Abstract:In response to the problems of poor adaptability of tree digging machines and low energy efficiency of hydraulic systems caused by dense forest planting and complex terrain in hilly and mountainous areas of China，the structure of the tree digging machine arm was designed and hydraulic cylinders model was selected，a co-simulation model of tree digging machine was established to analyze the structure and hydraulic system performance of the machine. Dynamic simulations were conducted on the chassis walking and arm of the tree digging machine using Recurdyn software. It was determined that the maximum lifting load of the tree digging machine was 2t. The maximum stress on the lifting arm was concentrated in the front of the lifting cylinder hole，with a maximum stress of 72.5 MPa. A co-simulation model of the tree digging machine-liquiddiscrete element method was established by using Recurdyn, AMESim, and EDEM software. The lifting condition of the tree digging machine，the soil cutting condition of the shovel, and the soil swinging condition were simulated and calculated. The pressure change curves inside the oil cylinder of each actuator and the soil cutting resistance change curve of the shovel were analyzed. The simulation results showed that during the lifting process of the arm，the pressure of the lifting oil cylinder was stable，with a pressure of 5.2 MPa. During the process of cutting soil，the resistance of the shovel was gradually increased, with a maximum resistance of 28 900 N. Under the condition of soil swing, the pressure inside the flap oil cylinder was increased with the increase of tilt angle, and the maximum pressure was 8 MPa. Testing on the hydraulic system of the tree digging machine in woodland was conducted, and the test results showed that the pressure of the lifting cylinder remained stable at 5.5 MPa during the lifting process of the arm. Under the condition of cutting soil，the pressure difference of the shovel cylinder was 10 MPa, and the indirectly calculated cutting resistance was 28 260 N. Under the swing condition of the soil, the pressure of the flap oil cylinder was increased with the increase of the tilt angle, and the maximum pressure was 7.7 MPa. Through comparative analysis of tree digging machine simulation experiments and hydraulic testing, the relative errors between simulation values and measured values under lifting, cutting soil, and soil swing conditions were found to be 5.5%，2.3%, and 3.9%, respectively. The accuracy of the cosimulation model of the tree digging machine and the stability of the tree digging machine arm structure and the hydraulic system was verified.

Abstract:Aiming at the problems of high digging resistance and high energy consumption of the digging shovel during the harvesting operation of Panax notoginseng, a high-efficiency drag reducing bionic digging shovel was designed with the toe of the front foot of pangolin as the bionic prototype. Taking Panax notoginseng rhizome and planting soil as research object, the intrinsic physical parameters were calculated, and the bonding key parameters were set to establish the discrete element model of Panax notoginseng rhizome. The root soil bonding mechanism was analyzed, and the discrete element composite model of Panax notoginseng rhizome planting soil was established by Hertz Mindlin with JKR. The point cloud model of pangolin′s claw toe was obtained by three-dimensional scanning, and the threedimensional model of the bionic excavator shovel was established by using Solidworks. The shape and structure of the bionic excavator shovel were designed according to the three-dimensional model of the claw toe. The mechanical analysis of the shovel tip and handle was carried out to determine the design parameters that affected the operation quality. Based on EDEM discrete element method, the component soil crop multiple simulation model was established, and the simulation experiments were carried out with soil particle velocity vector, soil disturbance rate and excavation resistance as evaluation indexes，and the influence laws of different indexes were obtained. The operation performance of the bionic excavator was verified by bench test. The average digging resistance of the bionic excavator and the plane excavator were 1 171.69 N and 1 442.36 N, respectively, and the average drag reduction rate was 18.81%. The bench test results were basically consistent with the simulation test results. The test results showed that the bionic excavator had good characteristics of reducing resistance and consumption, and the bionic structure design was reasonable, which can meet the harvesting and excavation of Panax notoginseng under the condition of acid laterite.

Abstract:A multi-helical rod pelleted Cistanchis precision seed metering device was designed to address the problems of current imperfect induction mechanism of Cistanche which requires multiple rounds of replanting, as well as the problems of narrow dispersal surface and poor uniformity within the dispersal surface of Cistanchia seed metering device. A helical rod mechanism was used to move the seed along the helical and the dispersal surface which was expanded by installing ten helical rods.The kinetic model of pelleted Cistanche seeds and helical rod was established, and the key structural parameters of the helical rods were theoretically analyzed and determined. Based on the agronomy of Cistanchis cultivation and the parameters of pelletised Cistanchis seeds of specific specifications, the theoretical seeding rate was calculated. The effects of key parameters such as pitch, depth and rotational speed on seeding performance were analyzed by using the discrete element method. The model of pelleted Cistanchis seed metering device was constructed by EDEM. The variation coefficients of seeding uniformity and seeding capacity were used as the test indexes, and the ranges of pitch, depth and rotational speed were determined through a single factor test. The test results showed that the optimal parameter range of pitch was 15~20 mm, the optimal parameter range of thread depth was 7~9 mm, and the optimal range of rotational speed was 60~100 r/min. The BoxBehnken central combination test of three-factor and three-level was carried out, and a multiple regression model was established. The variance and response surface analyses were performed by Design-Expert software, the helical rod structure and working parameters were obtained through objective optimization. The optimal pitch of the helical rod was 17.77 mm, the depth was 7.81 mm，and the rotational speed was 83.32 r/min. The bench test showed that the variation coefficient of seeding uniformity and seeding capacity were 3.75% and 5.33%, respectively. The design met the agronomic requirements of seed dispersal under the imperfect induction mechanism of pelleted Cistanche, and provided a reference for research programme of seed metering device to improve the uniformity of seed dispersal in the dispersal surface of pelleted Cistanche.

Abstract:To address the issues of significant fertilizer discharge fluctuations and poor stability in traditional spiral fertilizer dischargers, a four-head inclined spiral precision fertilizer discharger was designed. Through motion analysis and theoretical calculations, the key parameters affecting the stability of fertilizer discharge rate were identified, along with their optimal value ranges.Experimental factors included the number of spiral blades, discharger inclination angle, spiral blade diameter, spiral blade pitch, and spiral blade rotational speed, with the coefficient of variation of fertilizer discharge rate as the evaluation metric. Discrete element method （DEM） simulations were conducted for both single-factor and multi-factor scenarios. The single-factor simulation results indicated that the number of spiral blades, discharger inclination angle, spiral blade diameter, and spiral blade rotational speed all significantly impacted the stability of fertilizer discharge rate. It was found that using a four-head inclined spiral to convey fertilizer can enhance discharge stability. Multifactor simulation results showed that the optimal parameter combination for a four-head spiral configuration was with discharger inclination angle of 38°，spiral blade diameter of 46 mm, and spiral blade rotational speed of 31 r/min. Under this combination, the coefficient of variation of fertilizer discharge rate reached minimum value of 1.65%. Finally, bench tests were conducted to validate the simulation results under the optimal parameter combination. The tests revealed that the measured coefficient of variation for fertilizer discharge rate was 3.69%. Further optimization determined that at spiral blade rotational speed of 33 r/min, the coefficient of variation could be minimized to 2.92%. Both simulation and bench test results demonstrated that the improved four-head inclined spiral precision fertilizer discharger performed well in terms of fertilizer discharge stability, which can meet orchard fertilization standards, and satisfy the operational requirements for orchard fertilization.

Abstract:The long-term traditional farming methods in the southwest region have caused the plough bottom to move up and the soil to become increasingly barren. The traditional subsoiling and fertilization devices have poor operating efficiency. Combined the principles of bionics, a layered subsoiling and classified fertilization device was designed, a discrete element model of soil and fertilizer was established based on EDEM software, and simulation experiments were conducted. The simulation results showed that the optimal parameter combination for the subsoiling device was a subsoiling speed of 1.54 km/h, a horizontal spacing of 500 mm between the front and rear shovels，and a vertical spacing of 300 mm between the front and rear shovels;The optimal parameter combination for the fertilizer feeder device is a radius of 30 mm for the sheave, a distance of 28.5mm from the arc center, and a helix angle of 81°. Based on the simulation results, field experiments were conducted, and the results showed that the disturbance rate of the layered subsoiling and classified fertilization machine on the soil was 52.4%, the soil looseness was 19.7%, and the soil crushing rate was 78%, all meeting the design requirements of the subsoiler and surpassing the single-shovel subsoiling operation. The results of the fertilizer performance test showed that the organic fertilizer application rates were 120.8 and 182.8 g, respectively, while the inorganic fertilizer application rates were 620.3 and 916.9 g, respectively. The stable variation coefficients of organic fertilizer and inorganic fertilizer were 2.38% and 4.93%, respectively, meeting the requirements for fertilizer application and achieving the stratified application of organic and inorganic fertilizers. The integrated device for layered subsoiling and classified fertilization designed improved the soil crushing effect and fertilization efficiency of the plough bottom layer, providing a reference for optimizing the structure of conservation tillage machinery.

Abstract:Aiming at the problems of low steady-state accuracy and slow response speed of variable fertilization control system for liquid fertilizer of valve-controlled hydraulic motor, a variable fertilization control algorithm for liquid fertilizer based on particle swarm optimization RBF?PID（PSO?RBF?PID） was proposed. Firstly, the closed-loop transfer function of the control system of liquid fertilizer variable fertilization control system was established, and the key parameters of RBF neural network were optimized by PSO algorithm, and compared with traditional PID and RBF-PID control，Matlab/Simulink software was used for simulation analysis. The simulation results showed that the adjustment time and tracking error of the system under PSO ?RBF ? PID control were the smallest，which verified the feasibility of the algorithm. A test bench of liquid fertilizer variable rate fertilization control system was built and indoor experiments were carried out to verify the flow measurement accuracy of the system. The results showed that the relative error of the system measurement was less than 4%, which met the measurement requirements. The static and dynamic characteristics of the system under the control of different algorithms were tested. The experimental results showed that the maximum relative errors of system flow under the control of traditional PID，RBF ? PID and PSO ? RBF ? PID were 5.33%，3.83% and 2.50%, respectively, and the average adjustment time of the system was 5.16 s，3.80 s and 2.19 s, respectively, when the target flow value changed suddenly. Each index of the proposed PSO?RBF?PID control algorithm was superior to that of the traditional PID and RBF ?PID control, which can ensure that the system had good static and dynamic characteristics and met the variable application requirements of liquid fertilizer.

Abstract:In order to ensure the efficiency of harvester and reduce the damage rate of potato harvester during bagging process, a kind of bagging unloading device was designed, and the collision process and stress of the potato on the device were studied based on discrete element simulation. The device can complete the processes of cache collection, bag loading and bag unloading, ensure the working process without stopping, and improve the working efficiency of the machine. Through theoretical analysis, the force relation of lifting hydraulic cylinder was obtained and the type selection and layout of lifting hydraulic cylinder were determined. The collision process of potato block was analyzed, the relation expression of the collision stress was obtained, and the influencing factors of the stress were determined as the angle between the box wall and the horizontal plane and the normal impact velocity. Through analysis and calculation, the range of structural parameters of the collecting potato box was obtained, and the corresponding hydraulic system was designed according to the working requirements of the unloading bag device of the collecting potato. In order to verify the rationality of the selected work and the range of structural parameters and the stress of the potato in the bagging process, the discrete element simulation test was carried out by measuring the material characteristics of the potato and giving the motion properties of the corresponding components. The accuracy of the particle model was verified by the stacking angle and the corresponding single factor simulation test was carried out. The maximum force of the particles at each time step and the mean value of the forces at each stage were selected as the evaluation index.By analyzing the simulation test results, the influence of various factors on the force on potato particles was clarified, and corresponding simulation tests were conducted on the working sequence.The order of potato harvesting and bagging was determined. The simulation test results showed that the damage rate of potatoes collected first and then bagged was 1.07%, and the damage rate of potatoes collected first and then bagged was 3.04%. And the correctness of the working sequence of the first collecting potatoes and then bagging was verified through field experiments. The field experiment results showed that the potato damage rate during the whole machine operation of first collecting potatoes and then bagging was 2.59%, which was lower than that of the potato damage rate（3.71%） during the whole machine operation of the first bagging and then collecting potatoes.

Abstract:With the increasing demand for product quality in the manufacturing industry, the application of machine learning （ML） technology in manufacturing quality control has been under attention. To address the low automation and integration, as well as the lack of quantitative evaluation methods in the manufacturing quality inspection for combine harvester, a combine harvester manufacturing quality end-of-line inspection system was designed and developed. Based on this system, an "end-of-line inspection + secondary grading" manufacturing quality hybrid inspection method was proposed, which used the inspection software to screen out abnormal products outside the qualified range and select superior and inferior products. The secondary grading model performed a secondary inspection on qualified products and marks hidden problems. Firstly, based on the integration and analysis of the combine harvester manufacturing quality inspection requirements, the detection flow was designed. The overall design of the system was tested and simulated by using the Visual Components digital workshop platform. The LabVIEW-based end-of-line inspection software was developed according to the actual requirements and detection functions, and corresponding userfriendly human-machine interfaces were designed. The results of the end-of-line workshop inspection tests showed that the system can meet various inspection requirements and achieve software functions, preliminarily verifying the feasibility of the system. Secondly, local outlier factor （LOF） was selected as the secondary grading algorithm according to the scenario, and it was integrated into the detection flow based on its anomaly detection principle. Then, a manufacturing quality inspection and grading framework was established, and the grading process classified the initially screened qualified products into "good" and "tracked" groups based on the processing results, thereby improving the manufacturing quality inspection and evaluation system. The training results indicated that LOF-based method can identify anomalous samples in the dataset with insignificant differences. In the performance validation process, this method accurately identified the four "tracked" samples in the testing dataset, which was consistent with the distribution of the quartile plots, further validating the effectiveness of this hybrid detection method. The developed end-of-line inspection system for the manufacturing quality of combine harvesters and the proposed grading method had important practical application value, promoting the application of digital workshop concept and ML on agricultural machinery, and providing solutions and methods for agricultural machinery manufacturing quality control.

Abstract:To mitigate the issues of uncertainty disturbances and time delays faced by corn harvesting machines in complex and time-varying operational environments, and improve the disturbance rejection capability of the control system as well as the quality of corn harvesting, an adaptive control method based on disturbance observation was proposed for corn ear-picking harvesting. This method targeted to minimize the ear loss rate and utilized the rotational speed of the pulling rollers, operating speed, and header height as the primary control subsystems. Firstly，tailored to the operational environment and system characteristics of the harvester, models of the corn ear-picking system were constructed. Secondly, a parallel weighted PI controller was employed to optimize the desired target values for each subsystem. Thirdly, utilizing active disturbance rejection control （ADRC） as the feedback mechanism, an extended state observer was implemented to estimate and compensate for both internal and external disturbances in the system online;Finally, an adaptive control system model for the harvester was established, and simulation experiments were conducted to evaluate the effectiveness of the harvester system control strategy and the control performance of each subsystem. Field experiments were also conducted to verify the effectiveness of the control strategy. Experimental results demonstrated that all subsystems collaborated seamlessly according to predefined control rules, achieving control objectives quickly and stably with steady-state errors eventually converging to zero. The system exhibited strong anti-disturbance capabilities and achieved satisfactory control effects.

Abstract:Based on an in-depth analysis of the growth characteristics and picking requirements of Chrysanthemum morifolium, a reciprocating comb-tooth chrysanthemum picking device was designed. The device utilized an offset crank-slider mechanism to drive the comb-tooth array for picking and employed brushes to gather and transport the picked chrysanthemums to a collection box.To optimize the structure and working parameters of the device, force analysis and theoretical calculations were conducted to determine the design and parameters of the picking components.Subsequently, a coupled rigid-flexible model of the chrysanthemum and the picking device was established by using ADAMS software for simulation analysis of the picking process. Furthermore, a quadratic regression orthogonal rotation combination experiment was employed to study the effects of crank speed, working depth, and travel speed on picking rate, damage rate, and impurity rate.Multivariate regression fitting analysis of the experimental data yielded regression equations for each indicator and optimized the device′s working parameters. The test results showed that when the crank speed was 42 r/min, the working depth was 216 mm, and the travel speed was 0.2 m/s, the picking rate can reach 90.06%, the damage rate was 0.59%, and the impurity rate was 7.21%. This reciprocating comb-tooth chrysanthemum picking device demonstrated good passability and stability，effectively improving the picking efficiency of Chrysanthemum morifolium and reducing labor intensity. The research result can provide a significant reference for the mechanization of chrysanthemum picking, addressing the issues of traditional manual picking methods which were inefficient and labor-intensive, thereby contributing to the advancement of chrysanthemum harvesting technology.

Abstract:Aiming at the problems of poor adaptability of harvesting machinery and low picking efficiency of domestic dwarfed and densely planted walnut trees, a crawler walking shaker was created. Firstly, by establishing a tree vibration model and conducting kinematic analysis of the mechanism, the key components of the shaker were designed, and the development of the whole machine was realized. Then vibration tests were carried out by the shaker, and the significance relationship between the vibration parameters and the shedding rate was analyzed by using statistical methods to obtain the lower limit of the vibration parameters to meet the requirements of high shedding rate operation. By analyzing the vibration response under different excitation parameters，the correlation law between the excitation parameters and the shedding rate was given. The excitation parameters that significantly affected the shedding rate were quantified through linear regression and feature importance comparison, and finally the range of excitation parameters was visualized. It was found that within a certain range, the frequency affected the shedding rate more significantly than the amplitude and operation length, and it was difficult to increase the frequency to improve the shedding rate when the amplitude was lower than 60 mm. Finally, the lower limit of the vibration parameter corresponding to a shedding rate of 80% was tested to verify the accuracy and reliability of the results.The research result can provide a theoretical basis for the development of shakers and walnut harvesting operation. It also can provide a reliable range of excitation parameters for future walnut harvesting operations, aiming to improve harvesting efficiency and reduce harvesting costs, and can also guide the development of intelligent shakers and field harvesting operations.

Abstract:Aiming at the industrial hemp mechanized harvesting process, the cutting table operating performance and cutting and laying quality is poor, low efficiency, high failure rate, and high cost，the cutting table is easy to be blocked and the cutter device is easy to be damaged, etc., the theoretical analysis of the harvesting device was carried out, and the ideal hemp dumping rotational speed under the conditions of the hemp stalks were not pushed down or broken and multiple plants were dialed for 107.17~283.09 r/min was obtained. Kinematics and dynamics analysis of the cutter device was done, and a counterweight block was designed to balance the inertia force of the crank，the mass of the counterweight block was 6.672 kg;based on the response surface analysis method for field tests, the best combination of parameters were as follows：flinging speed was 628.08 r/min，traveling speed was 7.28 km/h, the cutter speed was 502.73 r/min;at this time, the stalks laying angle, the angle difference and the root difference were respectively 89.02°，5.16° and 17.46 mm;based on the optimal parameter combination, the straw laying angle of the cutting table was 88.20°，the angle difference was 4.95°, and the root difference was 18.35 mm, and the relative errors with the theoretical optimized values were no more than 4.85%. Field test results showed that the designed cutting table performance parameters met the standard technical requirements, which can achieve better cutting and spreading effects, and can meet the industrial hemp mechanized harvesting requirements.

Abstract:To address the issues of high labor intensity in manual defoliation, high damage rates，and low operational efficiency in existing mechanical de-leafing devices for Artemisia argyi, the design and experimental optimization of a low-destruction leaf-pulling defoliation device based on DEM?MBD were conducted. Firstly, the kinematic and dynamic model of the defoliating device was established, and the key components such as the conveyor belt, stem-pressing belt, stripping teeth，and stem-pulling rollers were designed through theoretical analysis. Subsequently, the joint simulation of the defoliation device was performed by coupling EDEM ? RecurDyn to verify the rationality of the device design, and the main operation parameters and their value ranges affecting the defoliation of Artemisia argyi were determined. Finally, prototype tests were carried out, with stripping gap height, conveyor roller speed, and stem-pulling roller speed as test factors, while whole-plant de-leafing rate, damage rate, and impurity rate served as evaluation indicators. The results showed that under the optimal parameter combination of a stripping gap height of 6.8 mm，conveyor roller speed of 25 r/min, and stem-pulling roller speed of 383.7 r/min, the removal rate，damage rate, and impurity rate of Artemisia argyi were 83.59%，4.27%, and 4.74%, respectively，meeting the de-leafing requirements for Artemisia argyi.

Abstract:Currently, there are mechanized products available for pepper transplanting and harvesting, but manual operations are still required for pruning, which is crucial for increasing pepper yield. Considering the diverse planting environments for peppers, a self-leveling pepper pruning machine was designed and its leveling control system was developed. Based on the agricultural practice of pepper pruning, an end-effector for pruning was designed and its working principle was introduced. The flexible-body discrete element model of pepper plants was established by using EDEM software to simulate the interaction between the end-effector and the plant.Orthogonal experimental analysis was performed by selecting three parameters, namely feed speed，end-effector tilt angle, and roller speed, which had a significant impact on the pruning performance.The optimal parameter combination for the end-effector was determined as follows：feed speed was 1.5 m/s, end-effector tilt angle was 15°, and roller speed was 1 187 r/min. To meet the requirements of operating in complex hilly terrain, a dual-axis leveling mechanism was designed and its working principle was introduced. Dynamic simulation of the designed leveling mechanism was conducted in ADAMS, providing reference for the selection of the driving components. Simultaneously, the leveling control algorithm was determined, and the response speed of the traditional PID algorithm was compared with the fuzzy PID algorithm by using the Matlab/Simulink simulation module. Finally，a prototype of pepper pruning was built and field experiments were conducted. The experimental results showed that at a working speed of 1.5 m/s, the lateral branch cutting rate reached 86.3%, the pepper plant damage rate was controlled within 6.7%, and the final success rate of pepper branching was 76.7%, verifying the feasibility of the designed pepper pruning mechanism.

Abstract:Aiming at the problem of high kernel breakage rate in the mechanical threshing process of fresh corn in China, on the basis of the existing threshing device structure, a “flexible gyroscopic roller + axial drum reducer threshing” combination of flexible reducer threshing device was designed to achieve high efficiency threshing. On the basis of the mechanical characteristics of fresh corn cob，the design concept of package cutting was adopted to establish a mechanical model of cob and carry out theoretical analysis, and finite element simulation analysis was carried out for the core threshing parts of the fresh corn thresher to determine the main factors affecting the threshing performance.According to the theoretical analysis and simulation results, the threshing prototype was designed，the threshing performance test was carried out. Design-Expert software was used to design a threefactor, five-level quadratic regression orthogonal rotary combination test, with drum speed, gyro roller speed and rotor axis distance as the test factors, and grain breakage rate and cob failure rate as the test indexes. The results showed that the biggest influence factor on the grain breakage rate was the axis distance of the rotary axis, and the biggest influence factor on the maize ear uncleaned rate was the drum rotational speed;according to the regression equation model, the optimal conditions were predicted as follows：drum rotational speed was 106.630 r/min, gyro roller rotational speed was 230.001 r/min, and rotary axis distance was 54.00 mm. The verification test was carried out according to the actual working conditions under the optimal conditions. After three parallel tests, the actual kernel breakage rate was （4.956±0.152）% , and the cob not stripped rate was （4.158±0.085）%, which was within 5% of the predicted kernel breakage rate of 5.033% and cob not stripped rate of 4.303%, which was basically in line with the optimization of the parameters and met the requirements of fresh corn threshing. The research result can provide technical support for the optimal design and selection of fresh corn threshing equipment.

Abstract:In view of the low picking quality caused by inaccurate operating parameters in the mechanical picking process of coffea, the discrete element simulation software EDEM was used to construct the discrete element flexible model of Arabica coffea fruit-bearing branches in harvesting time, the accuracy of the model setup parameters was verified by comparing the simulation and actual test results of fruit-stalk tensile separation force and branch three-point bending fracture force. The EDEM-Recurdyn coupling method was used to construct the simulation model of Arabica coffea fruitbearing branches interacting with a vibratory picking device, the influence regularity of the operating parameters of the picking device on the picking quality during the picking process was analyzed by means of a single factor simulation test, and the operating parameters value range were defined. The quadratic regression orthogonal rotation combination simulation test of three factors and five levels was carried out with vibration frequency, inclination angle of the amplitude adjustment mechanism and vibration time as the test factors, and picking clean rate, picking ripening rate, and branch damage rate were used as the test indexes. The results showed that the importance of the effect of each factor on each test index in descending order was the vibration frequency, the vibration time and the inclination angle of the amplitude adjustment mechanism. The best picking effect was obtained through parameter optimization when the vibration frequency was 64 Hz, the inclination angle was 40 °, and the vibration time was 1.5 s. The field-picking test was conducted under the optimized operating parameters. The mean values of 92.54%，95.47% and 5.01% were obtained for the picking clean rate, picking ripening rate and branch damage rate, respectively, and the relative errors with the simulation test were 4.23%，3.39%, and 6.37%, respectively, which verified the reliability of the simulation test, and provided a theoretical basis and technical references for the study of the operating parameters and the optimization of the structure of the coffee picking machine.

Abstract:The forage crushers have the problems of high-power consumption, low productivity and so on, which restricts the development of this kind of equipment to the direction of low energy consumption and greening. To accurately predict the power consumption of the forage crusher and then optimize the design of low energy consumption, the power consumed by the forage crusher was divided into four parts based on the working principle of the forage crusher and the DEM-BPM-CFD coupling method： the power consumed by the interaction between materials and materials, air flow，and the mechanical structure （cutters, hammers and tooth plate）, power consumption for endowing the air in the forage crusher with static pressure and flow velocity, the power consumed by bearing friction and the power consumption of the whole machine vibration. At the same time, the mathematical models of power consumption and productivity per kW·h of each part were established respectively. The DEM-BPM-CFD coupling method was used to simulate the power consumption of the crushed materials of forage crusher, and the power consumption prediction model was verified by the power consumption test. Results indicated that the relative error between the theoretical calculation value of the power consumption prediction model and the measured value of the power consumption was 6.94 %, which showed that the power consumption prediction model of the forage crusher was basically accurate. The predominant power consumed by the interaction between the materials and the materials, the air flow, and the mechanical structure （cutters, hammers and tooth plate）, constituting more than three-fifths of the total power consumption. The subsequent significant the power consumption for endowing the air in the forage crusher with static pressure and flow velocity, representing over a quarter of the total energy usage. The power consumption of the whole machine vibration accounted for nearly one-tenth of the total power usage, respectively. The smallest proportion was the power consumed by bearing friction. The research result provided a foundation for the design of low-power forage crushers.

Abstract:Orchard wind-sending plant protection machinery has low real-time performance, large drift, and poor environmental adaptability. To reduce the drift loss of orchard air-assisted plant protection machinery and improve the deposition amount and uniformity within the canopy, a control system based on the tent sparrow search algorithm （TSSA） to optimize PID （proportional-integralderivative） parameters was proposed on the basis of the orchard air-assisted anti-drift sprayer. This system enhanced the optimization ability for PID parameters by introducing tent chaotic mapping，random following strategy, and dimension-by-dimension lens imaging reverse learning into the sparrow search algorithm, avoiding the PID system from falling into local minima, and improving the level of automation in wind curtain speed regulation. Consequently, it reduced the drift loss of droplets and enhanced the canopy deposition amount and uniformity. Simulation test results showed that compared with the contrast algorithms, the response time was reduced by 45.77%, and the overshoot was reduced by 13.22%, demonstrating superior automatic regulation capability. Actual test results indicated that the average error and the longest response time for adjusting the wind curtain speed were 2.11% and 0.8 s, respectively, which were 24.1% and 20% lower than those of other algorithms. Compared with the orchard air-assisted anti-drift sprayer, after applying this system, the drift of droplets, ground loss, and the coefficient of variation of droplet deposition distribution were reduced by 13%，16.13%, and 29.62%, respectively, while the canopy deposition amount was increased by 11.97%. This research achievement provided a technical solution for addressing the problems of pesticide drift loss and canopy internal deposition in orchards.

Abstract:The performance of harvesting equipment affects the quality of bales. The physical properties （geometry and density）, contact properties （static friction coefficient, dynamic friction coefficient and collision recovery coefficient） and mechanical properties （blade elastic modulus and stalk deflection） of alfalfa materials were tested. Using the discrete element method, the clover stalk discrete element model was constructed based on the flexible fiber particle model, the clover blade discrete element model was constructed based on the flexible thin shell particle model, and the stalk and blade joint model was constructed based on the linear elasticity model. Aiming at the results of stalk resting angle, stalk three-point bending, blade slip angle and blade cylinder compression test，Plackett-Burman experiment, Steepest ascent experiment, center composite experiment and single factor experiment were used to calibrate the mechanics and contact parameters of stalk and blade that affected the simulation results. The results showed that the simulation stalk deflection was equal to the mean value of physical experiment. The relative error between the resting angle of simulated stalk and the mean value of physical experiment was 0.46%. The relative error between the compression density of simulated blade cylinder and the mean value of physical experiment was 0.82%. The relative error between the simulation blade slip angle and the physical experiment average was 0.15%. The calibration parameters can truly reflect the material characteristics of alfalfa, and the research results can provide theoretical basis and model basis for the optimal design of alfalfa harvesting machinery.

Abstract:Aiming at the problems of slow steering response, crop row detection methods, and poor stability of tracking controllers in existing sprayers, a four-wheel-drive, differential-steering spraying robot was designed based on a light detection and ranging （LiDAR） navigation system. The whole structure of the robot was firstly designed, and the key components were designed according to the working principle. Then a crop row detection method based on 3D LiDAR was proposed. This method involved obtaining the crop point cloud in front of the robot through point cloud preprocessing and ground point cloud filtering. Subsequently, different crop rows were identified by analyzing the distribution of point cloud data across the transverse coordinate axes. The centerlines of the crop rows were then determined by fitting segmented geometric centers. Meanwhile, a dual-input single-output fuzzy controller was designed to use the yaw angle and lateral deviation obtained from the centerlines of the crop rows as inputs. The controller performed fuzzy inference by using 49 fuzzy rules and the Mamdani method. The outputs were then defuzzified into the differential wheel speeds for the wheels on both sides of the wire-controlled chassis by using the center-of-gravity method. Finally, the robot driving performance test and navigation performance test were conducted in the seeding cornfield. The results showed that the robot can successfully climb slopes over 20°, and the average deviation of the geometric center was 7.66 cm when performing a turn at differential speed. This indicated that the robot possessed adequate driving force and excellent steering flexibility. When LiDAR detected corn crop rows at the three-leaf stage and the small trumpet stage, the average error angles were 0.93° and 0.85°, respectively, with an average running time of 0.031 s. Utilizing this localization information, the robot achieved an average tracking error of 0.061 m with a standard deviation of 0.038 m when navigating the crop rows through the fuzzy control algorithm. This level of accuracy can meet the requirements for automatic navigation in corn fields during the seedling stage.

Abstract:A set of pest and disease detection and precise variable spraying system, based on visual recognition, was designed for maize, addressing traditional issues of pesticide waste and spraying inefficiency. Utilizing image processing and machine vision, this system automatically and accurately identified pests and diseases in maize fields, adjusting spraying doses accordingly. It was then integrated into a computer control system, with its performance verified. The system surpassed the benchmark model YOLO v5s, improving P, R, and mAP by 1.6，1.3 and 0.7 percentage points，respectively. The high precision rate reduced false detection of pests and diseases to avoid false spraying of non-pest areas. The high recall rate reduces missed detection to ensure timely and effective treatment of pest and disease areas. The improvement of mAP value comprehensively reflected the overall identification ability of the system in different pest and disease categories. It stably identified maize stem borer, slime molds, grey spot, leaf spot, and rust diseases with over 60% accuracy, and red spider and aphid with over 40% accuracy. Field tests evaluated droplet deposition, drift, and pesticide saving rates. The system achieved a minimum droplet coverage of 52% and deposition density of 71.3 drops/cm2, satisfying pest control needs. Pesticide saving and ground wastage rates reached lows of 32.1% and 22%, respectively, significantly reducing overall pesticide consumption and waste. This maize pest and disease detection and precision variable spraying system significantly enhanced identification accuracy, improved pesticide utilization, and reduced environmental pollution, offering a scientific and efficient approach to pest and disease management.

Abstract:In response to the lack of effective discrete element model for the simulation analysis of the interaction between soil-engaging components and soil in disturbed saturated paddy soil environment, the disturbed saturated paddy soil with high moisture content, high rheology and high adhesion was taken as the research object. A two-phase mixing model of soil particles and water particles was established by using the Hertz Mindlin with JKR contact model in EDEM software. The discrete element parameters of the disturbed saturated paddy soil model was calibrated through slump tests and soil bin tests. With the slump flow was the experimental indicator, the Plackett-Burman experiment was used to screen out three parameters from eight relevant parameters, namely, the soilwater JKR surface energy, the soil-soil rolling friction coefficient and the soil-soil recovery coefficient. The soil-water JKR surface energy and the soil-soil rolling friction coefficient had a highly significant influence on the slump flow, and the soil-soil recovery coefficient had a significant influence on the slump flow. A second-order regression model of the slump flow and significance parameters was established and optimized based on the Box-Behnken experiment. The results of variance analysis of single factor showed that the soil-water JKR surface energy had a highly significant influence on the slump flow, and the soil-soil rolling friction coefficient had a significant influence on the slump flow. The interaction between the soil-soil recovery coefficient and the soilsoil rolling friction coefficient had a significant influence on the slump flow. Furthermore, the optimal parameter combination was obtained as follows： the soil-soil recovery coefficient was 0.402, the soilsoil rolling friction coefficient was 0.136, and soil-water particles JKR surface energy was 0.920 J/m2.The traction resistance of sliding plate of the rice seeder during its movement in disturbed saturated paddy soil as the indicator, a simulation of the interaction between the sliding plate of the rice seeder and the disturbed saturated paddy soil was conducted based on the calibrated parameter combination, and verified by the soil bin test. The results showed that the average value of traction resistance of the sliding plate in the simulation was 130.239 N, and the average value of traction resistance in the soil bin test was 139.231 N. The resistance value error of the sliding plate model obtained from the simulation and test was 6.46%. The discrete element simulation of the disturbed saturated paddy soil was consistent with the test, indicating that the parameter calibration method was accurate and feasible. The discrete element model of disturbed saturated paddy field soil established can provide technical support for the study of the interaction between soil and soilengaging components.

Abstract:Jiangxi Province is an important rice-producing region in China. However, the low efficiency of mechanized rice production restricts the improvement of production capacity per unit of cultivated area. Focusing on different operational scales of mechanized rice production models, from the perspective of operational costs, five key stages were selected as input indicators, namely land preparation, planting, plant protection, harvesting, and drying, with output value as the output indicator. The super-efficiency SBM model and Cobb-Douglas production function were employed to measure the technical efficiency of full-process mechanized rice production under different production scales and terrain conditions. The results showed that low pure technical efficiency was the main factor limiting the improvement of the technical efficiency of full-process mechanized rice production models. The technical efficiency of production models in the hilly areas of central and southern Jiangxi was gradually decreased, making these regions critical for technical efficiency improvements. As production scales expanded, the technical efficiency of the production models initially rose and then declined, highlighting the importance of rational allocation between technical models and production scale in improving technical efficiency. The application of high-performance machinery was encouraged, such as large and medium-sized tractors, ride-on rice transplanters, and large-scale dryers, in the northern plain areas of Jiangxi. In the central and southern hilly areas，family farms and specialized large-scale households can be established firstly, radiating mechanization development to surrounding smallholders. Subsequently, large cooperatives can be formed, along with the establishment of seedling and drying centers, eventually evolving into comprehensive agricultural service centers. This path promoted the technical efficiency improvement of mechanized rice production models in the hilly areas of Jiangxi Province. Large-scale operators in the northern plain regions can develop a “self-operation + external service” model and extend into the “full industrial chain” model. Meanwhile, small-scale operators in the central and southern hilly areas can develop “self-operation” and “entrusted management” models, effectively integrating mechanization technology to improve production efficiency and increase farmers’ income.

Abstract:Mechanization of agriculture is an important foundation for transforming the way of agricultural development and improving rural productivity, and it is an important support for implementing the rural revitalization strategy. Without agricultural mechanization, there can be no agricultural and rural modernization. The 20th Congress of the Communist Party of China proposed to speed up the construction of an agricultural power and achieve Chinese-style modernization, and put forward more updated and higher requirements for the comprehensive and high-quality development of agricultural mechanization in the new era. In order to accelerate Guangxi’s agricultural mechanization to a full, comprehensive, high-quality and efficient development, the current situation and trend of agricultural mechanization development in the world and China were summarized, the current situation and demand of Guangxi’s agricultural mechanization development were analyzed through the investigation of the production mechanization of major crops such as sugarcane, rice, citrus and tea in Guangxi, the existing problems in Guangxi’s agricultural mechanization were sorted out, and development ideas were put forward. In order to promote the faster and better development of the production mechanization of diverse characteristic crops in Guangxi, and improve the comprehensive agricultural production capacity and competitiveness，suggestions and measures were put forward.

Abstract:Considering the issue that the tomato-picking robot’s recognition algorithm has a complex network structure and a large number of parameters, which severely limit the detection model’s response speed, an improved lightweight YOLO v5（EDH ? YOLO） algorithm was proposed. To significantly reduce computational complexity and model size while maintaining high recognition accuracy, the lightweight EfficientNet?B0 network was introduced as the backbone of the YOLO v5 algorithm. To better locate target objects during training and improve detection accuracy, the DIoU loss function was introduced. To reduce the model’s computational complexity and enhance its expressive ability, the lightweight Hardswish activation function was introduced. Experimental results indicated that the EDH?YOLO model achieved accuracy, recall, and average precision of 95.9%，93.1%, and 96.8%，respectively, with minimal loss in recognition performance. The size of model was only 7.3 MB, and the detection speed reached 53.2 f/s. Compared with the original YOLO v5 model, the model size was reduced by 55.3%, and the detection speed of the EDH ? YOLO model was increased by 60.0%.Compared with Faster R?CNN, YOLO v7, and YOLO v8, the EDH?YOLO model demonstrated higher robustness under various lighting and occlusion conditions. Additionally, the EDH ?YOLO model was deployed on the Android platform through model conversion to optimize the inference process, meet real-time recognition requirements for tomato fruits in complex greenhouse environments, and provide technical support for robot target recognition and automatic harvesting operations based on mobile edge computing in facility environments.

Abstract:Based on the sensitive characteristics of chlorophyll molecules to light absorption and reflection in the visible and near-infrared spectral range （400~1 000 nm）, a crop chlorophyll detector based on a contact image sensor can be designed to achieve non-destructive, rapid, and accurate detection of crop chlorophyll content. Firstly, a hyperspectral camera was used to collect the reflection spectrum of cornleaves in the range of 397～1003 nm, and the true value of leaf chlorophyll content was extracted by using spectrophotometry. Nextly, the screening of chlorophyll - sensitive response wavelengths was carried out. The Monte Carlo uninformative variable elimination（MC - UVE）algorithm was used to screen variables within the range of 10 to 50 feature wavelengths, and it was found that using 30 feature wavelengths provided the optimal detection capability for chlorophyll content. Simultaneously, the successive projections algorithm（SPA）was employed for feature wavelength screening. The two algorithms yielded a total of seven overlapping feature wavelengths. Further, through correlation analysis between the bands and chlorophyll content, low-correlation band was eliminated, ultimately resulting in six feature wavelengths. The selected feature wavelengths were used to choose the bands for the contact image sensor. The hardware of the device mainly included sensor image acquisition, main controller, display, and other modules, which realized the functions of near - infrared and visible light reflection spectrum data acquisition, processing, display, and storage of crop leaves. Sensor performance tests and field application tests were conducted. By analyzing the reflectivity of the obtained multispectral images, a partial least squares detection model for chlorophyll content was constructed，with a coefficient of determination for the validation set of 0.697. By analyzing the correlation between various vegetation indices and chlorophyll content, the normalized difference red edge（NDRE）, green minus red（GMR）, and normalized difference red edge（MTCI）vegetation indices with higher correlation were selected for further combined modeling, improving the detection model accuracy to 0.706. The model was embedded into the system, ultimately achieving rapid detection of chlorophyll content in the field and providing technical support for crop growth analysis.

Abstract:In order to be able to improve the accuracy and efficiency of the acquisition of three-dimensional information of field crops, taking wheat as the research object, this paper develops a set of field multi-sensor data acquisition device, using a self-propelled vehicle as the mobile carrier and a three-axis gimbal as the stabilisation carrier, and a tightly coupled point cloud acquisition system of LiDAR and IMU was constructed. By studying the imaging characteristics of the sensors and the acquisition method, a laser SLAM-based acquisition method was proposed to construct a high-precision point cloud map in the field, so as to accurately acquire the point cloud information of crops in the field, and be able to complete the construction of the map at a speed of 1.5 m/s, without the need to add additional field targets, which saved the resources for matching the point cloud at a later stage. On the basis of the point cloud map, pre-processing was completed by using straight through filtering, Octree-based downsampling and statistical filtering. An accurate extraction method of ground area based on verticality and height model was proposed. For the difficulty of obtaining the root point cloud during the growth period of wheat, the point cloud PCA analysis was used to calculate the normal vector of the point cloud for the calculation of verticality, and the secondary combination of the height model successfully segmented out the irregular ground points, and the new canopy height model was calculated by using the ground stabilisation fitting plane. Through statistical analysis, compared with the true value of manual measurement, the accuracy of SLAM-based three-dimensional map of wheat in the field, the root mean square error can reach 0.04 m;at the same time, the correlation coefficient between the canopy height extraction algorithm and the true value of manual measurement reached 0.979. The research can provide a powerful tool for the design of the three-dimensional trait collection system and trait analysis of wheat in the field.

Abstract:The efficient management of agricultural resources can be significantly improved through the accurate extraction of apple cultivation areas. In order to solve the problems of poor classification accuracy and time lag in apple planting area extraction, a CNN?LSTM temporal classification model was proposed based on Sentinel-2 and MODIS fusion images. The ESTARFM spatio-temporal fusion algorithm was firstly used to construct the fusion image, which complemented the strengths and weaknesses of different satellite images in spatial and temporal monitoring capabilities, and obtained image data with high spatial and temporal resolution. The random forest model was utilized to select the most optimal feature combinations from the initial 25 features, narrowed down to 15 key variables using backward feature elimination. In terms of classification models, convolutional neural networks（CNN）can well extract effective features in the spatial and spectral domains. As an improvement of recurrent neural network, long short-term memory network （LSTM） can handle unequal input sequences. The combination of the two networks proposed can extract effective features in the spatial, temporal and spectral domains to achieve more accurate image classification and remote sensing data analysis. Taking Guanshui Town, Muping District, Yantai City as the study area, the spatio-temporal fusion algorithm was utilized to compensate for the lack of images from a single Sentinel-2, and the CNN?LSTM model was used for apple tree planting area extraction. The CNN?LSTM model achieved an overall accuracy of 97.98% and a Kappa coefficient of 0.9586, outperforming the other four machine learning algorithms by 15.43 percentage points，5.25 percentage points，4.00 percentage points, and 3.31 percentage points，respectively. The overall accuracy and Kappa coefficient of the CNN?LSTM model were improved by 2.11 percentage points and 0.0148, respectively, compared with that of the LSTM model. The precise remote sensing extraction method for apple tree planting areas proposed can provide strong support for the development of scientific and rational agricultural management.

Abstract:Abnormal plant removal is a critical step in ensuring seed purity during hybrid rice seed production. To prevent abnormal plants from producing abnormal pollen that could compromise hybrid vigor, current abnormal plant removal operations require repeated manual efforts, consuming significant time and labor. The automation of abnormal plant identification in the field is fundamental to achieve mechanized and automated removal. Aiming to achieve automated and precise detection of abnormal plant in hybrid rice seed production, UAV aerial images of hybrid rice seed production fields containing abnormal plants were collected, and high-quality and distortion-free images were obtained through center cropping. The abnormal plants in the images were annotated, and data augmentation was performed through geometric and color transformations to create a dataset of abnormal plants in hybrid rice seed production fields. To address the high similarity between abnormal and normal plants in the image dataset, a novel abnormal plant detection network model，T-CenterNet2, was proposed. This model enhanced the CenterNet2 network by incorporating a texture-aware module within the feature pyramid network, which reorganized channel information to extract texture features from the feature maps, thereby increasing the feature distinction between abnormal plants and the background. Additionally, a combination of loss functions was designed， including a texture loss that measured the difference between texture features and label ground truth to control the texture-aware module. DIoU was introduced as the bounding box loss to improve the accuracy of target center point predictions, in line with the practical requirements of abnormal plant removal operations. The effects of different loss function combinations on model convergence speed and detection accuracy were compared, with the combination of weighted texture loss and DIoU yielding the best results, demonstrating the effectiveness of the redesigned loss function for abnormal plant detection tasks. Using mAP and recall rate as evaluation metrics, the improved model was compared with the original CenterNet2 model and four typical models—Faster R-CNN, FCOS，YOLOX, and DeTR. Experimental results showed that the improved T-CenterNet2 model achieved an mAP of 86.4%, an increase of 11 percentage points over the original model, and a recall rate of 82.5%, an increase of 11.6 percentage points over the original model. The highest mAP and recall rate among the typical models were only 73.1% and 66.2%, respectively. The enhanced model exhibited high detection accuracy and robustness, effectively achieving reliable abnormal plant detection.

Abstract:Fish mass is crucial for evaluating fish growth status, promoting precise feeding in aquaculture, and improving aquaculture efficiency. To accurately estimate fish mass, a fish mass estimation method based on dual dimensional feature extraction and natural gradient boosting （NGBoost）was proposed under the premise of using binocular cameras. Firstly, fish images were obtained through a binocular camera, and camera calibration and image correction operations were performed. Secondly，image processing technologies were used to segment the corrected image to obtain the fish target, and the two-dimensional features of the fish target were extracted. On this basis, stereo matching was performed to obtain the fish disparity map, extract the corresponding key matching points of the left and right images of the fish, and calculate the coordinates of the three-dimensional spatial feature points by using the triangular transformation principle, achieving the extraction of the three -dimensional features of the fish target. Finally, the method based on NGBoost was used to predict fish mass. Different dimensional features of fish from two - dimensional plane and three - dimensional space were extracted, solving the problem of inaccurate prediction of fish mass caused by single -plane dimensional features. At the same time, in addition to common three -dimensional features such as length and width, the fish depth ratio was also extracted, enriching the feature representation of the model and improving the accuracy of fish mass prediction. The crucian carp were taken as the experimental object and the proposed method was tested on the real dataset. The results showed that the mean absolute error（MAE）was 0.006 3 kg, the root mean square error （RMSE）was 0.008 7 kg, and the coefficient of determination（R2）was 0.928 7.Compared with various mass estimation methods, the performance of each evaluation metric of the proposed method has been greatly improved, predicting the fish mass more accurately.

Abstract:As one of the important aquaculture species in China, river crabs are well-loved by consumers.In the process of river crab aquaculture, scientific baiting is a key factor to ensure the healthy growth of river crabs and improve aquaculture efficiency. By comprehensively analyzing the factors affecting the baiting amount of river crab aquaculture, an ensemble learning algorithm was used to establish a prediction model for the baiting amount of river crab aquaculture. A data collection system was set up to collect key parameters such as river crab biomass, crab population, sex ratio, water pH value, temperature, dissolved oxygen, and crab feeding amounts to establish a baiting data set;data preprocessing techniques were used to smooth and normalize the data set to reduce the interference of outliers on the prediction results, and at the same time to eliminate the influence of different scales of the characteristic data;the particle swarm optimization （PSO） algorithm was introduced to improve the ensemble learning and establish a baiting model for river crab culture. The particle swarm optimization algorithm was introduced to improve the ensemble learning, and the bait quantity prediction model was established to realize the accurate prediction of the bait quantity of river crab aquaculture. The results of practical application tests showed that the average absolute error （MAE） of this model was 0.349 71 g, the root mean square error （RMSE）was 0.491 14 g, and the coefficient of determination （R2） of key performance reached 0.903 58.

Abstract:Straw, as the primary agricultural waste in China, is the main material for biochar production. The fixed carbon, volatile matter, and ash content are key indicators that influence biochar quality and guide the parameters of pyrolysis processes. Aiming to address the need for rapid detection of these indicators in straw carbonization production lines by designing a straw composition detection module. Utilizing near-infrared spectroscopy, a method for detecting straw components was developed. A portable spectral sensor was selected and a diffuse reflectance detection path was designed. A spectral collection unit was established to capture spectra of coarse-cut straw in the 1100~2500 nm range. By applying Savitzky-Golay convolution smoothing（SG）, multiple scattering correction （MSC）, standard normal variate （SNV） preprocessing, and partial least squares regression （PLS）, quantitative prediction models were developed for fixed carbon, volatile matter，and ash content by using full wavelengths and feature wavelengths selected by competitive adaptive reweighted sampling （CARS）. Results indicated that models based on feature wavelengths outperformed those using full wavelengths. The optimal models for fixed carbon, volatile matter, and ash content were SG+MSC-CARS-PLS, SG-CARS-PLS, and SG+MSC-CARS-PLS, with prediction set correlation coefficients R2 of 0.891 6，0.931 7, and 0.929 7, respectively. The root mean square errors of prediction set （RMSEp） were 1.46%，1.39%, and 0.42%, yielding relative prediction deviations （RPD） of 2.54, 3.44, and 3.18, demonstrating accurate prediction capabilities. Furthermore, an online detection module for straw composition was designed. The module was divided into three units： the spectroscopic acquisition unit, the power supply unit, and the control and transmission unit. The pre-built model for predicting straw composition was embedded in the module. Based on the Raspberry Pi 4B development board and its built-in Wi-Fi module, it enabled functions such as online spectroscopic acquisition, model computation, and data transmission for straw. Through prototype testing, it was demonstrated that the module design and window location selection could capture near-infrared spectroscopic curves that met the requirements for online analysis. By using the slope/intercept calibration method, the laboratory model was transferred to the production line for online application. The prediction accuracy of fixed carbon，volatile matter, and ash content was improved, fulfilling the requirements for online analysis and providing data support for the regulation of pyrolysis process parameters.

Abstract:In order to improve the problems of low alkali utilization, poor gas-liquid contact and low ammonia absorption rate in the treatment of kitchen waste digestate by ammonia blow-off technology，and improve the ammonia recovery efficiency of kitchen waste digestate and the economy of the process, a two-stage digestate ammonia blow-off process was proposed, and the effects of different calcium hydroxide additions and gas flow rates on the effect of ammonia blow-off of kitchen waste digestate were investigated;the effects of different pre-blowing conditions （liquid inlet speed, preblowing time, aeration speed） and ammonia blow-off conditions （soda injection volume, gas-liquid ratio） on the operating effect of the two-stage ammonia blow-off process were also evaluated;and the economic evaluation of the process was carried out. The effects of different pre-blowing （feed rate，pre-blowing time, aeration rate） and ammonia blowing conditions （alkali dosage, gas-liquid ratio） on the operation of the two-stage ammonia blowing process were investigated, and the economic evaluation of the ammonia blowing process of kitchen waste digestate was carried out. The results showed that the ammonia nitrogen removal rate of the digestate was increased significantly （P≤0.05）when calcium hydroxide was added at 4 g/L，8.2 g/L and 8.7 g/L, with the ammonia nitrogen removal rates of 85.00%，87.64% and 91.79%, respectively. The total nitrogen removal rate was 81.80%，82.45% and 83.46% （P>0.05） when the gas flow rate was 1L/min，2 L/min and 3 L/min， respectively;the optimal conditions for the two-stage ammonia blow-off process for kitchen waste digestate were determined by the ammonia blow-off device experiment as the first pre-blow-off for 8 h （air intake rate 20 L/min）, and the second alkali blow-off for 12h（Ca（OH）2 dosage of 8.7 g/L and air intake rate of 40 L/min）. The final ammonia nitrogen removal rate was 86.61%, and the total phosphorus removal rates was 22.21%. The two-stage ammonia stripping process was evaluated, and the cost of treating each cubic meter of digestate under alkaline ammonia stripping conditions was 7.584 yuan, and the cost of treating each kilogram of NH+4-N was 2.68 yuan. The results can provide a reference for the application of the two-stage ammonia blow-off process for treating kitchen waste digestate.

Abstract:Anaerobic digestion food effluent （ADFE） is rich in nutrients, and the residual liquid after ammonia stripping still contains a large amount of nitrogen and phosphorus nutrients, making it a potential high-quality culture medium for microalgae cultivation. Two microalgae，Chlorella sp. and Scenedesmus quadricauda（S. quadricauda）, were selected as the objects of study, and the residual liquid after ammonia stripping of ADFE （referred to as “ammonia stripping digestate”） was cultured，and the growth characteristics, purification effect of pollutants and secretion of extracellular polymers （EPS） were investigated in the digestate at different concentrations （20%，40%，60%，80% and 100%）. The results showed that both microalgae species grew well in medium to low concentrations of digestate （40%~60%）. Chlorella sp. achieved the highest biomass of 1.0 g/L in 40% concentration of ammonia stripped digestate, while S. quadricauda obtained the maximum biomass of 0.9 g/L at a 60% concentration. However, in high concentrations of digestate （80%~100%）, the growth of microalgae was somewhat inhibited, and the removal efficiency of nitrogen and phosphorus was decreased. When cultivating Chlorella sp. and S. quadricauda with medium to low concentrations of digestate （20%~ 60%）, the removal effects on total nitrogen, nitrate nitrogen, total phosphorus, and chemical oxygen demand （COD） were the best. The highest removal rates for Chlorella sp. were 41.14%，48.64%，77.70%, and 62.08%, respectively;while for S. quadricauda, the highest removal rates were 59.10%，58.39%，82.65%, and 63.43%, which were higher than those of Chlorella sp. Analysis of EPS also revealed that the content of EPS in microalgae was firstly increased and then decreased with extended cultivation time. Chlorella sp. had the highest content in 40% digestate, whereas S.quadricauda had the highest content in 80% digestate. Furthermore, three-dimensional fluorescence spectroscopy analysis indicated that the main organic components of EPS were soluble microbial byproducts and fulvic acids. Therefore, considering the microalgal biomass, nitrogen and phosphorus nutrient removal efficiencies, and the cost of digestate dilution，S. quadricauda showed high adaptability in ADFE treatment. The research result can provide insights into the sustainable management and resource utilization of digestate.

Abstract:A portable nondestructive testing device was developed based on near infrared spectroscopy technology, which was used to evaluate the viability of various peanut seeds. With a near-infrared spectrometer as its core component, the device offered advantages such as low cost and rapid detection, enabling efficient non-destructive viability assessment of peanut seeds across multiple varieties and states. It was found that during seed aging, nutritional components such as fat and moisture were significantly consumed, showing a strong correlation with seed viability. In order to improve detection accuracy, competitive adaptive re-weighted sampling （CARS） algorithm was used to accurately identify characteristic wavelengths of water and fat, which were mainly distributed in the ranges of 1 000~1 150 nm，1 250~1 350 nm and 1 400~1 500 nm. On this basis, quantitative prediction models of moisture and fat content were established. For moisture content, the SNV pretreatment model achieved a high correlation coefficient of 0.948 6 and a low RMS of only 0.292 7% on the prediction set. For fat content, SG-MSC pretreatment still produced the correlation coefficient of prediction set of 0.852 1 and a root mean square error of 2.569 9. On this basis, the sparse partial least squares discriminant analysis （SPLS-DA） model was introduced to establish a peanut seed viability discriminant model. Results showed that the improved model significantly improved the classification accuracy for seeds under various conditions. The classification accuracies for Luhua No. 8, Lili Hong, Luori Hong, and Xiaobaisha varieties reached 91.20%，90.80%，90.00% and 90.00%, respectively, an average increase of 15.60 percentage points compared with models not considering characteristic wavelengths. Specifically, seeds were determined to be nonviable when fat content was less than 45% and moisture content was below 4%. This method was particularly helpful in distinguishing mildly aged seeds and low-viability seeds that are difficult to accurately identify through traditional spectral classification methods. A Matlab-based peanut seed detection software was developed to achieve “ one-click operation ” for rapid seed viability detection，providing users with a convenient testing experience. The non-destructive testing device developed provided a method for quickly and accurately evaluating peanut seed viability, and had a wide application potential in seed quality control, breeding selection and agricultural production.

Abstract:In China, the absence of dedicated equipment for separating stems and leaves of wakame leads to issues of high labor intensity, elevated labor costs, and poor working conditions in manual operations. A vertical roller type wakame stem and leaf separation apparatus was designed by using salted wakame as study object. The primary structural characteristics and operational parameters influencing the separation of wakame stem and leaf were identified by performing kinematic and force analyses on the process. ANSYS/LS-DYNA was used to build a stiff flexible coupling model for wakame stem leaf separation, and single factor simulation tests were carried out with structural parameters acting as influencing variables. Using the stripping rate as the assessment index，orthogonal simulation experiments were performed. Using Design-Expert software, the stem leaf separation device’s structural characteristics were optimized based on the trial results. The ideal structural parameters were found to be 140 mm for the leaf stripping roller，15 mm for the embedded rod diameter, and 10 embedded rods. The aforementioned structure was used to construct a prototype of wakame stem and leaf separation apparatus, and experimental study was carried out. Orthogonal experiments were conducted with clamping roller speed, stripping roller speed, and meshing depth as influencing factors, and stripping rate and damage rate as evaluation indicators. To find the ideal operating parameters, the maximum stripping rate and minimum damage rate were established as constraints： a 60 r/min clamping roller speed, a 400 r/min leaf stripping roller speed, and a 2 mm meshing depth. At this point, there was a 5.5% damage rate and an 84.3% stripping rate. After experimental verification, a peeling rate of 87.0% and a damage rate of 5.0% were obtained. In conclusion, the wakame stem and leaf separation apparatus may efficiently satisfy production requirements while reducing wakame damage.

Abstract:To address the challenges of low mechanization and limited theoretical research in shellfish harvesting on Chinese mudflats, a crawler mudflat shellfish mechanized brush-screen cooperative vibration harvester was designed for harvesting mudflats shellfish. This device integrated a double-deck vibrating screen and multi-stage roller brush system to efficiently excavate, screen，and transport shellfish. The white clam was used as the research object. The theoretical analysis of the double-deck vibrating screen and the first double-spiral harvesting roller brush were conducted and the key structural and operational parameters were then calculated. The mudflat shellfish harvesting single-factor test was conducted by using discrete element method （DEM） and multi-body dynamics （MBD） coupled simulations. The identified key structural parameters included a crank length of 10 mm for the double-deck vibrating screen and a spiral angle of 30° for the first double-spiral harvesting roller brush. The prototype was developed, and mudflat shellfish harvesting orthogonal field tests were conducted in Jinzhou City, Liaoning Province. The tests considered operational parameters such as crank rotation speed, the first double-spiral harvesting roller brush rotation speed, and secondary cleaning roller brush rotation speed. The optimal parameter combination was determined to be a crank rotation speed of 870 r/min, the first double-spiral harvesting roller brush rotation speed of 65 r/min, and secondary cleaning roller brush rotation speed of 110 r/min. Under these conditions, the device achieved a clam harvesting efficiency of 133.80 kg/h, a clam breakage rate of 5.25%, and a clam missing rate of 7.46%. Post-harvest, the shear strength of the mudflat at a depth of 50 mm was decreased by 65.13%, and the juvenile clam return rate was 92.29%, supporting the sustainable cultivation of shellfish. This device met the production requirements for mudflat shellfish harvesting and it can serve as a valuable reference for the mechanization of shellfish harvesting in China.

Abstract:The high energy consumption and quality degradation caused by low drying efficiency pose significant challenges in hot-air drying processing of fruits and vegetables. To enhance the hotair drying quality of wolfberry, pretreatment with different alkaline media soaking methods （3% Na2CO3 and alkaline electrolyzed water soaking for 1 min，10 min, and 20 min, respectively, was employed. The hot-air drying characteristics, microstructure, color, rehydration ratio, shrinkage，and nutritional content of wolfberry were investigated. The results revealed that compared with the control group, alkaline electrolyzed water pretreatment significantly reduced the hot air-drying time required for wolfberries （P<0.05）, with the shortest drying time observed in group treated by 10 min soaking of alkaline electrolyzed water. This group achieved a higher drying rate and an effective moisture diffusivity of 5.020×10?8 m2/min. After alkaline electrolyzed water pretreatment, the gaps between the waxy stripes on the epidermis were enlarged, with some stripes even dissolving or breaking. When pretreated with alkaline electrolyzed water for 10 min, the hot-air dried wolfberry exhibited the closest color to fresh samples, coupled with superior rehydration capabilities, reduced shrinkage and significantly elevated levels of polysaccharides （9.79%）, total phenols （4.18 mg/g），and total flavonoids （6.42 mg/g） compared with the control （P<0.05）. Pectin composition underwent notable transformations upon alkaline electrolyzed water pretreatment and drying. While fresh wolfberry primarily contained sodium carbonate-soluble pectin （SSP）, the content of chelator-soluble pectin （CSP） increased post-treatment, suggesting a reconfiguration of pectin fractions. In conclusion, pretreating wolfberries with alkaline electrolyzed water for 10 min produced the hot-air dried wolfberries with best quality. The research result can not only present a straightforward and effective pretreatment strategy for improving the hot-air drying of wolfberry but also can offer a green and environmentally benign approach for enhancing the quality and efficiency of drying processes for a wide range of fruits and vegetables.

Abstract:With the rapid development of agricultural and rural economy, the problem of rural labor shortage has become increasingly prominent. As one of the important links, picking has been developed to develop a riding tea picking machine to improve the mechanization level of tea gardens.Based on the principle of human biomechanics, the effects of human body parameters on vertical dynamic equivalent mass, horizontal dynamic equivalent mass and seat-head transfer function were analyzed by establishing a human body model consistent with human vibration characteristics.Through the simulation of vertical transfer rate, rotational transfer rate and apparent mass, it was concluded that low-frequency resonance occurred in the range of 4 ~6 Hz，8 ~10 Hz and 20 ~30 Hz.At the same time, tea field test was carried out on the whole vehicle, and objective data such as driver vibration acceleration was obtained by acceleration sensor, and the transmission, time domain and frequency domain characteristics in the resonant frequency range of high frequency and low frequency band were analyzed. Then by installing polyurethane foam seats and other vibration reduction means to reduce the transmission rate and avoid resonance, the purpose of improving the comfort of the riding tea picker was achieved. The test results showed that in the low frequency band，the transmission rate at the seat was reduced from 0~1.0 to 0~0.2, and the peak value was reduced by 80%, thus improving the noise and vibration performance of the riding tea picker. The human body model had a certain prediction accuracy, which can provide an important reference for human machine interface design in dynamic environment, and has a certain guiding significance for comfort in vibration environment and further research on human vibration.

Abstract:Aiming at the issue of overweight in gantry-type wide-span operation platforms for agricultural machinery, research on structural lightweight design was conducted and a method to achieve structural weight reduction was proposed by addressing the high complexity of structural analysis models. The corresponding implementation process was also provided. Firstly, to tackle the complexity and detail density of the complete model, an equivalent elastic support modeling method was developed by using surrogate models, which significantly simplified the analysis model for noncritical regions while ensuring the accuracy of the simulation in the core areas and effectively reducing computational costs. Subsequently, sensitivity analysis was employed to identify key parameters affecting stress and mass in the design, addressing the issue of excessive design parameters resulting from component diversity. Finally, a lightweight design solution was obtained by using response surface optimization. Compared with the existing design, the optimized longitudinal beam structure reduced the mass by approximately 1 424 kg, achieving a 4.31% reduction in total vehicle mass, and the specific mass was decreased to 118.67 kg/kW, while still meeting strength and stiffness requirements. The lightweight design methodology established can provide a foundational approach and reference for the development of gantry-type wide-span operation platforms for agricultural machinery.

Abstract:The typical characteristics of agricultural tires include large load fluctuations, special pattern shapes, harsh working environments, and significant tire body vibration. These features make it difficult to accurately obtain the vertical load of the tire in practical operations. However, vertical load has a significant impact on the performance of agricultural machinery and is a key factor in evaluating and optimizing the efficiency and stability of agricultural machinery operations. A state estimation method for agricultural tires based on sidewall bending strain is proposed to address the difficulties in obtaining vertical loads and the low estimation accuracy of traditional models. A tire state estimation system that integrated high-precision sidewall bending strain sensors, tire temperature and pressure sensors was designed based on the bending strain law of the tire sidewall under vertical load. A bending strain information collection experimental platform was established and various typical working condition testing experiments were conducted through the platform. Strain signals of tire sidewall under different tire pressures, speeds, and loads during the rolling process of non-road tires were obtained. A dataset was established for the bending strain, tire temperature, and tire pressure of the wheel rim sidewall during its rolling process. After denoising, screening, and feature extraction, the periodic strain curve and periodic features were extracted from the strain signal. Furthermore, a multi feature weighted vertical load prediction network （MVL-Net） and speed prediction network based on deep neural network （SDNN） were constructed to accurately and realtime estimate the vertical load and speed of the tire. A dataset of strain signals and tire temperature and pressure was established, and a multi-feature weighted vertical load prediction network （MVLNet） and speed prediction network based on deep neural network （SDNN） were constructed. The prediction results showed that the mean relative error （MRE） of the MVL-Net was 1.26%, and the root mean square error （RMSE） was 18.42 kg, which was 27.17% and 26.32% lower than that of the BP network, respectively. The MRE of the SDNN was 1.16%, and the RMSE was 0.10 km/h, which was 24.18% and 16.67% lower than that of the BP network, respectively. Ten-fold cross validation experiments were conducted, and the results showed that the MVL-Net and SDNN had good generalization ability. Research result showed that the proposed state estimation method of agricultural intelligent tire based on sidewall bending strain can achieve accurate prediction of state information such as vertical load and rotational speed of agricultural tires.

Abstract:To improve the precise regulation of cold chain equipments, an experimental platform for phase change material （PCM） cold storage refrigerated trucks was established to investigate the effects of factors such as the number of cold storage plates, door opening frequency, ambient temperature, cargo load, and driving speed on the cold storage time. The experimental results showed that increasing PCM, reducing the door opening frequency, lowering the ambient temperature and driving speed, and reducing the cargo load all contributed to prolonging the cold storage time. Among these factors, the effect of the driving speed and the cargo load on the cold storage time was less than 5% impact on the cooling time, making them minor factors. When the mass of PCM was increased from 120 kg to 220 kg, the cold storage time was extended by 715 min;when the ambient temperature was increased by 14 ℃, the cold storage time was reduced by 135 min;opening the door 1 to 4 times shortened the cold storage time by 6.39% to 50%. To better quantify the influence of multiple factors on the cold storage time, a high-precision cooling capacity algorithm model with a relative error of less than 9% was developed based on theoretical derivation. Furthermore, a display module for residual cooling capacity and the cold storage time was developed by using this model, providing theoretical support for the formulation of control strategies for PCM cold storage refrigerated trucks.

Abstract:To enhance the wear resistance of iron-based self-sharpening blades and improve the service life of agricultural machinery components, fiber-optic coaxial powder-fed laser cladding technology was employed on the surface of self-sharpening blade molds （65 Mn）. By adjusting the cladding scanning speed, a systematic analysis of the wear resistance and hardness of Fe901 hard alloy coatings was conducted. Techniques such as XRD, SEM-EDS, and friction and wear testing were used to analyze the cross-sectional morphology, phase composition, hardness, and wear resistance of the cladding coatings. The results showed that the microstructure of the Fe901 ironbased hard alloy coating consisted primarily of austenitic columnar and equiaxed dendrites, with needle-like （Cr, Fe）7C3 carbide precipitates observed around the matrix. The main phases of the coating included α-Fe solid solution，（Cr, Fe）7C3 mixed carbides, and CrFeB hard phases. At high scanning speeds of 3m/min and 4.2 m/min, the average hardness of the coating was 767.80 HV0.3 and 829.97 HV0.3, respectively, which were 2.79 and 3.02 times of the hardness of 65 Mn spring steel substrate （275.2 HV0.3）. Moreover, the wear volume per unit area of the coating was reduced by 27.39% and 32.78% compared with that of the uncoated substrate, significantly enhancing wear resistance. The research not only effectively improved the hardness and wear resistance of the Fe901iron-based hard alloy coating but also demonstrated significant potential for enhancing the performance of iron-based self-sharpening blades.