Abstract:As a crucial strategic and economic crop, cotton faces pressing needs to advance full-process mechanization in its production, particularly in the context of structural labor shortages in rural areas and continuously rising production costs. This advancement is essential for enhancing industrial competitiveness and ensuring national cotton security. The development status, representative models, structural features, and relevant technologies and equipment were systematically reviewed, both domestically and internationally, focusing on five key stages: tillage, seeding, field management, harvesting, and residual film recovery. It indicated that while China had essentially achieved full-process mechanization in cotton production, challenges persist, including insufficient performance and reliability of core equipment, reliance on imported key components, weak foundational research on soil-machine-crop interaction mechanisms, low levels of equipment intelligence, and uneven mechanization development across regions. It was proposed to focus on weak links such as harvesting, seeding, field management, and residual film recovery, and to make breakthroughs in core technologies. Additionally, strengthening the integration of fundamental research with cutting-edge technologies was crucial. The promotion of precision operation systems based on Beidou navigation, multi-source information perception, and intelligent decision-making was recommended to establish an integrated smart production system. Furthermore, formulating regionally differentiated development strategies would advance the R&D of equipment suitable for small and medium-sized fields and innovative socialized service models.

Abstract:In view of the low picking rate of remaining cotton recycling device in the mechanized cotton picking field in Xinjiang, combined with the planting mode of the cotton field in Xinjiang and the operation requirements of the mechanical picking up of the remaining cotton, a kind of strap - spring tooth type residual cotton recovery device was designed. The structure and working principle of the device were described, and the operation process analysis and key parameters design of the key components of the pick-up drum were carried out. According to the design results, a prototype was trial-manufactured. Taking the picking rate of cotton leftover from the ground and cotton pick-up rate on branches as the test index, the working speed of the machine, the speed of the picking drum and the spacing of picking straps as the test factors, the three-factor and three-level Box - Behnken response surface analysis test was carried out, and the mathematical regression model between the evaluation index and the influencing factors was established by Design-Expert 10.0 software. The influence of significant factors on the evaluation indicators was analyzed, the test parameters were optimized, and the optimal parameter combination was determined to be as follows: when the machine operating speed was 0.7 m/s, the pickup roller speed was 260 r/min, and the pickup strip spacing was 120 mm, the picking rate of cotton leftover from the ground was 90.01%, and cotton pick-up rate on branches was 89.76%. All performance indicators met the design requirements.

Abstract:Aiming to address the shortage of agricultural machinery in the cotton cultivation and sowing mode of wheat in the Yangtze River basin, a combined operation mode integrating rotary tillage, stubble removal, ridge formation and precision sowing was proposed, and a ridge forming device ( which mainly included boat-type side plate, contour-mimicking ridge plate, contour-mimicking ridge plate connecting mechanism and shaping roller) was designed. Firstly, the mechanical parameters of wheat stubble, soil and rotary tiller were tested in the middle and lower reaches of Yangtze River, and the discrete element simulation model was established. Secondly, based on the throwing situation of rotary tiller in the discrete element simulation, the boat-type ridge side plate and the contour ridge plate were designed respectively to reduce the wear between soil and components. The discrete element method (DEM) with multi-body dynamics (MBD) coupling simulation was employed to validate the designed ridge-forming device. A wear life calculation method was also proposed. The coupling simulation results demonstrated that the minimum service life of the boat-type ridge-forming side plate was 2 577.8 h, with a ridge-forming stability coefficient of 98.4%. Field tests confirmed the wear patterns matched the simulation results, showing a minimum service life of 2 430 h (5. 7% deviation from simulation) and a stability coefficient of 92. 26% (6.2% deviation from simulation). Ultimately, the field emergence results met the agronomie requirements for direct cotton seeding after wheat harvest in the Yangtze River Basin. The methodology developed thereby can provide a theoretical framework and practical reference for the design and optimization of ridge-forming devices and their soil-engaging components.

Abstract:Cotton topping is a crucial management practice in cotton farming, and accurately and rapidly detecting top buds in complex field environments is a key step to achieving precise topping. To address this challenge, a lightweight visual detection model, RE-YOLO-QAT, based on the YOLO 11n framework, was developed. The model was improved upon the YOLO 11n architecture by replacing its backbone network with the EfficientViT, a lightweight vision transformer. This replacement, coupled with a hybrid attention and convolutional structure, effectively reduced the model's computational costs while maintaining its feature extraction capabilities. Furthermore, the model incorporated a reparameterized feature pyramid network (RepGFPN), which significantly enhanced the model's ability to detect small top buds, improving detection accuracy in the field. Additionally, the model employed quantization-aware training and structured pruning techniques to dramatically compress the model size and reduce computational costs without significantly sacrificing detection accuracy. These optimizations helped ensure the model meet the real-time detection requirements necessary for efficient cotton topping operations. The experimental results demonstrated that the RE-YOLO-QAT model achieved a cotton top bud recognition rate of 94.2% in complex field scenarios. The model contained only 1.01×10? parameters and required just 2.3×10? FLOPs, which was a significant reduction in computational cost. Compared with the baseline model, RE-YOLO-QAT reduced the computational cost by 64.06% while suffering a negligible accuracy loss of just 0.2 percentage points. This made the model highly efficient, suitable for real-time detection in cotton topping operations. Overall, the results indicated that this research provided both the theoretical foundation and the technical framework necessary for the development of intelligent, precise, and efficient cotton topping systems in future agricultural operations.

Abstract:Aiming to address the issue of spray boom vibration seriously affecting operation quality in complex field environments, the vibration transmission characteristics of the "excitation source - frame - spray boom" structural chain was investigated. Using a 3WPZ - 1800G high-clearance sprayer as the research object, a method combining experimental testing and operational transfer path analysis (OTPA) was employed. Vibration response data were acquired by conducting three types of tests: static engine excitation, unilateral impulse response, and field driving excitation. Triaxial accelerometers were arranged at eight key measurement points to capture data under varying liquid loads (0 L, 600 L,1 200 L, 1 800 L), engine speeds ( 800 r/min, 1 200 r/min, 1 600 r/min), and driving speeds (6 km/h, 8 km/h, 10 km/h). The results indicated that under static load conditions, engine excitation had a limited impact on the spray boom, with high-frequency components (greater than 40 Hz) decaying rapidly along the transmission path. Under unilateral impulse excitation, the spray boom end exhibited a typical "whiplash effect", with a peak acceleration of 40.18 m/s2, which was 8.6 times that of the frame measurement point;the first-order bending mode frequency of the boom was approximately 1.97 Hz. Under field driving conditions, the acceleration RMS value at the left end of the spray boom was increased by 310% compared with flat roads, and the dominant vibration frequency was decreased from 12. 20 Hz to 2. 13 Hz. The coupling between low-frequency road excitation and the low-order mode of the spray boom was identified as the root cause of vibration deterioration. OTPA analysis revealed that under typical field conditions (1 200 L load, 8 km/h driving speed), road excitation contributed 50. 88%, liquid sloshing 28. 53%, and engine excitation 20. 59%. The coupling index (CI) in the main liquid sloshing frequency band (0. 5 ~5 Hz) reached 0. 462, indicating a significant synergistic amplification effect between road excitation and liquid sloshing. Based on the analysis of multi-source contributions, an active-passive synergistic control strategy featuring "passive optimization for resonance suppression and active control for low-frequency tracking" was proposed. The research results can provide a theoretical basis for the suspension optimization and vibration control of high-clearance sprayers.

Abstract:Aiming to address the issue of lateral tipping instability that high-clearance sprayers are prone to roll when subjected to road excitations, an integrated control strategy for maneuvering stability was proposed based on additional active steering. An eight-degree-of-freedom vehicle dynamics model, incorporating a high-precision magic formula tire model, was established to accurately describe the vehicle's nonlinear dynamic characteristics. Based on sliding mode variable structure control theory, a two-layer lateral stability controller was designed: the upper controller was the vehicle body attitude sliding mode controller, responsible for calculating the additional generalized torque required to maintain vehicle body stability;the lower controller was the electro-hydraulic position servo sliding mode controller, which precisely executed the steering adjustment commands given by the upper controller. A test platform for sprayer lateral stability control was constructed to verify the effectiveness of the controller. Test results showed that under step steering conditions, the absolute mean errors of yaw rate, roll angle, and centroid lateral deviation angle were decreased by 15.7%, 9.3%, and 35.0%, respectively;under sinusoidal conditions, the absolute mean errors of yaw rate, roll angle, and centroid lateral deviation angle were decreased by 17.8%, 8.6%, and 25.0%, respectively. The results validated that the designed active steering strategy effectively improved the lateral stability of the sprayer. This fully demonstrated the effectiveness of the control strategy in suppressing vehicle body roll and maintaining the driving path, providing a valuable solution for improving the active safety performance of high-clearance sprayers under low-speed complex conditions.

Abstract:Aiming to enhance the comprehensive steering performance of a high-clearance self-propelled sprayer operating under complex and uneven field conditions, focusing on improving steering response speed, driving stability, minimizing turning radius, and mitigating tire sideslip-critical factors for efficient and damage-free crop protection operations, based on a hybrid control strategy that integrated proportional feed forward control with yaw rate feedback, a full hydraulic four-wheel steering system employing a fuzzy PID controller was proposed and developed. Co-simulation conducted in AMESim and Matab/Simulink demonstrated that the designed control system achieved precise coordination between front and rear wheels, with the maximum following error of the rear wheel angle relative to the front wheel command remaining within ±1.8°, fully satisfying the geometric constraints of Ackermann steering theory. To further validate the system's effectiveness, a physical prototype test platform was constructed. Random steering tests were performed at a representative working speed of 4 km/h in four-wheel steering mode. Notably, the implementation of the four-wheel seering mechanism reduced the vehicle's minimum turning radius from 7.9 m (in two-wheel steering mode) to 4. 8 m-a 39. 2% reduction-significantly enhancing maneuverability in narrow rows and headland turns, while simultaneously improving lateral stability and operational efficiency during fieldwork. These outcomes underscored the system's potential to facilitate the transition of agricultural machinery from conventional mechanical steering toward intelligent, adaptive, and high-performance autonomous operation.

Abstract:In order to address the problems of small target scale, high overlap, and complex background of cotton leaves affected by aphids in field environments, traditional detection methods often struggle to balance accuracy and real-time performance, resulting in limited effectiveness in practical applications. Therefore, an improved lightweight detection model was proposed. On the basis of YOLO v8n, an adaptive downsampling module ( ADown) was added to enhance local feature extraction, a spatially enhanced attention module (SEAM) was introduced to achieve multi-scale information exchange, and a Focal - EloU loss function was constructed for dense small object perception, effectively improving the model's adaptability to small objects and complex backgrounds. The experimental results on the self built dataset showed that the improved model achieved significant improvement in detection accuracy while significantly reducing the number of parameters. The average accuracy reached 96.2%. In addition, the ablation study further demonstrated the effectiveness of each module in enhancing the overall performance of the cotton aphid infestation severity detection model. The comprehensive results indicated that the improved model balanced detection accuracy and model deployment efficiency while ensuring lightweight, providing a feasible solution for intelligent monitoring of agricultural pests.

Abstract:The dynamic characteristics and shift response of power shift transmission of cotton picker are the key factors affecting the shift quality of cotton picker. Taking a cotton picker power shift transmission as the research object, a performance research method of cotton picker power shift transmission was put forward based on Amesim mechanical-hydraulic coupling simulation and bench test. Firstly, based on the mechanical transmission structure and hydraulic system principle of power shift transmission, the simulation model of the power shift hydraulic coupling system of the cotton picker was constructed. Secondly, a comprehensive test bench of power shift transmission of a coton picker was designed and built, which realized the real-time acquisition of input and output speed, torque, and shift oil pressure of the power shift transmission. The accuracy of the simulation model was verified by comparing the transmission ratio change and shift oil pressure response between simulation and test under typical working conditions. Finally, a three-factor and three-level orthogonal test was used to quantitatively analyze the significance of the influence of input speed, load torque, and main oil pressure on the maximum impact degree of the output shaft and shift steady-state time. The results showed that the dynamie response trend of power shift transmission simulation was consistent with that of bench measurement. The amalysis of range and variance of orthogonal test showed that the oil pressure of main oil circuit was the most significant factor affecting the maximum impact degree of output shaft, while the speed of input shaft was the most significant factor affecting the steady-state time of shifting. To consider shifting speed and smoothness, the oil pressure of the main oil cireuit should be increased at the initial stage of shift to shorten the engagement time, and the oil pressure rise rate should be reduced in the middle and late stages of shift, to suppress the impact of the output shaft and realize the balance optimization of shift time and impact degree. The research results can provide a reliable theoretical basis and test platform support for performance prediction and control strategy optimization of the power shif transmission of the cotton picker.

Abstract:Aiming to address the challenge of high-fidelity modeling and rapid parameter optimization for machine - crop - soil coupling systems under small-sample conditions, taking the cotton stalk pulling device as an example, a cross-scale multi-objective optimization framework that integrated the discrete element method (DEM) with physics-informed neural networks (PINN) was proposed. The framework consisted of DEM-based microscale modeling, mechanical relation extraction, PINN physical-constraint embedding, and NSGA -II optimization. Firstly, a composite "rubber roller - cotton stalk - soil" DEM model was established, from which the stick - slip - separation contact mechanism was extracted. The geometric indentation and equivalent contact stiffness were merged into an identifiable parameter and incorporated into the PINN as a primary physical constraint to ensure physical consistency. Subsequently, with stalk breakage rate and miss-pulling rate as dual objectives, NSGA -II was employed to compute the Pareto front within engineering feasibility bounds and supported by sensitivity analysis, and the knee point was selected as the optimal operating condition. Results showed that the proposed framework reduced the computational cost per case from approximately 8 h by using DEM to 2 s by using PINN forward inference, achieving a validation R2=0.95 and improving prediction accuracy by 20% ~ 30% compared with conventional neural-network surrogates. The optimal operating condition corresponded to a breakage rate of 8% and a miss-pulling rate of 7%, with a field-test deviation of less than 2%. Overall, the proposed DEM - PINN cross-scale framework maintained physical consistency while significantly reducing optimization cost, demonstrating strong generalizability and transferability for soil - plant - machine coupling systems.

Abstract:In order to meet the needs of collaborative operation of cotton stalk returning to field and residual film recovery in Xinjiang cotton area, aiming at the problems of low qualified rate of cotton stalk crushing and high falling rate of crushed materials in the operation of existing cotton stalk crushing-residual film recovery machine, a Y-type cutter-tooth cotton stalk crushing and returning device with front-mounted residual film recovery machine was designed. The device can effectively improve the qualified rate of cotton stalk crushing, reduce the falling rate of crushed materials and reduce the occurrence of conveying blockage through the collaborative operation of cutter-tooth blade crushing and screw conveyor side throwing. The working principle of the device was expounded, and the structural optimization design of key components such as knife-tooth flail and screw conveyor was carried out. The trajectory equation of the flail was established, and the influencing factors of operation of crushing device were determined. The MBD - EDEM multi-body dynamics-discrete element coupling simulation was used to establish the interaction model between the crushing device and the straw. The number of flails, the rotation speed of knife shaft and forward speed were selected as the experimental factors. The three-factor and three-level simulation test was carried out with the cotton stalk crushing pass rate and the crushing drop rate as the evaluation index. The test results showed that when the screw flail was 32, When the rotation speed of the cutter shaft was 2 100 r/min and the forward speed was 1.5 m/s, the crushing performance was better. The qualified rate of cotton stalk crushing length was 93. 05%, and the crushing drop rate was 2.5%. The cutter shaft was analyzed under the optimal combination. The modal analysis of the cutter roller was carried out by ANSYS. The natural frequeney of the lowest order of the crushing cutter roller was 73. 541 Hz, which was much higher than the working frequency (31. 67 ~ 38. 33 Hz), and no resonance occurred. The simulation results shoved that the device structure and operating conditions met the design requirements. The field test results showed that the qualified rate of cotton stalk crushing was 94.3%, the unevenness of scattering was 13.8%, and the average stubble height was 75.6 mm, which were better than that of the national standard requirements.

Abstract:Aiming at the problems of low operating efficiency and insufficient residual film recovery rate of the existing traction residual film recycler, as well as the poor separation effect of membrane impurities due to the dispersed function of the film collecting system and the weak adaptability to the complex working conditions in the field, a self-propelled residual film recycler was developed, focusing on the design of the integrated film collecting-membrane impurity separation. A self-propelled film recycling machine was developed, focusing on the design of the integrated "film collection - film separation - film transport - film removal" function in one of the spiked belt film collection system. Combined with the agronomic requirements of Xinjiang cotton fields and the analysis of mechanical characteristics of residual film gripping, the staggered arrangement parameters of the film harvesting spike teeth and the key structural dimensions of the main stripping drive roller were determined;the dynamic model of the film-collecting belt system was constructed based on the ADAMS software, and the transverse deviation of the belt joints and fluctuating characteristics of the tension were analyzed within the range of the system's rotation speed of 118 ~ 158 r/min. The simulation results showed that in this speed range, the lateral offset of the belt segments was no more than 4 mm, and the fluctuation of tension force was controlled within ±15%, which verified the dynamic stability of the transmission system. Taking the forward speed of the implement, the speed of the film-retracting roller and the inclination angle of the belt as the test factors, the Box - Benhnken experimental design method was used to carry out the field test, and the optimal combination of operating parameters was obtained by using the response surface method of optimization: the forward speed was 8 km/h, the speed of the film-retracting roller was 138 r/min, and the inclination angle of the belt was 89°. The results of the validation test showed that the residual film recovery rate reached 91.17% under the parameter conditions. The research result can provide a theoretical basis and technical support for the optimal design of the film collection system of self-propelled residual film recycling equipment and the efficient operation on a large scale.

Abstract:Aiming at the problem of missed collection of residual films in narrow rows due to the interaction between root stubble and soil in the drum-type residual film recovery machine, a diagonal residual film collection and recovery device was designed. By optimizing the arrangement of the film-picking teeth and the width and density, the number of pickups per unit area was increased, thereby improving the rate of residual film collection ( without edge film). Through theoretical analysis, it was determined that the spacing of the pick-up teeth was 160 mm, the number of circumferential pick-up teeth was 8, and the pitch of the double helix arrangement on the drum was 320 mm. The working parameters of the device were determined through motion analysis. The forward speed of the machine was 5 ~9km/h, the rotation speed of the picking-up drum was 40 ~ 80 r/min, and the depth of soil penetration was 30 ~ 50 mm. A single-factor experiment was conducted by using the EDEM - Adams joint simulation, which narrowed the range of working parameters. Based on the results of the single-factor test, the forward speed of the machinery, the rotational speed of the picking-up drum, and the depth of soil penetration were taken as the test factors, and the residual film pickup rate was taken as the test index for the orthogonal combination simulation optimization test. The optimal parameter combination obtained was as follows: the forward speed of the machinery was 6. 2 km/h, the rotational speed of the picking device was 65 r/min, and the depth of the picking teeth into the soil was 41 mm. At this point,the residual film pickup rate was 98.96%. Field trials were conducted to verify that the average residual film recovery rate of this device was 92. 96%, improving the residual film recovery rate and significantly addressing the issue of leftover film being missed.

Abstract:Currently, most residual film recycling machines lack an effective operational status monitoring system, making it difficult for operators to grasp the working conditions of the equipment in real time and respond promptly to sudden failures. At the same time, the calculation of the working area still relies on manual or post-event statistics, resulting in insufficient real-time capability and accuracy. These two issues significantly restrict operational efficiency and compromise the recovery process. To address this, a residual film recycling machine operational status monitoring system was designed based on "device-edge-cloud" collaboration. The system achieved real-time perception of key operational parameters at the device layer, anomaly detection and control at the edge layer, and data management and efficient computing at the cloud layer, thereby realizing real-time acquisition, anomaly detection, alarm control, and remote monitoring of operational status parameters for residual film recycling machine. Firstly, the overall design of the monitoring system was presented, including hardware and software selection, deployment, and core program development. It then elaborated on the processing methods for key data, particularly the mechanism for extracting effective operational data based on working condition information and the algorithm for calculating operational area. Field experiments showed that the monitoring relative errors for machine speed, beating shaft rotation speed, pickup rotation speed and baling rotation speed, were 0. 84%, 0.54%, 3.46%, and 1. 27%, respectively. The effectiveness of door status and pickup depth alarms reached 100% and 98%, respectively, and remote monitoring effectiveness reached 100%. Regarding operational area calculation, the real-time incremental area calculation method based on valid operational trajectories achieved an average accuracy of 92. 57%, while the closed area calculation method based on trajectory point contour reached an accuracy of 97.09%. This system enabled real-time perception of key operational parameters, intelligent decision-making at the edge, and collaborative management in the cloud. It not only enhanced operational reliability and avoided frequent manual inspections but also provided effective data support for the optimized design and intelligent operational decision-making of residual film recycling machine.

Abstract:Aiming to solve the problems of lack of detection and sorting equipment for component-type germplasm and low detection efficiency, a seed detection and sorting device with double composite vibration seed metering was designed. The seeds were flattened through double composite vibration, and the sorting component selected high-quality seeds according to the detection results. The device consisted of key components such as the seed metering component, detection component, and sorting component. Structural design and theoretical analysis were carried out on the key components to design better components. Taking corn seeds as the test object, the voltage and vibration frequency ranges of the feeding tray and the leveling tray were determined through single-factor tests. With the qualified rate of seed spacing, seed fitting rate, and seed residual rate as the test indicators, a four-factor and three-level test was carried out, and a response surface mathematical regression model was established to analyze the influence of each factor and their interactions on the operation of the device. The results showed that when the voltage of the feeding tray was 60 V and the vibration frequency was 46. 0 Hz, the voltage of the leveling tray was 55 V and the vibration frequency was 51. 6 Hz, the seed metering effect of the device was optimal. The test verification with the optimal parameters showed that the qualified rate of seed spacing was 90. 5%, the seed fitting rate was 2. 9%, and the seed residual rate was 2. 3%. The sorting speed of the device can reach 1 000 seeds/min, which met the requirements for the selection and storage of germplasm resources.

Abstract:Aiming to meet the diverse seeding rate requirements of different rice varieties and improve the adaptability of rice seed metering devices to rice seeds of diferent grain types, based on the seeding agronomy of multiple rice varieties, taking four types of rice seeds with significantly different grain types as test objects, based on their basic physical properties, a seed metering device with adjustable opening of ladle-shaped holes was designed. The device adopted a composite structure of hole-type and fluted roller-type. It drived the transverse displacement of the adjusting wheel of the seed metering wheel through a gear-linked spring mechanism, realizing stepless adjustment of the hole opening (0 ~ 14 mm). Based on the three-axis dimensions of rice seeds, the optimized key parameters of the holes were as follows: hole length was 13 mm, width was 6 mm, depth was 4 mm, and a seed quantity adjusting plate ( with a height of 58 mm) integrated with a flexible seed cleaning brush and a guiding tooth structure. A three-factor test was conducted on the seed metering device, and the test results showed that as the hole opening increased (from 0 mm to 14 mm), the average seeding rate per hill of indica rice was increased from 1. 55 ~3.30 grains to 15.95 ~ 23. 09 grains, and that of japonica rice was increased from 1. 89 ~ 3. 98 grains to 16.54 ~ 21.69 grains. When the rotation speed of the seed metering wheel was in the range of 40 ~ 140 r/min, the coefficient of variation of the seeding rate was decreased from 67. 42% to 9. 27% as the opening increased. The seed metering device realized stepless adjustment of 3 ~ 20 grains with a coefficient of variation of 10% ~ 70%, meeting the precision seeding requirements of different rice varieties.

Abstract:To ensure the consistency of planting depth when facing irregular ridge shape of sloping farmland in hilly areas, domestic and foreign scholars have carried out a lot of research on profiling technology of transplanter components. Aiming at the problem that the passive profiling system is difficult to realize real-time adjustment of planting depth, an electric planting mechanism which can adaptively adjust the planting depth according to the ridge height was designed in this paper. For the mechanism, kinematic modeling of the mechanism was conducted, and the working principle of the planting system was analyzed. Using the results of the kinematic modeling, a computer-aided analysis and optimization interface for the planting mechanism was developed to study the mapping relationship between structural parameters and the planting trajectory. By establishing an objective optimization function and setting mechanism constraints, a relatively optimal parameter combination was obtained by using the successive approximation method. A simulation test for constant-depth planting was conducted, verifying that the proposed mechanism can adjust the planting depth through five sets of angle variations, while meeting the requirements for the planting trajectory and the velocity at the planting endpoint. A static trajectory test of the mechanism was carried out, showing that the maximum error of the lowest point between the five sets of actual static trajectories and the simulated static trajectories was 6.86 mm, and the maximum error in trajectory height was 13.98 mm, with a high degree of coincidence for each set of trajectories. The constant-depth planting performance test indicated that, at a planting frequency of 15 plants/min and a unit speed of 3.6 m/min, the mechanism can realize stable constant-depth planting operation on both flat and uneven ridge surfaces.

Abstract:In view of the limitations of domestic sweet potato transplanting mechanism on realizing horizontal transplanting trajectory, a sweet potato transplanting mechanism design method was proposed based on non-circular gears. Firstly, the requirements of horizontal transplantation agronomics were analyzed, the ideal trajectory, including seedling extraction, soiling, seedling throwing and other stages was planned, and nine key pose points were selected as the input parameters for solving the 2R chain opening mechanism. The non-circular gear transplanting mechanism was simplified into a plane 2R chain opening mechanism, an approximate multi-pose motion comprehensive equation was established based on the parameters of the pose points and center points, the mechanism parameters were solved through the homoethic algorithm, and rod length constraints and trajectory optimization were introduced to obtain the optimal solution (rod 1 was 100 mm, rod 2 was 240 mm). Matlab assisted optimization software was developed to complete the non-circular gear joint curve design and two-stage transmission ratio allocation, structural design was carried out by using these parameters, and virtual prototype simulation analysis was performed to verify the correctness of the sweet potato transplanting mechanism design. The physical prototype of the sweet potato transplanting mechanism was designed and field experiments were carried out. The results showed that the planting posture of the sweet potato seedlings was in line with the trajectory plan. When the planting rate was 45 plants/min, 55 plants/min and 65 plants/min, the passing rate of the planting depth, plant spacing, and under-soil horizontal length reached more than 85%. Among them, the passing rate of each item exceeded 90% at 45 plants/min, indicating the feasibility and efficiency of the mechanism, and providing a practical solution for mechanized transplanting of sweet potatoes.

Abstract:A soft pipeline robot specifically designed for agricultural irrigation inspection was described. The robot was capable of fast and low-resistance locomotion, achieved through a single-drive mechanism. The robot's peristaltic motion was produced by a linear extension actuator and six pairs of flexible feet, controlled through a single air channel. This integrated approach reduced control complexity and increases mobility. An investigation of the peristaltic mechanism in a single-actuated pipe robot was conducted, along with structural optimization, deformation modeling, and performance testing of the flexible foot and body actuators. A mechanical model was established in order to characterize the driving force and step displacement during the robot's propulsion phase, with its predictions subsequently verified by static experiments conducted inside a pipeline. Gait-based velocity modeling was conducted to examine the robot's motion characteristics. A 3D motion capture system was employed for precise motion tracking, thereby revealing the influence of air pressure and ventilation frequencey on overall locomotion performance. Experiments were performed in a simulated agricultural irrigation pipeline system to evaluate load capacity, adaptability to different pipe diameters, and mobility under varied pipeline conditions. Additionally, a front-mounted visual sensing unit was integrated to enable internal pipeline inspection. The robot achieved a maximum speed of 40 mm/s and carried a payload of 1 kg, enabling rapid exploration within complex agricultural irrigation pipeline systems.

Abstract:Accurate simulation of the stalk-tool interaction during mechanized rapeseed harvesting is limited by the lack of accurate and reliable bonding parameters for discrete element stalk models. To address this issue, rapeseed stalks of the cultivar "Huayouza 62" at the optimal harvest stage were used. The intrinsic and contact parameters of the stalks were systematically determined through physical tests. A double-layer bonded model of the rapeseed stalk was developed in EDEM by using the Hertz - Mindlin with Bonding V2 contact model. Using the measured maximum bending failure force 43.02 N and maximum shear force 186.22 N as the dual response values, a Plackett - Burman design was firstly employed to identify significant factors, followed by a steepest ascent experiment to approach the optimal range, finally, a Box - Behnken design was used to optimize the solution. The results showed that the skin-skin normal stiffness per unit area X?, skin-core shear stiffness per unit area X?, and core-core normal stiffness per unit area X? had significant effects on the model. In the optimal parameter combination, X? was 6.80×10?N/m3, X? was 5.93×10?N/m3 and X? was 3.18×10?N/m3. Based on this combination, a double-layer bonded discrete element model of the rapeseed stalk was established. Simulations demonstrated that the relative errors between the simulated maximum bending failure force and maximum shear force and the measured averages were 2.11% and 2.39%, respectively. The mechanical behavior of the model was consistent with that of actual stalks. The research result can provide a reference for the discrete element modeling of rapeseed plants and the design of harvesting machinery.

Abstract:In view of the phenomenon that the materials distribute unevenly on the sieve surface during the operation of 4LZ - 3.0Z rice combine harvester, with more materials on the sides and less in the middle, and more at the front and less at the back, which leads to insufficient screening of the materials during the cleaning process, a leaf-interlaced centrifugal cleaning fan was designed to generate a lateral airflow through the pressure difference between the front and rear vanes, improving the uniformity of the lateral distribution of the materials. A mathematical blade angle and wind speed model was established based on the pressure difference between the front and rear vanes during the fan's operation. By combining the theoretical pressure difference and the expression of the lateral wind, it was concluded that there was a nonlinear function relationship between the staggered blade angle and the lateral wind. Through numerical simulation of the fan flow field and the actual measurement of the wind speed on the sieve surface of the cleaning chamber, the rationality of the theoretical analysis was verified and the size of the lateral wind was obtained. Through single-factor experiments, it was determined that the deflector plate angle, fan speed and blade stagger angle were the influencing factors, and the grain impurity rate and cleaning loss rate were used as evaluation indicators to obtain the influence relationship of each factor. A three-factor three-level response surface experiment was designed. The optimal parameters were obtained through the response surface method and multi-objective optimization: the fan speed was 1 105. 614 r/min, the deflector plate angle was 39. 91°, and the blade stagger angle was 25. 073°. Under the optimal parameters, the grain impurity rate was 1. 08% and the cleaning loss rate was 0. 774%, which was superior to the performance of the traditional cleaning device.

Abstract:Addressing the issue of high water content in field corn harvested in the southern region of China, which complicates the peeling process, a peeling device was designed based on an analysis of the peeling process and its underlying principles. The device utilized directional friction to peel the cob after the bracts were scratched. The mechanical and kinematic analysis of the cob peeling process was conducted according to the physical characteristics of the corn cob. Additionally, a scratching device was designed to engage the surface of the cob bracts, facilitating the entry of the cob into the peeling device in a vertical orientation. The structural parameters of the scratching device were optimized through simulation testing by using EDEM software. High-speed camera technology was employed to study the peeling process and verified that the peeling roller effectively captured the bracts at the scratch points. The primary factors influencing peeling performance, identified as the speed of the peeling roller, the speed of the pressure feeder, and the distance between the pressure feeder and the peeling roller, were determined. The Box Behnken experimental design was employed to conduct a three-factor, three-level orthogonal test, with peeling roller speed, pressure feeder speed, and the distance between the peeling roller and pressure feeder as the test factors, and the rates of bract peeling and grain shedding as the response variables. The results demonstrated that at a peeling roller speed of 353.2 r/min, a pressing wheel speed of 81.42 r/min, and a distance of 37.16 mm between the pressing wheel and the peeling roller, the bract peeling rate of 95.67%, with a grain shedding rate of 1.45%. Verification tests under these conditions yielded a bract peeling rate of 93.33% and a grain shedding rate of 1.56%, values that were in close agreement with the optimized parameters and met the requirements for efficient corn peeling. These findings can provide a useful reference for improving the peeling process for high-watercontent corn in the southern region.

Abstract:Aiming to achieve the harvesting of various types of fruits and vegetables, a multi-segment independently controlled flexible picking gripper driven by electrorheological fluid (ERF) was designed based on a human-finger-inspired structure. The gripper adopted a modular and integrated design, and the flexible fingers were directly driven by micro-pumps. The finger comprised three multi-chamber flexible joints, each containing an embedded ERF microvalve. The electrorheological fluid served as both the actuation and working fluid. By leveraging its tunable rheological properties through the coordinated operation of the microvalve array, independent bending deformation of each joint was achieved. A comprehensive study was conducted on the control principles of the multi-segment finger. This involved developing theoretical models for the current-controlled microvalves, the deformation of the flexible joints, and the finger's overall kinematics, all of which were validated experimentally. The models quantified critical performance metrics, such as the microvalve threshold pressure, the normal output force and deformation as functions of air pressure, and the finger's kinematic workspace. Furthermore, a two-finger picking gripper prototype was fabricated, and an experimental platform was constructed to conduct grasping and picking tests on various fruits and vegetables. Results demonstrated that the finger exhibited smooth, flexible, and diverse movements. The integrated mierovalve system enabled a single input to produce human finger-like multi-modal deformation, the gripper offered a larger envelope workspace and superior shape adaptability. A maximum joint bending angle of 121° and a normal output force of 2.3 N were achieved. The two-finger picking gripper featured multiple grasping modes, including enveloping, pinching, and clamping, exhibiting excellent adaptability to different object shapes, which was suitable for harvesting small and lightweight fruits and vegetables.

Abstract:Aiming to address the airflow regulation challenges during visual detection and foreign fiber (e. g., film) removal in seed cotton, finite element software was used to simulate and calculate relevant parameters, analyze their impact on flow field characteristics, in order to optimize the flow field structure and improve impurity removal efficiency and conveying stability. A three-dimensional multiphysics geometric model was developed, incorporating key components such as the seed cotton inlet, impurity removal outlet, cotton outlet, nozzles, and auxiliary air inlets. Based on the SST k-ω turbulence model, the systematic simulation analysis was conducted on the angle and velocity of the auxiliary air inlets. The simulation results revealed that when the seed cotton inlet velocity was set as 9 m/s, the impurity removal air inlet velocity as 1 m/s, the cotton outlet auxiliary air inlet velocity as 6 m/s, and the auxiliary airflow angle as 15°, the airflow velocity distribution at both the impurity removal and cotton outlets was optimized, effectively meeting the operational requirements for foreign fiber separation and cotton discharge, while minimizing flow blockage and recirculation within the conveying pipeline. Prototype testing under the optimized simulation parameters confirmed that the integrated system, combined with machine vision-based recognition, achieved a foreign fiber removal rate of 90% and a processing capacity of 4 tons of seed cotton per hour.

Abstract:In response to the problem of complex structure, low collection efficiency and stability, as well as the tendency for negative pressure collection to cause adhesion to the wall and poor performance when rainwater adheres to the leaves in the end effector for high-quality tea picking, an end effector for integrated harvesting of high-quality tea was designed, featuring a simple overall structure, high efficiency, good stability, and excellent picking quality. Continuous picking and collection of tea leaves' tender shoots were achieved through diffuse reflection sensors for sensing, flexible synchronous belt clamping and combined with split type cutting tools. By analyzing the picking and collection process of the end effector, it was found that the factors affecting the success rate of transportation and the success rate of picking were the distance between the two synchronous belts and the lag time. Adopting the central composite design and response surface analysis method, and using the Design-Expert 13.0 software for experimental design and analysis, the influence of the distance between two synchronous belts and the lag time on the success rate of transportation and the success rate of picking was studied. And with the optimization objectives of maximizing the success rate of transportation and the success rate of picking, the optimal parameter combination was obtained: the distance between the two synchronous belts was 1.79 mm, and the lag time was 0.80 s. After conducting validation picking experiments on optimized parameters in tea gardens, the results showed that the end effector could efficiently and effectively complete the picking and collection work with high quality. The success rate of transportation and the success rate of picking were 96.7% and 86.3%, respectively. The relative error between the experimental and predicted values was less than 5%, and the optimized model results were reliable.

Abstract:Comprehensive consolidation and ecological restoration have a positive promoting effect on ensuring regional ecological quality, optimizing ecological security patterns, and enhancing the value of ecosystem services. Zoning of comprehensive consolidation and ecological restoration of land space is an indispensable and important part of the arrangement of projects. Taking Zhaoping County, Hezhou City, Guangxi Zhuang Autonomous Region as an example, a zoning method of comprehensive consolidation and ecological restoration of land space was proposed based on Potential-Motivation-Pressure framework for county-level. The main conclusions were as follows: the potential of comprehensive consolidation was mainly analyzed from the aspects of new arable land potential, arable land quality improving potential, land development potential, land reclamation potential, rural construction land potential and urban construction land potential. While the potential of ecological restoration was mainly analyzed from the aspects of vegetation coverage potential, water conservation potential, windbreak and sand fixation potential and biodiversity potential. In the motivation index, the overall level of normalized difference vegetation index was relatively high, the overall level of forest coverage was relatively high, and the grassland coverage rate was significantly higher in the north than that in the south. The distribution pattern of water area ratio was higher in the west and lower in the east, and the spatial distribution of construction land area ratio was uneven. In the pressure index, the per capita arable land area was higher in the south than that in the north, the per capita GDP was higher in the east and lower in the west, and the per capita net income of rural residents was higher in the northwest and lower in the southeast. Zoning of comprehensive consolidation and ecological restoration of land space showed that there was one town in Zhaoping County that belonged to the priority rectification and restoration area, four towns that belonged to the key rectification and restoration area and seven townships that belonged to the moderate rectification and restoration area. The research results had important guiding significance for the arrangement and engineering layout of comprehensive consolidation and ecological restoration of land space.

Abstract:The rapid acquisition of information on winter wheat planting areas and their changes using remote sensing technology is of great significance for ensuring national food security and promoting sustainable regional agricultural development. Temporal feature curves of winter wheat, winter rapeseed, and other land cover types were established by using Sentinel-1 SAR and Sentinel-2 optical images during the phenological period of winter wheat on the Google Earth Engine (GEE) platform. By qualitatively and quantitatively analyzing the differences among the temporal feature curves of various land cover types, the optimal features for winter wheat extraction were identified. Based on these optimal features, a hierarchical classification approach for winter wheat was developed. This method firstly distinguished winter crops from other land cover types using vegetation index time series, and then applied the OTSU algorithm to separate winter wheat from winter rapeseed, thereby achieving high-precision extraction of winter wheat. The results showed that among the three hierarchical classification algorithms, the "NDPI_TWDTW & NDSVI_OTSU" algorithm achieved the highest extraction accuracy for winter wheat (OA of 0.927, Kappa of 0.854), which was comparable to that of the "RF_Full Feature" algorithm (OA of 0.931, Kappa of 0.863). The "decision-level fusion" of the three algorithms had a limited effect on improving accuracy. Due to speckle noise, SAR images exhibited lower extraction accuracy for winter wheat in the VV (OA of 0.879, Kappa of 0.757) and VH (OA of 0.898, Kappa of 0.796) bands compared with the NDSVI optical vegetation index (OA of 0.927, Kappa of 0.854). Sentinel-1 SAR and Sentinel-2 optical data complemented each other in spatiotemporal resolution and characteristic information. Under cloudy and rainy conditions, SAR data effectively supplemented optical remote sensing data for winter wheat extraction, enhancing the timeliness and stability of regional winter wheat monitoring. The proposed hierarchical classification approach demonstrated mechanistic interpretability in feature analysis, low complexity, high accuracy, and strong robustness, and can be effectively applied to the identification and change monitoring of winter wheat over large areas and long time series.

Abstract:The rational application of nitrogen fertilizer during the growth of rice exerts a decisive influence on its development and yield. Traditional unimodal data, such as spectral or image data, struggle to capture the complex physiological states and nitrogen response mechanisms of rice seedlings comprehensively. A multi-modal fusion network (MMFN) was constructed to fulfil the requirement for identifying nitrogen application levels. To this end, a multi-modal dataset was compiled comprising near-infrared spectral data, leaf images and growth indicators of rice seedlings subjected to different nitrogen application concentrations. A recognition model based on MMFN was developed by incorporating an improved channel attention mechanism (improved channel attention mechanism, ICAM) and concatenation mechanism, which enabled the fusion of physical growth metrics with image feature information. The experimental results showed that the fusion model achieved an accuracy of 97.55%, a recall of 95.34%, a precision of 95.87% and an F1 score of 95.72% in identifying the nitrogen application level. The proposed model, through multi-modal data collaborative optimisation, effectively extracted complementary information across different modalities, demonstrating significant superiority over single-modal approaches. The proposed MMFN model fully exploited complementary information across modalities through collaborative multi-modal data optimisation, thereby enhancing the accuracy and robustness of nitrogen application level identification. It can offer reliable technical support for precise rice nitrogen monitoring and fertility regulation.

Abstract:Aiming to solve the problem of low recognition accuracy of jujube fruits and poor ability to distinguish between normal and cracked fruits under various complex environmental conditions, such as different lighting, occlusion, weather conditions, and the presence of distant targets, field jujube images in natural environments were collected. Data augmentation was performed by simulating four weather conditions, and a dataset for model training, validation, and testing was created. Based on the YOLO v8n model, the GSConv and RFCAConv modules were incorporated to build the GR-YOLO model, which was then trained and tested on the jujube dataset. The experimental results showed that the GR YOLO model achieved a precision of 95.14%, a recall of 97.71%, and a mean average precision (mAP) of 97.64%. The model size was 4.9 MB, the inference speed was 289.8 f/s, and the number of parameters was 2.38×10^6. Compared with models with the similar number of parameters, including YOLO v5n, YOLO v6n, YOLO v7 tiny, and YOLO v8n, the GR-YOLO model’s precision was improved by 1.82~2.61 percentages, recall was improved by 3.04~8.48 percentages, and mAP was improved by 2.40~7.88 percentages. Compared with existing mainstream models, GR-YOLO model demonstrated optimal recognition performance and a lower number of parameters. The improved model not only increased the recognition accuracy of jujube targets but also effectively distinguished between normal fruits and cracked fruits, while achieving lightweight design.

Abstract:Aiming to address the issues of excessive parameters, poor real-time performance, and insufficient lightweight design in traditional models for soybean field weed recognition, an improved lightweight weed detection model named YOLO 11n - ADS was proposed based on YOLO 11n. By replacing the original C3K2 module with a switchable atrous convolution (SAConv) module and integrating a global context mechanism with multi-dilation-rate feature fusion, the enhanced model strengthened multi-scale feature extraction capability and improved weed detection accuracy in complex environments. Meanwhile, the backbone network was optimized by using the adaptive downsampling module (ADown), which fused the average pooling and maximum pooling strategies to reduce the feature loss caused by target occlusion and light interference. The YOLO 11n - ADS model achieved detection accuracy, mAP50, mAP50-95, and recall rates of 90.6%, 92.0%, 86.0%, and 92.6%, respectively, representing improvements of 1.1 percentage points, 2.6 percentage points, 5.0 percentage points, and 3.6 percentage points over the baseline YOLO 11n model. Additionally, the parameter count was reduced from 2.6×10^6 to 2.2×10^6, while the floating point operations (FLOPs) was decreased from 6.3×10^9 to 4.6×10^9. Deployed on the Jetson Nano edge computing platform by using the TensorRT framework, the optimized model achieved efficient real-time inference with minimal resource consumption. The memory usage was only 1.6 GB, and the detection speed reached 35 f/s. In field dynamic validation tests, the end-to-end detection average frame rate reached 28 f/s, with detection accuracy, mAP50, mAP50-95, and recall rates of 89.5%, 91.0%, 84.0%, and 91.4%, respectively. Compared with YOLO 11n, the proposed model demonstrated stronger robustness and real-time performance under challenging scenarios such as uneven lighting and target occlusion. The research result can provide an efficient and lightweight solution for real-time weed detection in complex farmland environments, which was suitable for deployment on edge computing devices and contributed to the intelligent management of precision agriculture.

Abstract:Accurate monitoring and timely intervention of dairy cows' prepartum behavior are of great value for safeguarding the health of dairy cows, enhancing reproductive efficiency, and improving breeding productivity. However, prepartum behaviors such as tail warping and pelvic relaxation are characterized by small target sizes and overlapping features; they are also highly susceptible to interference from environmental factors, which often leads to misrecognition. To achieve rapid and accurate identification of dairy cows' prepartum behavior in complex barn environment, a prepartum behavior recognition method was proposed based on improved YOLO v8n. Firstly, an optimized HGNet V2 model was integrated to enhance the model's target recognition performance in complex barn scenarios. Secondly, a lightweight efficient channel attention (ECA) module was introduced after the C2f module in the model's head section. Finally, the MPDIoU Loss function was adopted to replace the CIoU loss function. Experimental results showed that compared with the baseline YOLO v8n model, the improved model achieved a mean average precision (mAP@ 0.5) of 92.9% for the recognition of the four prepartum behaviors-representing an increase of 1.6 percentage points. Specifically, the average precision (AP) for tail warping and pelvic relaxation behaviors was increased by 5.6 and 1.7 percentage points, respectively. When compared with other mainstream detection models, the improved model's mAP@ 0.5 was enhanced by 9.6, 10.5, 10.9, 6.5, 3.0, 2.8, 1.0, 1.0 and 1.1 percentage points, respectively. In conclusion, the model constructed exhibited strong robustness, enabling accurate identification of dairy cows' prepartum behaviors under dynamic and complex breeding environments as well as all-weather conditions.

Abstract:Aiming to address the challenges of slow convergence, low search efficiency, and limited global search capability in traditional ant colony optimization (ACO) algorithms for agricultural robot path planning, a multi-strategy fusion improved ant colony algorithm (MSFIACO) was proposed. The MSFIACO integrated a multi-layer vision expansion search strategy and introduced a non-uniform pheromone initialization with an angular judgment mechanism. This mechanism assigned higher initial pheromone concentrations to paths closer to the straight line connecting the start and end points. A reward-punishment constraint was applied along the ants' movement direction to enhance search efficiency and precision. Additionally, an adaptive pseudo-random state transition strategy was introduced to balance global exploration and convergence speed, while a smoothing factor was used to reduce turning frequency. An adaptive heuristic information strategy was also proposed to ensure efficient convergence in the early stages and effective global search in the later stages. The pheromone update rule was refined to distinguish between superior and inferior solutions, focusing the search on the neighborhoods of promising paths. Experiments showed that MSFIACO significantly reduced path length, decreased turning points, accelerated convergence, reduced the number of path nodes, and exhibited strong robustness and adaptability, enhancing the path planning capability of agricultural robots in complex environments.

Abstract:Aiming to meet the operational requirements of agricultural plant protection UAVs and ensure their flight efficiency and safety, a hybrid improved sand cat swarm optimization (HISCSO) algorithm was proposed for UAV path planning. Firstly, a nonlinear control factor was designed to dynamically balance the transition between algorithmic phases. Secondly, the golden sine strategy was introduced during the attack phase to enhance the algorithm's local exploitation capability and accelerate convergence. Finally, by leveraging the strengths of a smooth exploration strategy, population diversity was maintained, and the algorithm's global optimization ability was improved. The performance of the algorithm was validated by using the CEC2022 benchmark suite. Experimental results showed that compared with the original algorithm and six other optimizers, HISCSO achieved the best performance on 75% of the test functions. The study formulated a cost function that satisfied multiple operational constraints and constructed UAV mission environments in hilly areas based on a digital elevation model map. Across four environments of varying complexity, HISCSO consistently located the globally optimal route, producing the smoothest and shortest path. Compared with the original algorithm, HISCSO improved stability by 10.21%, 36.59%, 29.27% and 46.46% across the four representative agricultural scenarios, demonstrating that it simultaneously possessed global search capacity and local smoothness preservation, and thus offered a highly reliable path planning solution for low altitude plant protection UAV operations.

Abstract:Accurate trajectory tracking is fundamental to the autonomous operation of pineapple field management vehicles. To improve navigation accuracy, a control method was proposed. Firstly, a vehicle platform was developed and its kinematic model was established. Secondly, a model predictive control (MPC) algorithm was constructed based on this kinematic model. Then, to achieve high-precision trajectory tracking, a method was proposed that integrated MPC and Adaptive-PID. Both simulation and field experiments were conducted to verify the effectiveness of this method, and a systematic comparison was made among the Adaptive-PID integrated MPC method, the MPC method and the Adaptive-PID method. In the simulation results, the maximum lateral errors of the MPC method and the Adaptive-PID method were 1.38~2.05 times and 4.22~17.09 times higher, respectively, than those of the AdaptivePID integrated MPC method. In the field test results, the maximum lateral errors of the MPC method and the Adaptive-PID method were 1.86~2.30 times and 3.76~6.69 times higher, respectively, than that of the Adaptive-PID integrated MPC method. Both simulation and field experiments demonstrated that, compared with the MPC method and the Adaptive-PID method, the Adaptive-PID integrated MPC method achieved higher trajectory tracking accuracy. The research result can provide an effective technical solution and solid theoretical support for trajectory tracking control of unmanned pineapple field management vehicles, and demonstrated promising application prospects for improving the accuracy and efficiency of agricultural automation equipment.

Abstract:Aiming to address the issues of poor generalization of data-driven models and insufficient representation of spatial heterogeneity in current mushroom house model predictive control, a data mechanism collaborative modeling and global temperature field collaborative control method for heterogeneous mushroom houses was proposed, with the goal of energy saving and consumption reduction in industrial mushroom room production. Firstly, an environmental sensor placement optimization model was developed to capture complex thermal dynamics by using only a minimal number of sensors. Secondly, a simplified physics-informed neural networks (PINNs) architecture based on fully connected layers was designed, incorporating simplified Navier - Stokes and energy conservation equations as physical constraints to enhance temperature field prediction accuracy. Finally, an MPC framework was formulated with a joint optimization objective of temperature tracking error and energy consumption, and systematically validated in two representative production settings: a prefabricated modular Pleurotus citrinopileatus cultivation chamber in Fangshan District and a standardized Hypsizygus marmoreus factory facility in Tongzhou District of Beijing. Results showed that under sparse monitoring with only six sensors, the proposed method achieved a root mean square error (RMSE) of 0.267℃ in reconstructing the full three-dimensional temperature field in the modular chamber. The PINNs - MPC strategy enabled precise whole-space temperature control in the standardized facility, maintaining temperatures stably within 14.2℃ to 15.2℃, whereas in the modular chamber, localized temperature violations occurred due to strong environmental disturbances and non-standardized agronomic operations. In terms of energy efficiency, PINNs - MPC reduced energy consumption by 10.7% compared with conventional threshold-based control; however, its conservative control behavior limited further savings, as a thermally constrained MPC approach based on an energy-balance equation achieved a higher reduction of 13.1%. By effectively integrating physical laws with sparse observational data, this work significantly enhanced modeling fidelity and spatial awareness of thermal fields in heterogeneous agricultural environments, offering a theoretically grounded and technically viable pathway toward intelligent, energy-efficient, and robust environmental control in modern mushroom cultivation.

Abstract:The refined flour and whole grain flour of the same variety of wheat were used as raw materials to produce sourdough steamed bread by backslopping fermentation. The effects of white flour and whole grain flour on the microbiota of backslopping fermented sourdough and the physicochemical properties of steamed bread were investigated by comparing the differences between the sourdough microbiome and the properties of steamed bread. The results showed that the differences in fungal communities between mature refined flour and whole wheat sourdough were mainly reflected in the differences in fungal community structure. Blumeria graminis was the dominant fungus in refined flour sourdough, while Saccharomyces sp. was the dominant fungus in whole wheat sourdough. The physicochemical properties and sensory quality of steamed bread made from backslopping fermentation with whole wheat and refined flour were compared comprehensively. The results of principal component analysis showed that the preparation method was the main factor affecting the physicochemical properties of steamed bread, while the flour type (refined flour and whole wheat flour) was the secondary factor. Finally, Person correlation analysis proved that six key microorganisms had significant and specific effects on multiple physicochemical properties of refined flour and whole wheat steamed bread. In conclusion, the results proved that backslopping fermentation could effectively improve and enhance the comprehensive physicochemical properties of wheat steamed bread, and whole wheat flour could further improve the nutrition, function and sensory quality of wheat steamed bread compared with refined flour. These results provided theoretical insights and experimental basis for the development and application of whole wheat steamed bread.

Abstract:Tea processing is an important factor affecting both the physicochemical composition and flavor quality of tea. The composition and content of amino acids in large-leaf yellow tea (LYT) during the manufacturing processes were determined by automatic amino acid analyzer, the dynamic changes of the measured amino acids during LYT processing were analyzed, and the principal component analysis (PCA) was applied for discrimination of the differences among various stages of tea processing. Results showed that a total of 22 amino acids were detected in tea samples, including 19 protein amino acids and three non-protein amino acids(theanine, β-alanine and γ-aminobutyric acid (GABA)). The compositions and contents of amino acids were quite different during LYT processes, and 22 amino acids could be detected in fresh tea leaves (FTL), fixing (Fx-LYT) and rolling (Ro-LYT) samples, while only 12 amino acids were identified in the finished tea (FF-LYT) that underwent high-temperature roasting. And the concentrations of these 12 amino acids in FF-LYT were significantly lower than those of FTL or re-yellowing (RY-LYT) samples. In the previous samples of FF-LYT with high-temperature roasting processing, theanine, aspartic acid, glutamic acid and glutamine were the most abundant amino acids, and the concentrations of the latter three were about 2.0 mg/g in these tea samples, whereas theanine concentrations were more than 9.40 mg/g in these samples, with the highest concentration was 13.43 mg/g observed in Ro-LYT. The contents of individual amino acid varied greatly during LYT processing, among which the variation coefficient (CV) of tryptophan was the largest (227.48%) and that of cysteine was the smallest (17.26%). In addition, to further distinguish the differences of LYT processes, the PCA analysis was performed on the detected amino acids. The tea samples, FTL, FF-LYT, Fx-LYT and re-roasting (RR-LYT) could be separated according to the processes, in particular, the distance between FTL and FF-LYT was far, indicating that heat treatment, especially for high-temperature roasting treatment affected the compositions and contents of amino acids in LYT. And the amino acids involved in the thermal reaction as aroma precursors or reaction substrates to produce volatile components, which was the probably reason for the reduction of their contents. The findings of this research can provide essential data support for the research on the formation of tea flavor quality in LYT, and offer a scientific theoretical basis for the processing, quality regulation and quality control of tea.

Abstract:Traditional agricultural chassis have poor mobility and adaptability to hilly orchard terrain. To address this, a tracked bilateral coordinated swing-arm power chassis was developed, enabling adaptive track lifting and swinging via a spatial linkage mechanism, with hydraulic power supplied to front-mounted implements. Based on the operational characteristics of hilly orchards, such as dispersed plots and narrow inter-row passages, the overall configuration and dimensions were systematically designed. A bilateral coordinated swing-arm mechanism was proposed to enhance adaptability and driving stability on uneven terrain, and key components were optimized and matched to ensure coordinated operation. The chassis performance was evaluated through multi-body dynamic simulations and field experiments under typical hilly orchard conditions. Simulation results verified the strong adaptability of the proposed mechanism to terrain undulations, while experiments demonstrated that the driving stability on rough terrain was improved by 61.29% compared with that of a conventional crawler chassis. The developed chassis achieved a straight-line deviation rate of 4.03% and a minimum turning radius of 725.8 mm. It was capable of stable uphill, downhill, and lateral movement on slopes up to 16° without overturning or slipping and exhibited smooth obstacle-crossing performance. The implement mounting system provided a lifting range of -5°~32° and a lateral adjustment of 100 mm. During continuous weeding operations, sufficient power output and a low hydraulic oil temperature rise indicated reliable thermal behavior and suitability for long-term orchard applications.