, a wind speed of 1.0 m/s was identified as the optimal choice. This study provides data support and a theoretical basis for ventilation design in solar greenhouses.

This study uses high-speed photography to reproduce the vibration behavior of Camellia oleifera fruit and flower buds. Vibration energy is transmitted through the branches to break the balance of the Camellia oleifera branch system, causing the fruit and flower buds to exhibit anisotropic chaotic swings. Post-processing of images shows that the fruit detachment force ranges from 4.57 to 11.85 N, and the flower bud detachment force ranges from 0.68 to 3.24 N. At a vibration frequency of 12.98 Hz, the vibration amplitude has no significant effect on the detachment force of fruit and flower buds. The detachment time of Camellia oleifera fruit and flower buds decreases with the increase of vibration amplitude or excitation frequency. Through fitting and analysis of the collected data, the Fourier fitting curves and functions of the motion acceleration of oil-tea fruit and flower buds are obtained, with R² values of 0.83996 and 0.73718, respectively.

The objectives of the study were: to determine the ratio of electrical voltage and volume to the hydrodistillation rate, conductivity, energy, and yield. The method used is hydrodistillation of nutmeg leaves according to the treatment. The treatment of voltage and water volume were 3 levels each. Observations included voltage, current, temperature, and yield. The results showed that the hydrodistillation rate, electrical conductivity, energy, and oil nutmeg leaves yield were influenced by electrical voltage and solvent volume. The maximum oil yield was 1.07% at a voltage of 50 V and 750 ml of water.

Identifying dry-direct seeded rice seedlings provides valuable information for field management. To address the challenges of seedling detection in cold-region dry-direct seeded rice fields, this study proposes an enhanced YOLOv11n-DF model. Key innovations include: 1) integrating DSConv into the C3k2 module to optimize phenotypic feature extraction, and 2) employing the FASFF strategy to improve scale invariance in the convolutional head. Experimental results show that the improved model achieves an mAP50 of 96%, with high recall, precision, and a processing speed of 251.5 FPS, outperforming the original YOLOv11n by 5 percentage points in mAP50, and surpassing YOLOv7–YOLOv10 in detection accuracy. The proposed algorithm effectively addresses challenges such as seedling occlusion and non-uniform distribution, offering a robust solution for automated seedling monitoring in precision agriculture.

Based on the agronomic characteristics of pineapple fertilization and the challenges posed by moist fertilizer particle adhesion, an interactive gear fertilizer discharger was developed. This device utilizes a pair of continuously meshing, counter-rotating gears to achieve centralized and interactive mixing of fertilizer. Through theoretical analysis, the fertilizer discharge volume and maximum operating speed of the discharger were determined. In the experimental design, the pressure angle and helix angle of the fertilizer wheel were selected as test factors, while the coefficient of fluctuation in fertilizer uniformity served as the test index. A single-factor test was conducted to analyze the influence of each factor on the index and to determine the appropriate range of values. Subsequently, a quadratic general rotary combination design was used to optimize the fertilizer wheel parameters. The results indicated that both the pressure angle and helix angle had a significant effect on the coefficient of fluctuation of fertilizer uniformity (P<0.01). The lowest fluctuation coefficient, 11.82%, was achieved when both angles were set to 30°. A simulation verification test yielded a result of 11.92%, with a relative error of 0.85% compared to the theoretical value. Bench tests conducted under optimal parameters showed a relative error of 2.77% between the experimental and simulated values. Furthermore, the average error between the measured fertilizer discharge flow rate and the theoretical value under varying speeds was 3.75%. No fertilizer adhesion was observed on the surface of the discharge wheel, demonstrating that the system enables fine regulation of fertilizer output while effectively mitigating fertilizer adhesion.

In response to the issues faced by conventional combine harvesters threshing drums, such as the difficulty in maintaining stable speed due to load fluctuations and torque disturbances, as well as the limited speed adjustment range to adapt to changes in crop varieties and properties, this study proposes a novel dual power source threshing drum (DPSTD). By incorporating a power coupling device and a motor into the existing mechanical structure, the DPSTD utilizes the flexible speed and torque output capabilities of the motor to achieve a wide speed adjustment range and enhance its resistance to interference. A robust model predictive control strategy is designed based on finite time disturbance observer (FTDO) to compensate for torque under load fluctuations. Simulation results demonstrate that the DPSTD can achieve rapid and accurate speed regulation. Moreover, under torque disturbances and load mutations, the DPSTD with the torque compensation control strategy, can reduce the speed error by 73.90%. This significantly improves the speed tracking performance of the threshing drum, exhibiting good anti-interference capabilities. Finally, the superiority of the DPSTD is validated through bench tests, confirming its effectiveness and feasibility and providing new insights for control research on threshing drums.

This study used SolidWorks to create a chili harvester model, 3ds Max to create planting terrain and chili plant models, and developed a virtual simulation platform for chili harvesting experiments based on Unity 3D. Finally, the feasibility of the virtual simulation platform was confirmed through human-machinery interaction effects and virtual simulation harvest experiments, which can provide reliable training and research data, solve the drawbacks of on-site training and testing, and contribute to the sustainable development of Chinas chili industry.

The article addresses the development of foundational principles for improving the quality of plant product preservation. The application of the proposed technologies enables a reduction in nutrient losses in raw plant materials during storage. This assertion is supported by the results of both theoretical and experimental studies on the physical and mechanical properties of plant materials, as well as on the energy, operational, and technological performance of the equipment, including its structural design parameters and modes of operation. The use of the proposed technology enhances nutrient preservation by preventing cell structure damage and preserving plant juices, while also minimizing internal voids and reducing the amount of free air within the compacted monolith. Consequently, this leads to a decrease in the cost of dietary components. The implementation of the proposed technological process in haylage production significantly reduces nutrient losses during storage. Furthermore, using plant raw materials stored with this method results in lower milk production costs compared to traditional storage techniques.

In the conditions of intensively developing digital technologies, agriculture is also an active environment for their application. The article demonstrates some of the capabilities of the intelligent Scanfield-5S system and the integrated DSC - "Digital Soil Cube" for assessing the condition of the soil. The DSC method uses non-contact measurement of electrical conductivity (ECa) up to 0.4m depth in the soil. The study was usually conducted after harvest and before agricultural operations for the next crop. All collected ECa data are georeferenced. An adaptive soil sampling scheme was applied, which is specific for a given field. The number of sampling sites was determined after applying a graph-analytical method. A high confidence probability (over 80%) was obtained from the ECa data, which is a confirmation of the suitability of the method. Analyses were performed for bulk density (BD), relative humidity (dW), clay content (Clay), organic matter (OM) and activated carbon (C_(act.)) in the soil. The presented results characterize the soil as homogeneous with relatively good biological indicators (OM and C_(act.)). The adaptive soil sampling scheme and the obtained regression models for the soil parameters are specific to the studied field. The regression models for the observed parameters are linear and are presented through spatial resolution maps. The Scanfield-5S system provides solutions such as variable rate maps (VRA), soil carbon prediction, and overall soil health assessment. The digital soil model created using the DSC method is specific to the field under study, but has the potential for universality.

This study used eXtreme Gradient Boosting (XGBoost) to predict the drying behavior of Boesenbergia rotunda (L.) under hot air and ultrasonic vibration. A dataset of 73 samples with temperature, time, and vibration as inputs was used. The model achieved high accuracy with R² = 0.99 and RMSE = 8.71 on test data. SHAP analysis revealed that ultrasonic vibration improved moisture reduction, especially during longer drying times. These results demonstrate the effectiveness of combining XGBoost and SHAP for understanding and optimizing complex drying processes in food and agricultural engineering.

An accurate method for determining palm fruit maturity is needed by the palm oil industry to increase productivity and quality of crude palm oil. This study developed a portable instrument based on the AS7265x sensor (410–940 nm) to determine oil palm maturity accurately. Reflectance from 270 oil palm fruits at various maturity were measured, pre-processed, and classified into three maturity levels using PCA. PCA with SNV pre-treatment explained 97% of the variance. Classification was validated using SVM, RF, and KNN, with KNN achieving 100% accuracy. The instrument with SNV-PCA-KNN method has the potential to be used for oil palm fruit maturity classification.

The study investigates the vibration dynamics of the integrated shallot harvesting machine to support design refinement, aiming to improve operational efficiency, harvesting performance, and packaging for transportation. The investigation is based on an existing integrated machine developed under a Vietnamese State-Level Scientific Research Project. A dynamic model with 17 degrees of freedom was developed to comprehensively represent the system’s motion, including the tractor, harvester, harvesting head, coupling joint, and eight wheels. Nonlinear soil characteristics were modeled using the Bekker terrain model, with specific soil parameters measured in Vietnam fields. Simulation results show notable vibrations in the integrated shallot harvesting machine and the harvesting head. At the beginning of the harvesting phase, the head experiences large amplitudes and high accelerations, which may damage shallot bulbs. The study proposes technical solutions such as optimizing damping coefficients, adjusting system stiffness, or incorporating active control systems to maintain stable harvesting depth and improve harvesting quality. The results provide a foundation for further refinement of integrated shallot harvesting machine design.

To address the issue of height disparity between the output device of the tray placement machine and the seedbed in stacked-tray germination under enclosed conditions - which can lead to vibration and displacement when trays are placed - a slide-type output device with a buffering function equal to the height difference was designed to ensure gentle placement of seedling trays. A three-factor, five-level quadratic orthogonal rotational combination test was conducted using Adams software for simulation analysis. The test factors included the horizontal inclination angle of the tilting frame, the horizontal inclination angle of the sliding plate, and the speed of the seedling tray pusher. Evaluation indices included the qualification rate of tray spacing, the qualification rate of alignment, and the efficiency of tray placement. Test results were analyzed using Design Expert 13 software, and multi-objective parameter optimization was performed. The optimal parameter combination was determined as follows: a tilting frame inclination angle of 15°, a sliding plate inclination angle of 18°, and a tray pusher speed of 0.16 m/s. Field trials conducted to validate these parameters resulted in a 96.11% qualification rate for tray spacing, a 95.89% alignment qualification rate, and a tray placement efficiency of 714 trays per hour - within 1% of the theoretical optimal value. These findings provide a theoretical foundation for the smooth and efficient operation of automatic seedling tray placement machines.

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.

Aiming at the problems that the soil covering machine of the seeder is prone to, such as uneven soil covering thickness and seed position deviation, which affect the sowing quality under sandy loam soil conditions, an innovative disk-type soil covering device based on the bionic shape of the claw legs of sand crabs has been optimized and designed. In this study, the contour features of the pincers of sand crabs were extracted using MATLAB software and applied to the tooth profile design of the soil cover plate, aiming to improve their operational performance in sandy loam soil. This study analyzed the operation mechanism of the bionic soil covering device based on the characteristics of sandy loam soil and determined the key parameters affecting the soil covering effect, including the installation deflection angle, installation inclination angle and the distance between the openings at the back of the soil covering tray. Taking the qualified rate of soil cover thickness and the standard deviation of seed lateral displacement as evaluation indicators, and with the help of EDEM discrete element simulation technology, the interaction model of sandy loam - seed - bionic soil cover device was constructed, and the single-factor and Box-Behnken response surface experimental designs were systematically carried out. Through simulation analysis and optimization, the optimal parameter combination of the soil covering device suitable for sandy loam soil was determined as follows: installation deflection angle 21.3°, installation inclination angle 20.1°, and the opening spacing behind the soil covering tray 100.0mm. It has laid a foundation for the research on improving the uniformity of soil covering and the straightness of seed rows in the soil covering device of sandy loam soil seeders.

Fruit instance segmentation algorithms are critical for target localization in fruit-picking robots, enabling accurate area estimation of fruits within images. However, existing methods often face limitations such as high computational cost, poor adaptability to low-power devices, and reduced detection performance in complex environments. To address these challenges, YOLOSeg-Jujube was proposed - an improved instance segmentation algorithm based on YOLOv8 - validated on a jujube image dataset annotated with bounding polygons. The architecture of YOLOSeg-Jujube was optimized through ablation studies to identify the most effective structure. The final network design includes Focus, CBS, Conv4cat, SPD, and SPPFr modules as the backbone, and a YOLOv8 head incorporating the SIoU loss function. Compared to YOLOv4-tiny, YOLOv5n, YOLOv7-tiny, and YOLOv8n, YOLOSeg-Jujube achieves reductions in parameter size of 62.8%, 9.8%, 73.9%, and 23.6%, respectively. The model achieves 83.5% B_mAP and 83.2% S_mAP, outperforming mainstream YOLO variants in both segmentation accuracy and area estimation of target objects. YOLOSeg-Jujube is robust, fast, and computationally efficient, making it suitable for deployment on resource-constrained platforms. Furthermore, it demonstrates strong potential for recognizing ripeness stages of fresh jujubes, providing technical support for intelligent harvesting systems in the field.

Intelligent and accurate shelling technology is essential for improving the quality of clam products. To enable the rapid and precise localization of clam meat in Ruditapes philippinarum (with half-shell) on an automated processing line, an improved clam meat detection algorithm - EET-YOLOv5, based on YOLOv5s - is proposed. This algorithm enables real-time detection and localization of clam meat on the production line. It integrates the Efficient Local Attention (ELA) mechanism to enhance target localization, adopts the EIoU loss function to reduce bounding box regression error, and replaces the original detection head with a TSCODE decoupled head to improve detection accuracy. The algorithm achieved a Precision of 93.03%, Recall of 97.03%, and mean Average Precision (mAP) of 93.55%, with a detection speed of 13.3 ms. Compared to YOLOv4, Faster R-CNN, SSD, and the standard YOLOv5 series, EET-YOLOv5 demonstrated superior performance. It was deployed on a test workbench for positioning experiments, achieving an average response time of 1.8 seconds and a positioning success rate of 92.7%, indicating its suitability for automated clam shell-meat separation production lines.

This paper presents a comprehensive analysis of the global state of research and technological advancements in irrigation systems for agricultural crops, with a focus on the development of an energy-independent subsystem designed by the authors – SEMSI (Self-sustaining Energy Mobile Subsystem for Irrigation). SEMSI operates using renewable energy sources such as solar and wind power and is intended for use in areas lacking access to the electrical grid but with available water resources. To support the conceptualization and future optimization of this subsystem, the study outlines key principles of irrigation, reviews existing technologies and their performance, and analyses the functionality of both traditional and modern irrigation systems, including gravity, sprinkler, and drip methods. This documentation lays the groundwork for further development of the SEMSI system and serves as a reference base for theoretical modeling and experimental validation in future research stages.

In order to obtain accurate contact parameters for foxtail millet seeds in discrete element method (DEM) simulations, this study selected the Jinmiao K10 variety as the research object. Physical experiments and DEM simulations were integrated to calibrate the contact parameters. First, the restitution coefficient, static friction coefficient, and rolling friction coefficient between foxtail millet seeds and polylactic acid (PLA) material were measured using methods such as free-fall and inclined plane tests. Subsequently, based on the experimentally measured repose angle of 21.93°, a Plackett–Burman design was employed to screen the key parameters significantly influencing the repose angle. These included the inter-seed static friction coefficient, rolling friction coefficient, and restitution coefficient. The steepest ascent test was then conducted to determine the direction of parameter optimisation. A quadratic regression model was established using the Box–Behnken response surface methodology to determine the optimal combination of contact parameters: restitution coefficient of 0.512, static friction coefficient of 0.22, and rolling friction coefficient of 0.041. These parameters were imported into EDEM software for simulation validation. The simulated repose angle is 22.2°, with a relative error of 1.23%, demonstrating high accuracy and practical applicability of the calibrated parameters. This study provides reliable simulation parameter support for the design and optimization of critical components in foxtail millet seeding machinery.

Multi-blade centrifugal fans are regarded as an important direction for agricultural cleaning fans due to the advantages of compact structure, low noise and high efficiency. In view of the insufficient aerodynamic performance of traditional centrifugal fans in combine harvester cleaning devices caused by straight-blade structures, as well as the lack of design theory for multi-blade centrifugal fans, this study focused on a multi-blade centrifugal fan with wing-shaped blades. Using a combination of CFD numerical simulation and bench tests, the influence of the volute width-to-diameter ratio on fan performance and the internal flow field was investigated. The results showed that at a rated speed of 1000 r/min, the optimal volute width-to-diameter ratio was 1.3. Compared with the prototype fan, the optimized fan achieved a 7.13% increase in efficiency, a 6.53% increase in air volume, and a 16.63% improvement in air distribution uniformity. In addition, the internal flow of the optimized fan was improved, with reduced turbulence intensity in the tongue region of the volute. Furthermore, a volute width-to-diameter ratio–speed–air volume function model was established using MATLAB, providing a theoretical basis for the selection and design of high-performance multi-blade centrifugal fans.

To enhance the operational efficiency and structural design accuracy of rock-picking machinery, this study proposes a Discrete Element Method (DEM)-based approach for the optimization of the lifting shovel in the 1JS-200 rock picker. A coupling simulation model of the shovel-soil-rock system was established to analyze the dynamic interaction mechanisms between the shovel and mixed granular media. Key parameters influencing excavation performance-including forward speed, digging depth, and shovel angle-were optimized using a Box-Behnken Design (BBD) response surface methodology. Regression models were constructed for torque and rock excavation efficiency (REE), and the optimal combination was determined to be a forward speed of 0.5 m/s, a digging depth of 170 mm, and a shovel angle of 40°, under which the predicted REE reached 88.1% with a torque of approximately 386 N·m. To validate the simulation results, field tests were conducted under optimal parameter conditions. The experimental results showed that REE values fluctuated within 2%, and torque errors remained within 4% of the predicted values, confirming the accuracy and applicability of the model. This research provides a practical, data-driven design method for rock-picking implements and offers theoretical and technical support for improving rock separation efficiency, reducing structural wear, and advancing the intelligent development of agricultural machinery.

To enhance the operational efficiency and structural design accuracy of rock-picking machinery, this study proposes a Discrete Element Method (DEM)-based approach for the optimization of the lifting shovel in the 1JS-200 rock picker. A coupling simulation model of the shovel-soil-rock system was established to analyze the dynamic interaction mechanisms between the shovel and mixed granular media. Key parameters influencing excavation performance-including forward speed, digging depth, and shovel angle-were optimized using a Box-Behnken Design (BBD) response surface methodology. Regression models were constructed for torque and rock excavation efficiency (REE), and the optimal combination was determined to be a forward speed of 0.5 m/s, a digging depth of 170 mm, and a shovel angle of 40°, under which the predicted REE reached 88.1% with a torque of approximately 386 N·m. To validate the simulation results, field tests were conducted under optimal parameter conditions. The experimental results showed that REE values fluctuated within 2%, and torque errors remained within 4% of the predicted values, confirming the accuracy and applicability of the model. This research provides a practical, data-driven design method for rock-picking implements and offers theoretical and technical support for improving rock separation efficiency, reducing structural wear, and advancing the intelligent development of agricultural machinery.

During the paddy rice drying process, the uneven spatial distribution of pore spaces within drying chambers poses a significant challenge to accurate porosity characterization and results in inefficient energy utilization. To address this issue, this study proposes a porosity prediction model based on Support Vector Regression (SVR), aimed at effectively monitoring porosity variations during drying and enhancing energy efficiency. Using MATLAB based image processing, the porosity of paddy rice was quantitatively extracted. A Response Surface Methodology (RSM) was then employed to analyze the influence of geometric characteristics, moisture content, and grain bulk height on porosity during drying. To further improve the predictive performance, the SVR model was optimized using the Snake Optimizer (SO) algorithm. The resulting SO-SVR model was evaluated against porosity values derived from image analysis. Experimental results demonstrate that the SO-SVR model achieves high accuracy, with a Root Mean Square Error (RMSE) of 0.0095 and a coefficient of determination (R²) of 0.9913. Compared to standard SVR and BP neural network models, the proposed model reduces RMSE by 0.0867 and 0.1663, and increases R² by 0.0449 and 0.1102, respectively. These findings indicate that the SO-SVR model provides a reliable and efficient approach for predicting paddy rice porosity during drying, offering valuable support for energy-saving and intelligent drying system design.

To address the limitation of existing agricultural unmanned plant protection equipment in perceiving crop growth status in real time during the maize seedling stage, this study proposes a crop row extraction method based on image processing. A crop semantic segmentation network was developed using the UNet framework, with VGG19 as the encoder and transposed convolution as the decoder. Model testing demonstrated that the segmentation network achieved accuracy rates of 0.9865 on the training set and 0.9864 on the validation set, with corresponding loss values of 0.0254 and 0.0270. In continuous processing scenarios, the average time for semantic segmentation per image was 120 milliseconds, while crop row extraction required 23 milliseconds.

As an economic crop of Rosaceae family, strawberry has the advantages of short reproductive cycle, wide ecological adaptability and significant economic benefits, and its planting industry has been rapidly developed in recent years. Aiming at the low efficiency and high labor cost of traditional manual picking detection methods in the intelligent transformation of strawberry industry, this study innovatively proposes a strawberry fruit intelligent detection system based on YOLOV8N. By introducing RFAConv dynamic sensory field convolution, SENet channel attention mechanism and InceptionNeXt lightweight structure, combined with Wise-IoU loss function and DIoU-NMS post-processing algorithm, the synergistic enhancement of detection accuracy and computational efficiency is realized. The ablation experiments show that the improved model has a precision rate of 95.92%, a recall rate of 95.45%, and a mAP50 of 98.29% on the strawberry dataset, which are 4.14%, 3.31%, and 1.55% higher than that of the baseline model, respectively, while the number of model parameters is compressed to 5.17 M (a reduction of 12.96%). This research can provide technical support for intelligent strawberry picking.

To investigate the effects of different fertilization treatments on rice growth, nutrient uptake, and nutrient loss in paddy fields, the study was conducted in Jiansanjiang Qixing Farm, Company 36, Jiamusi City, Heilongjiang Province as the experimental site. A total of six treatment groups (G1-G6) were set up with different fertilization methods and fertilizer application rates. The data demonstrated that the number of tillers increased by 61.4% in G2 and 80.4% in G3 compared to G1. The number of tillers in G6 was 489×104/hm2, which decreased by 13.6% compared to G3. At tillering stage, the nitrogen content of G1, G2 and G3 stems was 2.08, 2.19 and 2.51%, respectively. The cumulative loss of potassium in paddy fields was 0.19 kg/hm2, 0.59 kg/hm2 and 0.41 kg/hm2 for G1, G2 and G3, respectively. The results indicated that the use of mechanical side deep fertilization could bring many advantages to rice compared to the traditional manual fertilization method, including an increase in the number of tillers and plant height, as well as an increase in the leaf area index and dry material accumulation. At the same level of fertilization, the mechanical side deep fertilization method resulted in less ammonia volatilization, which would contribute to reducing ammonia volatilization from the paddy field and allow more nitrogen to be stored by the paddy field. In addition, the cumulative loss of runoff from the paddy field was reduced by the mechanical side deep fertilization treatment compared to manual fertilization. The study provides a useful reference for optimizing rice cultivation techniques to improve yield and reduce nutrient losses.

This study presents the development and evaluation of an enhanced, modular, IoT-based hydroponic system for indoor seedling production. Designed to be low-cost and scalable, the system integrates automated lighting, irrigation, and environmental monitoring with cloud-based control. Its performance was validated using two tomato varieties, achieving 94–96% germination without added nutrient solution. A cost-benefit analysis indicated a payback period of under two years. While promising, the system has not yet been tested with other plant species, substrates, or nutrient solutions. Future research will focus on these variables to assess broader applicability and optimize performance.

Deep tillage is capable of penetrating the tough subsoil layers, while vertical rotary tillage improves soil aggregate structure and moisture retention. In this study, an innovative deep vertical rotary tiller was engineered. Utilizing ANSYS Workbench, stress simulation on the blade was conducted. The EDEM software was employed to explore how the machines forward speed and the blades rotational speed impact operational quality. Field trials of the developed prototype revealed that the tillage depth, tillage depth stability coefficient, and soil fragmentation rate all complied with the national standard GB/T 5668-2017 for rotary tiller testing. Comparative analysis with the current vertical rotary tiller confirmed that the tiller developed in this study exhibited superior performance, thereby facilitating the adoption and technological evolution of vertical rotary tillers.

In this study, a non-invasive experimental method based on the shell temperature field to invert the internal steam pressure is proposed for the distribution characteristics of saturated steam pressure in the mill chamber of the heat mill and its dynamic influence on the feed of fiber materials. The surface temperature field of the grinding chamber shell was measured by a high-precision infrared thermometer. Combined with the inverse heat conduction problem model and SteamTab software conversion, the cross-scale mapping relationship of shell temperature-internal pressure was constructed, and the pressure distribution cloud map was generated. The experimental materials were mixed wood chips with a moisture content of 45 ± 2 %. After preheating and cooking ( 160 ° C, 3 min ), they were put into the grinding chamber to analyze the coupling effect of the pressure field and the tooth shape parameters of the grinding disc. The results show that the saturated steam pressure in the grinding chamber is significantly non-uniformly distributed along the radial direction, and the pressure gradient is mainly driven by the gradient change of the tooth shape parameters of the grinding disc. A solution to the problem of blocking backflow and excessive feed caused by non-uniform distribution of steam pressure is proposed in combination with the tooth profile parameters of the grinding plate.

It is difficult to accurately calculate the mutual force and seedling loss law between seedling needles and seedlings when conducting simulation analysis of cutting and transplanting rapeseed blanket seedlings with a transplanting machine due to a lack of reliable discrete element simulation parameters for rapeseed blanket seedlings. The physical and contact parameters of rapeseed blanket seedlings are calibrated in this study using the EDEM discrete element method. The Hertz Mindlin with JKR model in EDEM software was used to simulate the shear force of rapeseed blanket seedlings after conducting experiments to determine the density, shear modulus, Poissons ratio, friction coefficient, and collision recovery coefficient between seedlings and steel materials. The surface energy parameters, recovery coefficient, static friction coefficient, and rolling friction coefficient between seedlings were used as experimental variables, with the maximum shear force of seedlings serving as the response value. Using Box Behnkens response surface optimization method, multiple regression fitting analysis was performed on the experimental results to obtain the regression model of seedling shear force, which was then optimized to obtain the parameter optimization solution for rapeseed blanket seedlings. Finally, the collision recovery coefficient was calculated to be 0.6. The ideal contact characteristics for seedlings are a static friction coefficient of 0.8, a rolling friction coefficient of 0.3, and a JRK surface energy of 12.3 J/m2.

Classification of barley plants plays a crucial role in understanding barley varietal diversity and breeding. Traditional classification methods rely on expert experience and require significant manual effort. With the rise of deep learning based on machine vision technologies, particularly the emergence of transfer learning, the issue of model overfitting on small datasets has been mitigated, leading to enhanced generalization capabilities. This study employs a self-constructed barley plant image dataset to compare five state-of-the-art deep learning models, while analyzing the impacts of various factors - including image resolution and training-test split ratio - on classification accuracy. The results indicate that the DenseNet model achieves the best classification performance at an input resolution of 512×512 pixels, with an accuracy of 96.02%. Increasing the proportion of training data further improved performance, with the 80%:20% training-test split ratio yielding optimal results across all five models. Transfer learning models outperform training from scratch, with EfficientNet-v2 achieving the highest accuracy of 98.86%. Additionally, gradient-weighted class activation mapping (Grad-CAM) was utilized to generate heatmap visualizations of the decision-making processes in each transfer learning model. By applying deep learning for barley plant classification and selecting the optimal model, this research provides a reliable technical solution for barley variety identification and classification.

Classification of barley plants plays a crucial role in understanding barley varietal diversity and breeding. Traditional classification methods rely on expert experience and require significant manual effort. With the rise of deep learning based on machine vision technologies, particularly the emergence of transfer learning, the issue of model overfitting on small datasets has been mitigated, leading to enhanced generalization capabilities. This study employs a self-constructed barley plant image dataset to compare five state-of-the-art deep learning models, while analyzing the impacts of various factors - including image resolution and training-test split ratio - on classification accuracy. The results indicate that the DenseNet model achieves the best classification performance at an input resolution of 512×512 pixels, with an accuracy of 96.02%. Increasing the proportion of training data further improved performance, with the 80%:20% training-test split ratio yielding optimal results across all five models. Transfer learning models outperform training from scratch, with EfficientNet-v2 achieving the highest accuracy of 98.86%. Additionally, gradient-weighted class activation mapping (Grad-CAM) was utilized to generate heatmap visualizations of the decision-making processes in each transfer learning model. By applying deep learning for barley plant classification and selecting the optimal model, this research provides a reliable technical solution for barley variety identification and classification.

In sea cucumber production lines, manual sorting, de-sanding, and organ removing operations still predominate, severely limiting production efficiency and posing sig-nificant risks to product consistency and hygiene. This paper introduces a novel piece of equipment for sea cucumber sorting, de-sanding, organ removing, and washing. Em-ploying an innovative process of "automatic sand mouth removal + high-pressure water jet washing + hook-assisted organ removal," the equipment adapts well to sea cucum-bers of various sizes and conditions, offering high versatility and facilitating an auto-mated, efficient, and standardized processing workflow. Moreover, due to the high sensitivity to the sea cucumbers external shape and posture and the complexity of the background caused by residues during the de-sanding and organ removing process, which complicates the recognition of the sea cucumbers sand mouth, this study pro-poses a sand mouth recognition algorithm for complex backgrounds. Utilizing tiered recognition technology, the algorithm precisely locates the sea cucumbers sand mouth following the extraction of its position through artificial intelligence, significantly en-hancing the success rate of recognition. Experimental results indicate that the prototype of this equipment increases the speed of de-sanding and organ removing by 107.8% compared to existing integrated sea cucumber washing and cutting equipment. The cleaning effectiveness is excellent, with a high sea cucumber cleaning acceptance rate of 91%, which is an increase of 6.125% over manual sorting, de-sanding, and organ re-moving methods, and the work efficiency has increased more than ninefold, achieving the anticipated outcomes.

To address the challenges of low mechanization in harvesting and the high damage rate of fresh corn, this paper proposes a cutter-stalk pulling roller type tassel picking mechanism designed to achieve low-damage tassel picking and fresh corn harvesting. Using high-speed camera technology and pressure testing, a kinematic analysis of the tassel picking process was conducted. A three-factor, three-level orthogonal experimental design was employed to systematically investigate the effects of forward speed, stalk-pulling roller rotational speed, and cutter clearance on operational quality. The results showed that the picker maintained the force at the bottom of the corn cob below 35 N, effectively reducing mechanical damage. Under the conditions of a forward speed of 0.40 m/s, stalk-pulling roller speed of 420 r/min, and cutter clearance of 34 mm, the cob damage rate was 0.05%, and the impurity rate was 0.62%. These findings provide a valuable basis for the optimization and further improvement of single-row fresh corn cob picking cutters.

In the intelligent transformation of global animal husbandry, precise calf feeding faces challenges like arching and flow fluctuations during milk replacer delivery, leading to inaccurate feeding, diarrhea and higher costs. This study aims to enhance the precision and stability of milk replacer delivery via structural innovation. Through shear tests, the internal friction angle of milk replacer was measured at 23.76° and the external friction angle at 23.97°. Combined with discrete element simulation and orthogonal experiments, the scraper conveyor device was optimized. A particle model of milk replacer was established using the discrete element method to analyze the effects of blade curvature, number of blades and guide plate tangent angle on conveying performance. Optimal parameters were determined through orthogonal experiments. Results showed that the guide plate tangent angle most significantly affected conveying rate, with peak efficiency at 45°. The number of blades was the main factor for operational stability, with 8 blades reducing delivery fluctuations. The optimal combination was found to be curved blades, 8 blades and a 45° guide plate tangent angle. Validation tests showed a stable conveying rate of 7.50 - 8.48 g/s, a standard deviation below 0.43 g, and operational stability of 71.80% - 75.96%, effectively solving arching and flow fluctuation issues. This study offers theoretical support for developing precise powder delivery equipment and core technological support for domestic smart feeding equipment. It directly helps reduce calf morbidity and improve ranch economic efficiency.

Aiming at the problems of high crushing rate and severe loss of sesame capsules caused by mechanical collision in the conveying link of the header of the traditional sesame combine harvester, a low-damage screw conveying device has been optimized and designed. This study analyzed the operation mechanism of the low-damage screw conveyor based on the characteristics of the sesame capsule and determined the key parameters affecting the crushing rate of the sesame capsule, including diameter, pitch and rotational speed. Taking the crushing rate as the evaluation index and with the aid of EDEM discrete element simulation technology, the interaction model of sesame capsule - sesame stem - low-damage screw conveying device was constructed, and the single-factor and Box-Behnken response surface experimental designs were systematically carried out. Through simulation analysis and optimization, the optimal parameter combination of the low-damage screw conveyor device was determined: diameter 302 mm, pitch 422 mm, and rotational speed 200 r/min. Under these conditions, the crushing rate of sesame capsules can be reduced to less than 3%. This study provides a theoretical basis and technical reference for the structural optimization of the screw conveyor device of the sesame combine harvester and the reduction of the header loss rate.

Corn kernels collide with the pipe wall during pneumatic conveying, which affects their quality. Based on the theory of oblique impact collision dynamics, this paper investigates the breakage of corn kernels at impact angles ranging from 10°~40°. The results show that secondary collisions have a significant effect on the normal restitution coefficient. As the impact angle increases, all three restitution coefficients tend to increase. At impact angles of 40° and above, the normal and overall restitution coefficients are related to the initial velocity, while the tangential restitution coefficient is not. At impact angles of 30° and above, the normal restitution coefficient is smaller at the top and larger at the abdomen, and the tangential and overall restitution coefficients are independent of the collision location. The three restitution coefficients when the rotation direction is in the same plane are all smaller than those when rotation occurs perpendicular to the translational plane, with the normal restitution coefficient being most affected by the direction. When the impact angle is between 30°~40°, the tangential restitution coefficient of corn kernels increases with increasing rotational speed. When the impact angle is between 20°~40°, the normal restitution coefficient decreases with increasing rotational speed. When the impact angle is between 10°~40°, the overall restitution coefficient is independent of the rotational speed.

To address the lack of contact parameters in designing hazelnut harvesting machinery, this study measured the physical properties of hazelnuts through experiments and obtained a 3D model via scanning. A DEM model was established accordingly. Contact parameters between hazelnuts and steel were calibrated through physical and simulated tests, yielding a restitution coefficient of 0.31, static friction of 0.39, and rolling friction of 0.04. Using the relative error of repose angle as the response variable and hazelnut–hazelnut contact parameters as factors, a quadratic regression model was developed by steepest ascent and Central Composite tests. The optimal contact parameters of hazelnuts were 0.11 (restitution), 0.21 (static friction), and 0.04 (rolling friction), with a 1.84% error.

Corn kernels collide with the pipe wall during pneumatic conveying, which affects their quality. Based on the theory of oblique impact collision dynamics, this paper investigates the breakage of corn kernels at impact angles ranging from 10°~40°. The results show that secondary collisions have a significant effect on the normal restitution coefficient. As the impact angle increases, all three restitution coefficients tend to increase. At impact angles of 40° and above, the normal and overall restitution coefficients are related to the initial velocity, while the tangential restitution coefficient is not. At impact angles of 30° and above, the normal restitution coefficient is smaller at the top and larger at the abdomen, and the tangential and overall restitution coefficients are independent of the collision location. The three restitution coefficients when the rotation direction is in the same plane are all smaller than those when rotation occurs perpendicular to the translational plane, with the normal restitution coefficient being most affected by the direction. When the impact angle is between 30°~40°, the tangential restitution coefficient of corn kernels increases with increasing rotational speed. When the impact angle is between 20°~40°, the normal restitution coefficient decreases with increasing rotational speed. When the impact angle is between 10°~40°, the overall restitution coefficient is independent of the rotational speed.

Accurate and real-time weed identification is a key technology for ensuring the efficient operation of field weeding robots. It plays a vital role in reducing pesticide usage, minimising environmental pollution, and protecting the agricultural ecosystem. In response to these demands, this paper proposes a lightweight and high-precision weed recognition method, YOLO-LMSW, tailored for Chinese cabbage field scenarios. Firstly, a lightweight multi-scale convolutional module (LMSConv) is designed, upon which the LMSC3k2 structure is constructed to reduce computational complexity and enhance multi-scale feature extraction capabilities. In terms of network architecture, a lightweight backbone network, LMSNet, is built to significantly reduce model parameters while maintaining detection accuracy. Additionally, a detection head, LMSHead, is designed to further optimise the model structure. To improve localisation accuracy and convergence speed, the Wise-IoU (WIoU) loss function is introduced. Experimental results demonstrate that, compared to YOLOv11n, YOLO-LMSW achieves improvements of 1.2%, 1.0%, and 0.6% in precision, recall, and mAP50 respectively, while reducing the Params and FLOPs by 34.6% and 36.5%, respectively. These results demonstrate its application potential in the actual deployment of field weeding robots.

This study provides valuable insights into the interaction between seeds and soil, specifically focusing on quinoa seeds. Accurate discrete element contact models and parameters have been lacking in this area, which has somewhat hindered the optimization and improvement of seeders. By analyzing the collision process between quinoa seeds and soil through contact experiments, the study finds that the adhesive force of the soil on quinoa seeds is much greater than the gravitational force on the seeds and increases with higher soil moisture content. The load-displacement curve obtained from the experiments closely resembles the loading-displacement curve of the JKR (Johnson-Kendall-Roberts) model, leading to the selection of the JKR contact model for simulating the quinoa seed-soil mixture. Using Plackett-Burman experiments, the study identifies the key factors that significantly affect the dynamic angle of repose: soil surface energy, quinoa seed-soil surface energy, and the static friction coefficient between quinoa seeds and soil. Based on the principles of Box-Behnken experimental design, regression analysis and parameter optimization are conducted on these significant factors, resulting in optimal parameter combinations under varying soil moisture content. The verification experiment shows an error of within 1.9%, demonstrating the accuracy of the calibrated parameters. This research provides a solid theoretical foundation for discrete element simulation of seed-soil contact, offering a reference for improving the design and efficiency of seeders.

The lateral stress on the soil in the lateral position during the operation of a subsoiler directly affects the soil displacement and quality of operation. In this study, soil pressure sensors are used to measure the soil lateral stress at the lateral position of the subsoiler in the soil trough, and a discrete element simulation is used to explore the relationship between the change pattern of the soil lateral stress and soil displacement. The test results show that the soil at the lateral position of the subsoiler is subjected to lateral stress, which leads to the generation of lateral displacements. The lateral stress lasts no longer than 3.5 s, the lateral displacement lasts no longer than 3 s, and both exhibit a fluctuating pattern of peaks and troughs over time. The positive peaks are significantly larger than the troughs, indicating that the positive peaks are the main factor generating soil disturbance. The lateral stress and lateral displacement changes are consistent on the timeline, with the lateral displacement moving in the positive (negative) direction when the lateral stress direction is positive (negative). Both values tend to decrease linearly with increasing transmission distance.

To address issues such as soil compaction, plough layer damage, and hardpan formation caused by long-term use of rotary tillage and conventional ploughing, a winged subsoiler shovel was designed based on the soil characteristics of the black soil region in Northeast China. A mechanical contact model between the subsoiler shovel and soil was established to identify the design factors affecting the operational quality of the shovel. Using discrete element simulation technology, a shovel-soil interaction model was constructed. Through a quadratic regression rotational orthogonal design test, the effects of shovel structural parameters on soil disturbance area were determined. Taking the maximization of soil disturbance area as the objective, the shovel parameters were optimized. The optimal parameter combination was obtained as follows: shovel tip entry angle of 21°, wing inclination angle of 28°, and wing width of 104 mm, resulting in a maximum soil disturbance area of 1699.80 cm². A bench verification test was subsequently conducted, showing an actual soil disturbance area of 1666.08 cm². The test results were consistent with the simulation optimization results, confirming that the optimal parameter combination meets operational requirements and satisfies agronomic needs for deep loosening.

To address the issues of high labor intensity, low efficiency, and excessive flesh loss in pineapple eye removal, a design scheme of pineapple eye removal end-effector based on mechanical arm was proposed according to the analysis results of pineapple eye morphological characteristics. Based on the experimental determination of the elastic modulus of the pineapple fruit eye area the design of the end-effector mechanism for pineapple eye removal, kinematic and dynamic simulation analysis, and finite element optimization design of the eye removal gripper were carried out. Finally, a pineapple eye removal device was built for experimental verification. The simulation results indicated that the optimal length of the driving link of the pineapple eye removal end-effector mechanism was 23 mm. The eye removal trajectory of the gripper tip was highly consistent with the morphological contours of pineapple eyes. The maximum contact forces between gears and at the gripper-pineapple interface reached 33.13 N and 78.64 N, respectively. working conditions, the maximum stress and deformation at the gripper tip were measured as 532.15 MPa and 0.17 mm, revealing a significant stress concentration phenomenon. After structural optimization, the maximum stress and deformation were reduced to 133.13 MPa and 0.12 mm, with reduction rates of approximately 75% and 29.4%, respectively. These improvements effectively enhanced the structural stress distribution and deformation behavior, meeting the material strength requirements. The experimental results showed that pineapple eye removal operations were efficiently completed by the end-effector mechanism, with an eye removal success rate of 98%. The research results provide theoretical and technical reference for the development of pineapple automatic processing equipment.

In the current agricultural production, widely used plastic seedling trays suffer from poor air permeability, poor water permeability, and slow degradation. Therefore, this study proposes the preparation of degradable seedling trays utilizing the bonding effect of lignin, and the design of a heating compression forming mold according to the size and forming requirements of the trays. Flexural strength of seedling trays and seedling experiments were conducted to verify the effectiveness of the preparation process and mold design. The experiment results showed that the heating function requirements of the mold can be achieved when the heating power of the electromagnetic induction heater was set to 3000 W, and the heating time of the frame was 60 s. The heating compression mold designed can ensure that the seedling trays were heated uniformly and have excellent mechanical properties during the forming process. Biomass seedling trays prepared using lignin bonding and specific raw material ratios performed well in terms of compressive strength, water resistance and degradability. During the seedling cycle, no rupture occurred in the seedling trays, and the growth of maize seedlings was good enough to meet the actual seedling needs. The results of the study provide new ideas and methods for the preparation of lignin bonding degradable biomass seedling trays, and the application of heating compression forming mold provides technical support for the efficient and large-scale production of seedling trays.

This study evaluates the influence of grinding time on the particle size distribution of rosehip powder. A universal mill based on the cutting principle was used, equipped with four sieves with different mesh sizes (0.2 mm, 0.5 mm, 1 mm, and 4 mm). The grinding durations were set at 30 seconds, 60 seconds, 90 seconds, 120 seconds, and 150 seconds. Each grinding stage was followed by particle separation using a set of sieves to determine the particle size distribution. The results indicated that both the grinding duration and the mesh size of the sieves significantly impacted the particle size distribution The 0.5 mm sieve associated with a grinding time of 90 seconds provides an optimal balance between achieving a reduced average particle diameter, high specific surface area, and energy efficiency. These findings have important implications for the production and quality control of rosehips powder in various applications, including food, nutraceutical, and pharmaceutical products. Understanding the granulometric behavior of rosehip powder can help improve the solubility, stability, and bioavailability of the product. This research establishes the groundwork for further studies focused on optimizing grinding processes and developing rosehip powder products with customized particle size distributions to meet the specific demands of various industries.

To enhance the efficiency and quality of whole-stalk reed harvesting and baling, and to address issues such as low efficiency, high susceptibility to reed damage, and high fragmentation rates associated with manual baling, this study focuses on a critical component of the reed baling device: the design and optimization of a trigger rope-feeding mechanism. This mechanism comprises a triggering mechanism and a rope-feeding mechanism. The triggering mechanism employs a combination of a rocker-slider mechanism and springs to achieve reliable triggering and resetting of the baling action. The rope-feeding mechanism utilizes a crank-rocker mechanism to drive the rope-feeding needle, completing tasks such as rope delivery, knotting initiation, and reed compression to enhance bale compactness. A 3D model of the mechanism was established using SolidWorks, and kinematic simulation and parameter optimization were performed using ADAMS. The key parameter combinations for the trigger mechanism rocker length of 100 mm, connecting rod length of 30 mm, offset distance of 90 mm, and the rope feeding mechanism crank length of 160 mm, rocker length of 175 mm, and connecting rod length of 630 mm were determined. Based on the optimization results, orthogonal experiments were conducted with rope type, operating speed, and pre-tightening force as factors. The results indicate that the optimal parameter combination is nylon rope, operating speed of 90 r/min, and pre-tightening force of 123N. Experimental analysis revealed that operating speed has the most significant impact on productivity, pre-tightening force is the dominant factor influencing the damage rate, and rope type primarily affects the bale forming rate. Bench tests verified that this trigger rope-feeding mechanism effectively enables low-damage, high-efficiency reed baling, significantly improving bale density and operational efficiency, providing new technical insights for the development of reed baling devices.

Harvest quality is one of the key indicators for evaluating the performance of mechanized wheat harvesting. To enable real-time monitoring of harvest quality during the wheat combine harvesting process, this study integrates an online detection system specifically designed for wheat harvest quality. The system is capable of real-time monitoring of threshing and cleaning losses, breakage rate, and impurity rate. To assess the effectiveness of the online detection system, field experiments were conducted. The experimental results showed that the average threshing and cleaning loss rates of the harvester measured manually were 0.69% and 0.75%, respectively, while those detected by the online system were 0.52% and 0.50%, respectively. The average grain breakage rates obtained through manual measurement were 0.67% and 0.58%, whereas the system detected breakage rates of 0.49% and 0.52%, respectively. For impurity rate, manual measurements yielded average values of 0.81% and 0.69%, while the system recorded 0.73% and 0.67%, respectively. The results demonstrate that the developed online wheat harvest quality detection system can effectively perform real-time assessments of harvest quality during combine harvesting operations.

Flower thinning is an important agricultural process in orchards. To address issues such as insufficient thinning accuracy and the need for secondary operations in traditional orchard thinning mechanisms, and to meet the agricultural demands for precise flower thinning, this study designs a comb-type flower thinning end effector based on the physical characteristics of the flowers and the agronomic requirements for thinning. This study adopts a flower thinning method combining thinning leaf spring piece and comb teeth. By optimizing the design of the spring piece dimensions, the study first systematically analyzes the force characteristics of the spring piece during the working process, as well as the force conditions of the flowers under different operating conditions, to identify the key factors affecting flower abscission. Next, ADAMS software is used for rigid-flexible coupling dynamic simulation analysis to determine the optimal working speed of the key flower thinning device as 230–250 r/min. Finally, field tests were conducted to verify the working reliability of the flower thinning machine. The test results show that the machine meets the expected design goals, providing technical support for efficient and precise flower thinning operations in orchards.

In a weeding machine, the weeder component determines overall working performance, while the weeding blade influences soil-cutting quality, weeding efficiency, resistance, and power consumption. In this paper, a bionic design for a weeding cutter inspired by the soil-cutting process of the cicada nymph has been proposed and the intent is to promote the performance of the bionic design. The weeding cutter introduced in this paper has been equipped with lightweight and simplified rotary robotic-machinery commonly utilized in hilly and mountainous areas. To assess the performance of the bio-mimetic weeding cutter during rotary, the forces in 3 directions (X, Y & Z) and the resistance torque have been investigated. An experimental platform with a 4 m long soil bin has been utilized for tests indoors. Meanwhile, the discrete element method (DEM) simulations with different conditions like the operated angle, the rotation speed, the forward velocity, and different cutting edges, have been carried out. Either the experimental result or the simulative result indicated that the bio-mimetic weeding blade ameliorated the performance of soil-cutting, energy consumption and weeding efficiency. Specifically, the bio-mimetic weeding blade exhibits a notable reduction of 14.19% in average torque and 14.95% in horizontal resistance compared to the traditional weeding blade. Finally, it could be found that the bionic rotary cutter proposed in this research could be a better choice for the weeder machine used in hilly and mountainous areas.

Pod pepper is widely cultivated worldwide and serves diverse agricultural and industrial value. To reduce the loss and damage rate of mechanized harvesting of pod peppers, this study conducted multiple measurement ex-periments on the planting modes and biomechanical characteristics of pod peppers under different moisture contents. The experimental results are as follows: the average size of pod pepper fruit is 63.2 mm in length and 10.5 mm in diameter, the average density is 0.82×103 kg·m-3,and the elastic modulus is 9.6 MPa. The maximum tensile force required for detaching pod pepper stalks from stems is 13.85 N, and the average value is 11.62 N; whereas the maximum tensile force required for separating pepper fruit from stalk ranges from 16.0N to 35.0 N, indicating that the fruit-stalk connection is stronger than the stalk-stem connection. In addition, the moisture content has a significant effect on the biomechanical properties of pod peppers. Within a certain range, the compressive resistance of pod peppers increases first and then decreases with the decrease of moisture content.

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.

To improve the accuracy of discrete elemental simulation parameters for tiger nut seeds, this study suggests the measurement of the triaxial dimensions of tiger nut seeds using MATLAB. This method proves exhibiting a maximum error of no more than 0.16% compared to the traditional measurement techniques. Experimental measurements were used to obtain the basic physical parameters of the seeds. Physical tests on the angle of stacking of tiger nut seeds were conducted. A discrete meta-model for tiger nut seeds was constructed. The Plackett-Burman test and the steepest climb test were used to identify critical parameters affecting the angle of accretion and to delineate the optimal range of parameters impacting larger parameters proportionately. The optimum values for the coefficient of static and rolling friction between tiger nut seeds were established at 0.32 and 0.24, respectively, using response surface methodology. The average tangent of the simulated stacking angle obtained was 0.731 in three tests were carried out using the optimised parameters, representing a mere 0.41% deviation from the physical test average of 0.734. The experimental outcomes confirm that these parameters can provide reliable data for discrete meta-simulation experiments involving tiger nut seeds.

To address the seeding difficulty and low precision of small sized vegetable seeds, a new precision seed metering device with separated seed picking and dropping functions was developed. The structure and working principle of the device were analyzed. The mass, three-axis dimensions, and angle of repose of three cabbage seed varieties were measured to analyze the statistical patterns of their orientation distribution. The structural parameters of key components including the seed picking wheel, seed dropping wheel, and separator plate were designed using numerical calculation methods. Bench performance tests were conducted for parameter optimization. The experiment adopted a quadratic orthogonal rotation composite design, with the working speed and tilt angle of the measuring device as experimental factors, and the qualified seed spacing index and coefficient of variation as evaluation indicators. The results indicated that when the working speed was 0.40-0.63 r/s and the tilt angle was 5.32-9.67°, the seeding qualified index can exceed 90% and the variation coefficient was smaller than 12%, meeting the requirements of good seeding standards. This study used a separated harvesting vegetable seed metering device to sow small vegetable seeds, providing a new concept and theoretical reference for precision vegetable seed metering devices.

To improve the quality of paddy, reduce post-production losses, and ensure processing efficiency, this paper proposes a variable-temperature drying process based on the glass transition phenomenon, using the relationship between paddy moisture content and glass transition temperature. An optical three-dimensional scanning method was employed to obtain the paddy grain particle model. Subsequently, a heat and mass transfer model for paddy was constructed to analyze the temperature distribution and moisture migration behavior during drying. Experiments were conducted using a constant temperature of 40 ? and heating amplitudes of 5 ?, 10 ?, and 15 ? to investigate the drying characteristics and quality evolution of paddy. The simulation results showed that the average errors for moisture content and temperature were 1.58% and 2.66%, respectively. Compared with constant temperature drying at 40 ?, the variable-temperature drying with heating amplitudes of 5 ?, 10?, and 15? reduced the drying time by 19, 58, and 63 minutes, respectively. Among the tested conditions, the 5 °C heating amplitude yielded the best results, with a crack increase rate of only 2.5% and a head rice yield of 68.3%. These findings offer valuable insights for understanding the mechanism of variable-temperature drying and for optimizing the drying process of paddy.

Rice straws poor palatability and low nutrition limit its utilization as livestock feed, resulting in low intake rates. To address this, three fermentation treatments (compound bacteria, compound bacteria + enzymes, lactic acid bacteria) were tested over 25, 45, and 65 days, analyzing crude fiber, soluble sugar, and protein to evaluate quality. Results showed compound bacteria + enzymes performed best at 65 days: crude fiber reduced to 34.83%, crude protein increased to 9.45%, and soluble sugar reached 1.89%. Feeding trials with Simmental cattle revealed a 93% intake rate for fermented straw—65% higher than unfermented. This study establishes a microbial degradation technology for rice straw, identifies optimal starters, and forms a feed production process, laying a foundation for enhanced rice straw utilization as livestock feed.

To address the challenges of testing traction performance parameters of agricultural tires under varying soil types, tire specifications, and operational conditions, a dedicated test platform was developed to improve efficiency and reduce workload. The system includes a hydraulic drive with adjustable torque and speed, and a data acquisition module. Key components were selected based on design requirements and the frame was modeled and analyzed using ANSYS for both stress strain behavior and sixth-order modal characteristics, confirming structural integrity and absence of resonance. Field experiments were conducted using a 12.4-24 agricultural tire in accordance with the Chinese national standard GB/T 14828-2003, under three test surfaces: asphalt, withered grass field, and tilled soil. Results indicate that the developed equipment performs reliably through these conditions, demonstrating good stability and accurate data acquisition. The systems main specifications include external dimensions of 3155 mm x 1750 mm x 1385 mm, a total mass of 835.5 kg (1308.5 kg fully loaded), a variable speed range of 0-12 km/h, and a maximum driving torque exceeding 1000 N·m. These findings support the effectiveness and practical applicability of the device in agricultural tire traction performance testing.

To address the issues of high grain impurity rate and low cleaning efficiency in the cyclone separation cleaning system of small wheat combine harvesters in hilly and mountainous areas, this study proposes an internally screened cyclone separation cleaning system. Using the CFD-DEM coupled simulation method, the effect of the screen structure on the motion trajectory and separation behavior of the threshed material was systematically analyzed. A response surface methodology was applied to analyze the impact of interaction factors on cleaning performance. The optimal working parameters were determined to be: fan speed of 4020 r/min, scraper shaft speed of 340 r/min, clearance between the screen and the chamber wall of 37mm, and mesh size of 29 mm. Under these parameters, the simulation results showed a grain impurity rate of 2.99% and a cleaning loss rate of 1.10%, while the field experiment results were 3.08% and 1.14%, respectively. The field experiment results indicate that the designed cleaning system improved the cleaning performance and operational efficiency of the small combine harvester, providing both theoretical support and engineering guidance for the optimized design of cleaning system in small combine harvesters for hilly regions.

Traceability is a crucial component in ensuring the secure sharing of information throughout the entire supply chain of agricultural products. However, existing blockchain-based traceability solutions lack identity anonymity, face challenges in regulatory traceability, and lack dynamic member management capabilities. This paper proposes a blockchain privacy protection scheme integrating group signatures, pseudonymity mechanisms, and revocable accumulators to address these issues. The core design of this scheme encompasses three key aspects: first, this study introduces a dynamic group signature algorithm to balance the anonymity of data signatures with the traceability of responsibility, ensuring both privacy protection and accountability for data sources. Second, this study designs a pseudonym-based identity-hiding and authentication-obfuscation mechanism to enhance privacy protection further, improve resistance to on-chain analysis, and prevent the leakage of user identities. Finally, an efficient dynamic member management protocol is constructed to support rapid node joining and flexible revocation, thereby addressing frequent member changes in agricultural supply chains. Security analysis indicates that the scheme meets stringent security requirements regarding anonymity, non-repudiation, and traceability. Experimental results show that the proposed scheme outperforms existing solutions regarding signature verification overhead, communication costs, and operational efficiency, demonstrating good practicality and scalability, and providing practical support for privacy protection and digital regulation in agricultural supply chains.

To meet the demanding terrain of China’s forest regions—characterized by obstacles, gullies, and uneven ground—a wheel-leg hybrid chassis is proposed for a three-axle unmanned electric forestry vehicle. Parametric modelling in SolidWorks and dynamic simulations in MSC Adams quantified the critical load cases on the swing arms during obstacle negotiation. The Analytic Hierarchy Process (AHP) assigned optimal weights for multi-scenario topology optimisation. A finite-element model of the arms was built in HyperMesh/OptiStruct; post-optimisation analyses confirmed structural integrity. Masses of the front and rear arms converged to 31.3 kg each, and the middle arm to 39.44 kg, realising weight reductions of 29.1 % and 21.7 %, respectively, with no loss in performance.

To achieve mechanized deep topdressing for winter wheat, the planting pattern of narrow and wide row spacing of (10+20) cm was proposed and the topdressing applicator was designed to implement deep topdressing in the 20 wide row. The numerical simulation was conducted to evaluate the soil disturbance of furrow opener, the precision fertilization control system was attached to improve the fertilization accuracy, the precision row alignment system was attached to furnish support for the operation between the rows, the field experiments were conducted to evaluate the performance of topdressing applicator and the effect of deep topdressing fertilization on grain yield and yield trait parameters after harvest. The simulation results showed that the double-disc furrow opener with parameters of disc diameter 350mm, disc angle 10° and offset angle 25° caused the soil disturbance within the ± (5–15) cm range along with a heave less than 2 cm at a furrowing depth of 10cm, the row alignment deviation values of guideline alignment and cross-track deviation under flat ground were mostly distributed within ±3cm at a tractor speed of 3km/h. The field experimental results indicated that at a working speed of 3km/h, the topdressing applicator can accomplish 10 cm deep topdressing in the 20cm wide row along with the row alignment deviation largely distributed with in ±5cm, the GNs, TKW and grain yield were higher than broadcasting group, which verifies the feasibility of the planting pattern of (10+20) cm and the practicality of the deep topdressing applicator.

The natural conditions in northwestern China are extremely harsh, with severe desertification. Frequent sandstorms cause significant damage to transportation infrastructure along desert margins, agricultural cash crops, and the local ecological environment. This study proposes a novel high vertical curved sand barrier system. Field experiments were conducted to validate numerical simulation results, and the properties of wind-driven sand movement and protective efficiency of these barriers were analyzed. Furthermore, double-row sand barriers with varying spacing were investigated to determine the optimal arrangement. The research demonstrates that flow deflection angle of the high vertical curved sand barrier relative to the vertical direction significantly impacts its protective efficiency, showing a fluctuating pattern with changing angles. When the inlet airflow speed reaches 10 m/s with a barrier inclination angle of 75°, the leeward deceleration zone achieves an area of 25.450 m² with a coverage range of 19.31H (where H represents barrier height), resulting in optimal overall protective efficiency. For double-row sand barriers, the protective efficiency is influenced by inter-barrier spacing. As the spacing between two rows increases, the protective efficiency initially improves before declining. The system reaches peak overall protective efficiency when the inter-barrier spacing is set at 20H.

This study proposes an improved honey badger algorithm (IHBA) and a linear active disturbance rejection controller (LADRC) for the electric red bean precision seeder control system to solve high sowing missing rates and uneven sowing in high-density red bean precision sowing. The research details the design process of the intelligent control system, the improvement of the honey badger algorithm, and the intelligent adjustment method for key parameters of the LADRC controller. Simulation experiments show that the IHBA-LADRC based control system has no overshoot, short adjustment time, minimal steady-state error, no oscillation, and strong anti-interference ability. The system achieves an adjustment time of 0.79 seconds, almost zero static error, and an interference recovery time of 0.22 seconds. Bench tests indicate that compared to traditional PID electronic control sowing, the IHBA-LADRC system improves the pass index by 1.32 percentage points, reduces the multiple index by 0.49 percentage points, and lowers the missed sowing index by 0.76 percentage points. The overall variation coefficient decreases by 6.30 percentage points, and the variation coefficient of qualified plant spacing drops by 5.43 percentage points. This system reduces the discrepancy between the actual and theoretical plant spacing of red beans.

Aiming at the difficulty and low efficiency of threshing green prickly ash, a small-sized integrated pruning, threshing and branch chopping machine driven by a DC motor was designed and studied. The dynamic and static analyses of the tubular straight-cut threshing device and the clamping traction roller-cutting device were carried out respectively. The results show that the maximum shear stress on the green prickly ash branch during the cutting and separation process is 0.156 MPa. The maximum stress on the cutting blade of the clamping traction roller-cutting device is 333.99 MPa, and the maximum strain is 0.0017, which fully meets the usage requirements. The response surface analysis experiment was conducted with the moisture content of green prickly ash branches and the motor speed as the influencing factors and the undamaged threshing rate as the index. After optimization and solution, it was found that when the moisture content of green prickly ash branches was 43% and the motor speed was 1488 r/min, the undamaged threshing rate reached 95.9%, and the integrated pruning, threshing and branch chopping machine for green prickly ash reached the optimal working state.

Sheep face detection is critical for intelligent livestock management and breeding, yet existing models often struggle in complex farm scenarios due to inadequate multi-scale feature utilization and high computational demands. To address these challenges, this study proposes a lightweight multi-breed sheep face detection framework named YOLO-LSD (Lightweight Sheep Face Detection), achieving an optimal balance between detection accuracy and computational efficiency through multi-dimensional optimizations. At the feature enhancement level, the lightweight channel attention mechanism Efficient Channel Attention (ECA) is embedded into the backbone network to dynamically strengthen the channel responses of key facial features through local cross-channel interactions. Concurrently, Ghost convolution is introduced to replace traditional convolutional layers, leveraging feature redundancy mining technology to substantially reduce computational complexity while maintaining the ability to represent diverse facial features across sheep and goat breeds. To address the limited sample diversity in multi-breed datasets, a transfer learning strategy is employed, involving directional fine-tuning of breed-specific facial features based on large-scale pre-trained models to enhance the models generalization ability across diverse sheep and goat varieties. Experimental results demonstrate that YOLO-LSD achieves a mAP@0.5 of 99.29% on a self-constructed multi-breed sheep face dataset, marking a 0.59% improvement over the baseline YOLOv11. Notably, the parameter count of YOLO-LSD is only 2.4×106, while achieving an inference speed of 60 FPS and 6.3 Flops. This study presents a high-precision, lightweight solution for intelligent livestock monitoring systems, offering practical insights for the deployment of multi-breed sheep face detection models in real-world farm applications.

Aiming at the difficulty and low efficiency of threshing green prickly ash, a small-sized integrated pruning, threshing and branch chopping machine driven by a DC motor was designed and studied. The dynamic and static analyses of the tubular straight-cut threshing device and the clamping traction roller-cutting device were carried out respectively. The results show that the maximum shear stress on the green prickly ash branch during the cutting and separation process is 0.156 MPa. The maximum stress on the cutting blade of the clamping traction roller-cutting device is 333.99 MPa, and the maximum strain is 0.0017, which fully meets the usage requirements. The response surface analysis experiment was conducted with the moisture content of green prickly ash branches and the motor speed as the influencing factors and the undamaged threshing rate as the index. After optimization and solution, it was found that when the moisture content of green prickly ash branches was 43% and the motor speed was 1488 r/min, the undamaged threshing rate reached 95.9%, and the integrated pruning, threshing and branch chopping machine for green prickly ash reached the optimal working state.

To address the problems of poor fertilizer distribution uniformity and low application accuracy during the operation of rice fertilizer spreaders, this paper designs a shaftless double-screw fertilizer spreader for rice. Combining dynamic methods to analyze the filling stage and discharge stage of the fertilizer spreader, and investigating the impact of its structural parameters on the uniformity of fertilizer discharge. The fertilizer distribution process was simulated using EDEM software, and single-factor experiments were conducted to analyze the effects of spiral blade outer diameter, pitch, and rotational speed on fertilizer distribution uniformity. Furthermore, a two-factor orthogonal rotational combination experiment was carried out under different structural parameter conditions to analyze the interactions between influencing factors and determine the optimal parameter combination. The optimal parameters were found to be an outer diameter of 23.3 mm, a pitch of 23.6 mm, and a rotational speed of 149.2 r/min, yielding a uniformity coefficient of variation of 6.1%. Bench test results showed that the relative error between the simulation and experimental uniformity coefficient was 0.7%, indicating strong agreement. The results meet the agronomic requirements for fertilization and ensure stable fertilizer supply. These findings provide a reference for the design optimization of spiral fertilizer spreaders.

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.

To address the limitations of conventional tillage machinery in compacted, high–viscosity saline–alkali lands, this study designed a vertical rotary tiller tool structure suitable for deep fragmentation operations in saline-alkali lands, aiming to improve soil fragmentation efficiency and reduce operational resistance. Employing orthogonal experiments and response surface methodology (RSM), this study established a quadratic regression model correlating soil fragmentation rate and blade force, utilizing the tool camber angle, blade inclination angle, and internal bending angle as key variables. On this basis, a kinematic model of the cutting tool was constructed, and the correlation between the speed ratio and operating conditions was elucidated. Based on the EDEM simulation platform, the dynamic characteristics of cutting force, torque, particle flow velocity, and particle force during one complete rotation of the cutting tool in saline–alkali land were simulated and analyzed. Results indicated that the tool camber angle and internal bending angle exerted the most significant influence on operational effectiveness, with a notable interaction effect observed between them. An optimal parameter combination was ultimately derived through optimization: camber angle of 8.06°, blade–inclination angle of 7.48°, internal bending angle of 7.46°, resulting in a force of 2295.27 N and a soil fragmentation rate of 91.59%. The results established a theoretical foundation for optimizing vertical tiller blade design, while practical guidance for saline-alkali land tillage was developed through studies on soil fragmentation and energy use.

Aiming at the issues of high energy consumption and poor tillage quality caused by soil compaction and heavy clay in the saline-alkali soils of the Yellow River Delta, this study systematically analyzes the dynamic interaction between rotary tiller blades and soil, investigates the influence of structural parameters on tillage performance, designs a blade edge curve based on the shearing effect, and identifies the primary factors affecting energy consumption and their respective ranges. Using EDEM software in combination with a quadratic orthogonal rotational combination test, with bending angle, working width, and tangential surface height as the experimental factors and operational power consumption as the evaluation index, the optimal structural parameters were determined through Design-Expert 13 optimization: bending angle of 136.49°, working width of 34.20 mm, and tangential surface height of 42.3 mm, corresponding to a theoretical power consumption of 36.64 kW. Field experiments demonstrated that the designed rotary tiller blade consumed 38.49 kW under the same operating conditions, representing a 16.07% reduction compared with traditional blades, thereby validating the effectiveness of the design. The findings provide a theoretical basis for the utilization of saline-alkali land as well as for resistance reduction and efficiency improvement in rotary tillers.

This study investigates the distribution performance of solid organic fertilizers using vertical helical rotor equipment, focusing on the optimization of spreading uniformity. EDEM simulations were employed to analyse the particle flow and distribution patterns under varying rotor speeds (360, 440, and 540 rpm) and inclination angles (75°, 80°, and 85°). The results indicate that the most uniform distribution is achieved at 540 rpm with a 75° rotor inclination. The study was extended to a configuration with four rotors, simulating field-like conditions. Although a good lateral and longitudinal spread was observed, material concentration along the machine’s central axis suggests a second pass is needed for complete field coverage. These findings contribute to the design optimization of fertilizer spreading equipment to improve efficiency and application precision.

This study proposes a grading method using combined target detection and segmentation models to enhance grading accuracy for pepper plug seedlings. We constructed separate datasets for target detection and image segmentation of pepper plug seedlings, then trained various detection models including EfficientDet, Faster R-CNN, SSD, and YOLO-series architectures. After comprehensive evaluation, YOLOv5 achieved optimal performance with 99.5% mAP, a compact 3.8 MB model size, and rapid 5.33ms inference time per image. To avoid the impact of the culture medium and adjacent pepper seedlings on the image, we developed an improved U-Net model incorporating an Efficient Channel Attention (ECA) mechanism, enhancing segmentation accuracy by 1.29% to 93.01% while reducing processing time by 69.7% (33.32 ms). Subsequent feature extraction analysed lateral area, height, stem thickness, hypocotyl length, and divergence degree from segmented images. Using these extracted features, grading models were trained employing support vector machines, k-nearest neighbours, and random forests. The random forest model achieved a grading accuracy of 99.33%, validating that this method meets the accuracy requirements for pepper seedling grading.

A discrete element model (DEM) of rapeseed threshing and separation was established based on EDEM software. The simulation results were compared with bench test data, showing an absolute error of 0.33% and a relative error of 55% for threshing loss. The relative errors for the proportion of threshed material on the sieve surface and the left–right distribution ratio of threshed material on the sieve surface were 1.21% and 2.38%, respectively, verifying the accuracy of the simulation model. Secondly, a three-factor, three-level Box–Behnken experimental design was conducted, with threshing drum speed, guide plate angle, and threshing gap as test factors, and threshing loss, proportion of threshed material on the sieve surface, and left–right distribution ratio of the threshed material as evaluation indicators. The influence of each factor on the evaluation indicators was analyzed, and regression models between the test factors and evaluation indicators were established. Through a multi-objective optimization solution, combined with consideration of the actual operating conditions and processing requirements of the rapeseed combine harvester, the optimal parameter combination was determined as: drum speed of 550 r/min, guide plate angle of 75°, and threshing gap of 8 mm. Finally, a prototype was developed based on the optimized structure and operating parameters, and its rapeseed harvesting performance was tested by a third-party inspection agency. Field tests showed a total harvest loss rate of 5.6%, impurity rate of 2.3%, breakage rate of 0.4%, and an operational productivity of 0.57 hm²/h. The performance exceeded the requirements of industry standards. This study provides a valuable reference for the performance optimization of threshing devices.

This research was conducted for enhancing the linearity (or straightness) of the movement of a seeding machine-tractor system (SMTS), encompassing both theoretical and experimental studies. The experimental studies were conducted using a SMTS as part of a classic all-wheel drive tractor and a pneumatic trailed seeder. Theoretical research of the SMTS was carried out for three versions of the initial parameters of the mathematical model. In the first version the speed of movement was v = 2.5 m·s–1, the distance from the point of the seeder trailer to the centre of mass of the seeder was l5 = 2 m, and the pressure in the pneumatic tires of the tractor wheels was Pw = 0.10 ?P?. In the second one, the speed of the unit is increased to v = 2.8 m·s–1, the distance from the point of the seeder trailer to the centre of mass of the seeder is increased to l5 = 3 m, and the pressure in the tractor’s tires is Pw = 0.10 MPa. In the third versions, the pressure in the tractor tires is increased to 0.12 MPa. The difference between values O(1)(T), O(2)(T) of the trajectory of the movement of the centres of mass of the tractor and the seeder, determined during the theoretical research, and determined during the experimental research is 11%, and the discrepancy between the values of the rotation angles of the centres of mass is 9%; therefore, the mathematical model of the dynamics of the SMTS may be considered adequate.

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.

To investigate the movement characteristics and distribution patterns of materials inside the threshing drum during the rapeseed threshing process, a simplified DEM model was established using three typical particle types: seeds, pods, and stalks. A simulation system of the drums internal environment was constructed, and combined with high-speed photography experiments to analyse the material distribution and dynamic movement trajectories. The results showed that materials primarily migrated in a helical pattern along the outer edge of the drum. Seeds and pods were mainly concentrated in collection box 2, accounting for 33.6% and 27.6% of the total, respectively. The material quantity gradually decreased from collection boxes 2 to 6, while 56.5% of the partially unbroken stalks were discharged from the straw outlet and collected in box 7. To verify the accuracy of the simulation, a test platform was built and high-speed photography experiments were conducted, showing that the actual material trajectories closely matched the simulation. Both simulation and experimental results indicated the highest material concentration in collection box 2, at 24.6% and 19.2%, respectively, and the lowest in box 6, at 5.1% and 6.8%. These findings provide theoretical support and technical guidance for optimizing drum structure, achieving precise threshing control, and advancing intelligent agricultural machinery design.

Although composting is a well-established method for the biological stabilization of organic matter, in recent years advanced technologies and optimized operational strategies have been introduced, that are aimed at enhancing both compost quality and processing efficiency. These innovations, ranging from improved aeration and moisture control systems to the use of bio-activators and process monitoring tools, have significantly reduced decomposition time, while ensuring a more homogeneous, nutrient-rich final product. The aim of the paper is to systematically centralize relevant information from the literature with the purpose of identifying the key parameters that most significantly influence the composting process and evaluate the advantages and disadvantages of the most widely used composting technologies. Experimental results reported in the literature indicate that emerging processing technologies offer faster composting and improved compost quality, by enabling more efficient optimization of operating parameters. By producing higher-quality compost, these technologies enhance soil fertility, structure, and microbial activity, leading to improved nutrient cycling and water retention. In the long term, it can play a crucial role in promoting sustainable soil management, restoring degraded soils, and enhancing carbon sequestration, thereby contributing to climate change mitigation.

The aim of the study was to develop an efficient and cost-effective tractor-front-mounted conveyor-based pulse crop harvester to meet the increasing demand for mechanized harvesting methods, particularly for crops like chickpeas, lentils, and black gram. The research is based on the principles of agricultural mechanization, focusing on optimizing key crop parameters such as cutting force requirements, plant spacing, row spacing, and stem diameter. These factors are crucial in selecting appropriate motors and cutting blades to enhance harvesting efficiency of pulse crops. The study involves torque calculations to determine the power requirements for cutting and conveying units, with results showing 1.24 Nm and 7.06 Nm, respectively. A gear motor was employed to ensure the proper conveyance of cut crops at a linear speed of 0.54 m/s. Field tests were conducted in a black gram field to evaluate the harvesters effectiveness under varying conditions. Field tests demonstrated the harvesters ability to handle premature crops, with cutting efficiency ranging from 71.04% to 75.06% at different forward speeds. The harvester achieved a maximum field capacity of 0.225 ha/h at a speed of 1.50 km/h, with a corresponding field efficiency of 75.02%. The working capacity varied from 0.831 ha/day to 1.80 ha/day, proving its suitability for pulse harvesting. Power consumption analysis indicated a total power requirement of 0.569 kW, enabling the harvester to operate for approximately 4 hours on a fully charged 12V, 200 Ah batteries. The developed harvester provides a viable solution for pulse crop harvesting, addressing the need for mechanization in pulse cultivation. Its efficiency and economic feasibility make it an attractive option for widespread adoption, contributing to improved productivity in Indian agriculture.

To address the poor seeding effect of inside-filling pneumatic high-speed precision seed-metering device for cotton, Computational Fluid Dynamics (CFD) and Discrete Element Method–Computational Fluid Dynamics (DEM-CFD) approaches were employed for simulation and optimization. The CFD technique was applied to analyze the influence of key structural parameters of the negative-pressure air chamber on the adsorption performance of the suction holes, while the DEM-CFD approach was used to investigate the motion of cotton seeds inside the seed-metering device during the seed-throwing stage. Based on these analyses, the optimal combination of structural and performance parameters of the seed-metering device was obtained. The simulation-optimized parameters were then validated through bench tests, which showed that at a suction hole diameter of 3.1 mm, a forward speed of 2.33 m/s (8.4 km/h), and a negative pressure of 3,178 Pa, the evaluation indexes were optimal: 96.25% qualification index, 1.83% multiple sowing index, and 1.92% missed sowing index. Further high-speed adaptability test showed that when the forward speed was 1.67~3.33 m/s (6~12 km/h), the qualification index remained greater than 91%, and both the multiple and missed sowing index were less than 5%, meeting the agronomic requirements of high-speed precision seeding for cotton.

To solve the problems of multiple picking and missed picking in garlic planter, single-seed picking has become a critical technology that urgently needs improvement. In response, a claw-chain mechanism for single garlic seed picking was developed. Based on theoretical analysis of the clove picking claw’s motion, the feasible ranges of claw speed (v), minimum clove-holding length (S2), and sprocket inclination angle (?) were determined. Using the EDEM simulation platform, the single-seed picking rate and missed picking rate under different parameter combinations were obtained. A Box–Behnken central composite design was used to conduct a three-factor, three-level quadratic regression experiment involving claw speed, minimum clove-holding length, and sprocket inclination angle. Design-Expert software was employed to establish a response surface model and optimize the key parameters affecting the single-seed picking rate. The optimal parameter combination was a claw speed of 0.05 m/s, a minimum clove-holding length of 32.4 mm, and a sprocket inclination angle of 5.6°. Under these conditions, the single-seed picking rate reached 91.7%, and the missed picking rate was 4.4%, meeting the technical requirements for garlic single-seed planting.

Drought and soil degradation represent critical challenges to sustainable agriculture and global food security, because they limit crop productivity and disrupt ecosystem services. The paper explores novel solutions designed to mitigate the impacts of drought and restore soil functionality in agricultural systems. The proposed approaches integrate innovative soil amendments, such as biochar and compost-based bioproducts, with advanced water conservation techniques and biological interventions aimed at improving soil structure, fertility, and moisture retention. Furthermore, the study examines the role of microbial inoculants and organic matter management in enhancing soil resilience to climatic stressors. This paper focuses on the analysis of case studies that illustrate notable advances in mitigating the effects of drought and improving the quality of agricultural soils through the use of unconventional and innovative technologies. The results highlight the effectiveness of integrating biological, physical, and chemical strategies, carefully adapted to specific site conditions. The adoption of these novel approaches has the potential to enhance the resilience of agricultural systems to climate variability, while simultaneously supporting productivity and long-term sustainability. This research supports the development of climate-smart agricultural approaches that harmonize productive farming with long-term environmental sustainability. This paper synthesizes and critically evaluates the most effective and current strategies identified in recent literature, offering a comprehensive overview aimed at advancing efforts to combat climate change.