Abstract:Tropical crops constitute a significant component of global agricultural production and trade. However, their wide distribution, complex operating environments, and low level of harvesting mechanization have become critical constraints on agricultural development in tropical regions. Focusing on major tropical economic crops, including rubber, cassava, sweet potato, and coffee, as well as tropical fruits such as coconut, their distribution characteristics and industrial scale were systematically reviewed and comparatively analyzed. It further summarized the harvesting patterns, structural features of harvesting equipment, and recent advances in key operational technologies for different crops. Given that tropical crops were typically cultivated under high-temperature and high-humidity conditions, in heavy and cohesive soils, predominantly across hilly and mountainous terrains, and often exhibited dispersed maturity periods, mechanized harvesting in tropical regions faced multiple challenges. These included insufficient fundamental theoretical research, a shortage of small-scale machinery suitable for hilly and mountainous areas, restricted operational conditions for mechanized applications, and incomplete industrial support systems. Accordingly, future development should prioritize strengthening research on harvesting mechanisms, promoting the integration of agricultural machinery with agronomic practices alongside scale-oriented cultivation, advancing lightweight and precision-oriented harvesting equipment, and improving the industrial chain and organizational systems.

Abstract:In order to solve the problem that the finger-clamp sweet potato transplanter fails to successfully pick up the potato seedlings and plant them into the soil due to poor posture of potato seedlings and insufficient feeding amount, based on the 2ZQX - 2/1 transplanter, a missed planting detection system with infrared photoelectric sensor and an automatic replanting device were designed. The missed planting detection system and replanting device were independent working modules on the transplanter, and the missed planting detection system adopted a delayed discrimination mechanism constructed by a through-beam infrared photoelectric sensor, which detected the triggering of the sensor between the planting clip and the sweet potato seedlings within a limited time window, and determined whether the planting clip successfully picked up the sweet potato seedlings and implanted them into the soil in real time. The planting mechanism of the replanting device adopted a stepper motor to drive the replanting clamp, and combined the electric push rod and guide rail to realize the left and right row replanting operations. The seedling feeding mechanism of the replanting device adopted a lead serew guide rail controlled by a stepper motor to accurately push the potato seedlings for the replanting device. A control system with PLC as the core was constructed to realize the automatic collaborative work between missed planting judgment and replanting operation. The bench test showed that the sum of missed detection and false detection rate of the missed planting detection system was less than or equal to 5% in the range of transplanter speed of 0. 2 ~ 0.4 m/s. The field test results showed that when the working speed of the transplanter was 0.2 m/s, 0.3 m/s and 0.4 m/s, the success rate of missing planting detection system was more than 97%, and the average missed planting rate of the whole machine was 0.68%, 0.85% and 1.37%, respectively, compared with that when the replanting device was not activated, it was decreased by 8. 11 to 9. 55 percentage points.

Abstract:Aiming at the problems that the seedling tray lifting operation during the hardening stage of rice seedling raising mainly relies on manual work, which leads to high labor intensity and low production efficiency, a rotary poking-tooth type seedling tray lifting device for factory-style rice seedling raising was designed. The device mainly consisted of a rotary poking-tooth type seedling tray lifting mechanism, a lifting and conveying mechanism, and a traveling mechanism. During the forward movement of the machine, the rotary poking-tooth type seedling tray lifting mechanism continuously poked up the seedling trays, which moved backward along the poking-tooth roller and were then transported to the rear by the lifting and conveying mechanism. Through dynamic analysis of the seedling tray lifting operation process, parameters such as the number of poking-teeth, the length of poking-teeth, and the inclination angle of the lifting and conveying mechanism were determined, and the key factors affecting the seedling tray lifting effect and the range of main parameters were clarified. Taking the poking-tooth rotary speed, poking-tooth included angle and speed ratio of the device as test factors, and the seedling tray lifting success rate and seedling tray damage rate as evaluation indicators, the Box - Behnken experimental design method was adopted, and three-factor and three-level orthogonal experiments were carried out by using the soil trough test bench. Analysis of variance (ANOVA) and response surface analysis were performed on the test results by Design-Expert software to reveal the influence rules of test factors and their interactions on the operating performance of the seedling tray lifting device, and multi-objective solution and optimization were carried out on the constructed linear regression model. The results showed that the optimal seedling tray lifting effect was achieved when the poking-tooth rotary speed was 45. 12 r/min, the poking-tooth included angle was 88. 28°, and the speed ratio was 1. 49. Verification tests were carried out with the optimized parameters. The results indicated that the average seedling tray lifting success rate was 93. 33% and the average seedling tray damage rate was 1.67%, which met the requirements of seedling tray lifting operation during the hardening stage of rice seedling raising.

Abstract:Aiming at the problems of low efficieney and high damage rate of manual seedling placement in the seedling feeding link of the existing finger-clamped sweet potato transplanter, a split-type seedling clamping device that can automatically open and close was designed based on the 2ZQX -1 clamp-type bare-root sweet potato transplanter. On the basis of expounding the overall structure and working principle, the rubber clip parameters were optimized through theoretical analysis to reduce seedling damage. The motion equation of the rubber elip was established via kinematic analysis, and the mechanism parameters were determined along with the optimal contour line of the are-shaped plate. The single-factor test results indicated that the conveying speed range was 30 ~ 70 mm/s. Taking seedling clip displacement, rubber clip inclination angle and conveying speed as influencing factors, and clamping qualification rate as the evaluation index, bench tests were conducted by using the Box - Behnken central composite design method. Variance analysis was performed on the test results, and response surface analysis was applied to explore the influence rules of interactive factors on the test index, thereby determining the optimal values of the factors. The results showed that when the seedling clip displacement was 12.6 mm, the inclination angle was 16. 9°, and the conveying speed was 49.5 mm/s, the clamping qualification rate reached 95.3%, achieving the optimal seedling feeding effect. Subsequently, full factor field experiments were carried out with different seedling feeding modes ( split-type/integratedtype) as the categorical factor and conveying speed as the numerical factor. The results demonstrated that the split-type seedling clamping device could adapt to a conveying speed of 44.0 mm/s, with the clamping qualification rate remaining at 97.0% and the damage rate at 3.9%, increasing the overall operating efficiency by 31% compared with the integrated seedling clip. This device featured high operating speed and stability, which can provide a technical solution for the efficient operation of mechanized sweet potato transplanting.

Abstract:Aiming to address issues such as crop damage, insufficient flexibility, and poor trafficability of existing chassis for crop breeding phenotypic robots, an amphibious six-wheel differential steering chassis was designed via Solidworks. The design incorporated the agronomic characteristics of major field crops in China's tropical regions and features a stepless, precisely adjustable wheel track ranging from 1 800 mm to 2 000 mm. Based on theoretical calculations, key components were selected, and the steering, stability, and trafficability of the chassis were analyzed. Specifically, a ball screw mechanism was employed for wheel track adjustment, while two interchangeable travel mechanisms-rigid impellers for paddy fields and pneumatic rubber tires for dry land-were configured to adapt to amphibious environments. Finite element analysis (ANSYS) validated the frame's structural integrity, identifying a first-order natural frequency of 59.58 Hz to effectively prevent ground resonance. Field trials confirmed the prototype's robust performance: it achieved a maximum speed of 1. 23 m/s, a minimum turning radius of 1 626 mm, climbing angles exceeding 37°, a vertical obstacle clearance of 435 mm, and a ridge crossing capability of 320 mm. The experimental results corroborated the theoretical analysis, demonstrating excellent chassis performance that satisfied the requirements for field crop phenotyping in tropical regions, thereby providing technical and equipment support for phenotyping monitoring.

Abstract:In order to improve the picking efficieney of the mango-picking robotic arm, trajectory planning was carried out based on the establishment of both positive and negative kinematice models of the robotic arm, with its motion parameters were optimized through the application of intelligent algorithms. The Denavit - Hartenberg (D -H) method was employed to model the mango-picking robotic amm, while the 3 - 5 - 3 hyrid polynomial interpolation method was utilized for effective trajectory planning. Additionally, an improved spider wasp algorithm (ISWO) was proposed to address the specific characteristics of the mango-picking robotic armn. This algorithm primarily focused on initializing the population through Latin hypercube sampling, which enhanced the search stability by incorporating linearly decreasing stochasticity weights. Furthermore, a dynamic switching mechanism was designed to balance global exploration with local exploitation, thereby optimizing both search efficiency and convergence speed. Experimental results indicated that each joint of the mango-picking robotic arm utilizing ISWO significantly outperformed the particle swarm optimization algorithm (PS0) and the standard spider wasp algorithm (SWO) in terms o iteration speed and accuracy. In four actual picking experiments, the total picking time recorded for ISW0 were 62.94 s, 32. 22 s, 39.52 s, and 46. 11 s, demonstrating a reduction of 18.25% to 42.98% in picking time. The research result conclusively showed that the ISWO algorithm significantly enhanced the picking efficiency of the mango-picking robotic arm, providing reliable data support for subsequent related application studies.

Abstract:Aiming to address the shortage of discrete element simulation models and key contact parameters in research on low-damage mechanized mango harvesting, which has limited the accurate interpretation of interaction mechanisms betwveen fruit and mechanical components, two typical cultivars at optimal harvest maturity, Tainong and Egg were selected. The physical parameters of the fruits were measured in detail, and a discrete element model of mango fruit was developed by integrating threedimensional scanning with EDEM simulation analysis, ensuring that the geometric features and material attributes of the model were consistent with the actual samples used in the experiments. To obtain accurate contact parameters for typical engineering materials used in harvesting equipment, free-fall impact, inclined-plane sliding, and rolling tests were conducted separately for the two cultivars under identical test conditions. On EVA material, the measured coeficients for Tainong and Egg were as follows: coefficient of restitution was 0. 499 and 0. 590, static friction coefficient was 0. 478 and 0. 481, and rolling friction coefficient was 0. 037 8 and 0. 022 0, respectively. On rubber, the corresponding values were coefficient of restitution of 0. 388 and 0. 420, static friction coefficient of 0. 723 and 0. 779, and rolling friction coefficient of 0. 037 6 and 0. 017 9, respectively. In addition, fruit-to-fruit collision behavior was analyzed using a double pendulum test, which yielded same-cultivar coefficients of restitution of 0. 312 for Tainong and 0. 294 for Egg, providing essential data for modeling fruit-to-fruit contact in discrete element simulations. To further calibrate the inter-fruit friction parameters, angle-ofrepose experiments were combined with the steepest-ascent method and a central composite rotatable design to establish a quadratic regression model describing the simulated repose angle. Through optimization of this model, the static friction coefficients between fruits of the same cultivar were determined to be 0. 280 for Tainong and 0. 302 for Egg, while the rolling friction coefficients were 0. 025 2 and 0. 019 5. Model accuracy was verified using a lifting-cylinder validation test. The simulated repose angles exhibited relative errors of 3. 84% for Tainong and 4. 35% for Egg, indicating that the calibrated contact parameters were both accurate and reliable. These results demonstrated that the discrete element model can effectively reproduce the mechanical behavior of mangoes during contact and motion. In conclusion, a discrete element simulation model of mango fruit at optimal harvest maturity was established and a complete, experimentally calibrated set of contact parameters was obtained. The model accuracy was verified through multiple independent experiments. The research can provide a dependable simulation foundation and theoretical basis for analyzing contact processes between mango fruits and mechanical components, and it offered valuable reference data for the design and optimization of actuating components, conveying systems, and end-effectors used in low-damage mechanized mango harvesting equipment, thereby supporting the development of efficient and high-quality mango harvesting technologies.

Abstract:Fresh sweet potato is an important economic crop in Hainan Province, and the use of robots for picking operations is an effective means to improve harvesting efficiency. However, in complex field environments, accurate extraction of target contours still suffers from poor robustness and low segmentation accuracy. To address this issue, a cascaded improved detection-segmentation method was proposed: lightweight YOLO 11n was used as the detection backbone, cascaded with boundary-aware salient net (BASNet) to enhance the accuracy and stability of contour recognition in the field. Firstly, the original backbone of YOLO 11n was replaced with lightweight FasterNet, significantly reducing model parameters and improving inference speed. Secondly, a large separable kernel attention (LSKA) module with multi-scale and variable dilation rates was introduced into the backbone network to flexibly expand the receptive field, enhancing the response capability to small targets and targets occluded by vegetation. Then, the squeeze-and-excitation version 2 (SENetV2) channel attention module was inserted before the small and medium target detection heads to further improve target signal extraction capability in complex backgrounds through global feature recalibration. Finally, the detector's bounding boxes were passed to BASNet for pixel-level salient segmentation, removing background noise and refining object contours. On benchmark comparisons, the improved FLS-YOLO 11n achieved a 4.1 percentage points increase in recall and a 1.8 percentage points gain in mAP while substantially reducing model volume and parameter count and improving inference FPS. After cascading with BASNet, segmentation MAE was reduced by roughly 40%, and max Fβ, maxEφ, and max Sm was increase by 3.37%, 3.31% and 4.19%, respectively. Field trials on a harvesting robot produced a 90.6% grasp success rate. Results demonstrated that the proposed pipeline attained high contour recognition accuracy in complex harvesting environments, offering a practical technical path for engineering deployment of sweet potato picking robots.

Abstract:Aiming at the problems of poor soil fragmentation quality and high energy consumption in cassava harvesting, a cumulative impact energy dissipation model of cassava - soil complex was established. By integrating physical experiments with simulation modeling, a high-precision cassava - soil composite model was established. The effects of three vibration modes-steady-state sinusoidal, linear frequency sweep, and logarithmic frequency sweep-on soil particle spatial trajectory displacement (S_r), cassava-soil separation rate (Q_cs),and energy consumption per unit separation rate (E_SR) were compared, revealing the spatial movement characteristics of soil and the underlying energy dissipation mechanisms. Results showed that the relative errors between simulation and physical tests were 0. 98% for cassava compression and 0. 21% for soil cone penetration. Under all three vibration modes, S_r was positively correlated with Q_c, with the strongest correlation observed under steady-state sinusoidal excitation. In linear and logarithmic sweep frequency modes, the sweep range had no significant effect on S_r but significantly influenced both Q_cs and E_SR. Notably, steady-state sinusoidal excitation performed best at 6 Hz and 15 mm, achieving Q_c of 89. 50% while maintaining E_SR at a relatively low level of 0. 32 J/%;for linear sweep frequency excitation, the optimal parameters were a starting frequency of 3 Hz, ending frequency of 9 Hz, and amplitude of 15 mm, yielding Q_c of 76. 75% and E_SR of 0. 37 J/%;logarithmic sweep frequency excitation performed optimally at a starting frequency of 6 Hz, ending frequency of 9 Hz, and amplitude of 12. 5 mm, achieving Q_c of 83.92% and E_SR of 0. 46 J/%. The research refined the interaction theory among cassava, soil and vibration modes, providing theoretical basis and parameter guidance for the development of low-energy cassava harvesting equipment.

Abstract:The application of agricultural machinery navigation system is becoming more and more extensive, but the path tracking accuracy and robustness of the navigation system will be significantly reduced under the working conditions of large terrain, harsh working environment and complex crop types, such as hot areas and hilly areas. Steering system is the core component of agricultural machinery navigation system. Accurate steering control is the premise of achieving high-precision path tracking. Obtaining an accurate mathematical model of the steering system can effectively reduce the design difficulty and complexity of the steering control algorithm. However, due to the unmodeled dynamics such as dynamic lag, nonlinear friction, dead zone effect and environment-related disturbance in the steering system, it was difficult to establish an accurate mathematical model of the steering system by using the mechanism analysis method. Therefore, a steering system modeling method based on Kalman filter (KF) and regularized recursive least squares ( RRLS) joint identification was proposed. The identification platform was built in Matlab environment, and the simulation comparison of RLS, KF + RLS and KF + RRLS identification schemes was realized and compared, respectively. The model with the best performance was selected as the system model identified by KF + RRLS. Subsequently, a field verification test under complex working conditions was carried out. The resulted showed that the trajectory prediction of the wheel angle response of the identified model was 95. 26% consistent with the measured data, and the key dynamic characteristics were reliably reproduced and met the real-time requirements. The model provided a reliable model basis and engineering support for the design of path tracking control algorithm of agricultural machinery navigation system in complex environment.

Abstract:Aiming to improve the stability and obstacle-crossing capability of small tracked transport equipment operating in tropical region hilly areas, a tracked chassis with a rotary center-of-gravity (CG) adjustment mechanism was developed. Based on the operating environment and requirements, a quasistatic slope passability model incorporating yaw angle was established. A rotary center of gravity adjustment mechanism was proposed, and the selection of key parameters and the analytical calculation of the center of mass offset were completed. A parametric numerical study was conducted with respect to the counterweight radial displacement and rotation angle to identify the optimal counterweight configuration. Subsequently, a multibody dynamics prototype was built in RecurDyn, and dynamic simulations of continuous slope climbing as well as oblique step and trench crossing were performed. Functional validation was further carried out on a physical prototype. Results from numerical analysis, multibody dynamics simulations, and prototype tests consistently showed that, compared with the non-adjusted condition, the optimal counterweight adjustment increased the critical rollover slope angle by approximately 6.7%, 13.0%, and 12.5%; at a 10° slope, the maximum step-crossing height was improved by about 23. 6%, 20. 5% and 21. 2% ; and at a 10° slope, the maximum trench -crossing width was enhanced by approximately 17.9%, 16.7%, and 17.5%. The proposed rotary CG adjustment mechanism significantly enhanced chassis stability and obstacle-surmounting performance in hilly terrain, providing design-oriented analytical and experimental evidence for stability improvement of small tracked agricultural platforms in complex orchard and forest environments in tropical regions.

Abstract:It is very important to research the autonomous robotic row-following navigation method of ratoon-emerged sugarcane field robots at the seedling stage to promote the intelligent upgrading of management and protection in the seedling stage of ratoon-emerged sugarcane fields. On the basis of the research on the extraction of row-following navigation lines at the early stage of sugarcane seedlings, a row-following navigation path planning strategy was proposed based on spatio-temporal memory compensation, a light robot system was built, and an autonomous row-following navigation method for perennial sugarcane field robots at the seedling stage was established. At the optimal travel speed of 0. 2 m/s, when the set path fitting interval was 2/3 of the effective field of view, the accuracy of rowfollowing navigation was high, and the root mean square error was 4. 41 cm, indicating that the robot system exhibited robustness in image noise and seedling absence in sugarcane fields. The average frame rate of the robot system was 17.3 f/s, and the average time of logical decision-making and control was only 4. 7 ms, indicating that the vision model, navigation line extraction algorithm and autonomous "capability stack" constructed at the edge can meet the timeliness requirements of robots to quickly and autonomously row following in an unstructured sugarcane field environment.

Abstract:Aiming to address the problem of low recognition and rejection rates caused by poor seed flow uniformity in parent seed selection machines, a technical approach of guiding seed paths and constraint seeds was proposed and a type of seed guiding constraint device was designed. The transport trajectory of the seeds to be inspected was rationally planned and the force and dynamic analysis of the seeds was conducted to ensure the stability of the seed drop point and velocity direction. The structural parameters of the guiding are surface and the wavy chute were designed, and the key parameters and their value ranges that affected the uniformity of seed drop were clarified. The optimal combination of structural parameters for the seed guiding constraint device was determined through single-factor and multi-factor tests. The results showed that when the radius of the guiding are surface was 26 mm, the average seed trajectory offset was 23. 75 mm and the average detachment velocity angle was 15.64°, which had a good guiding effect. When the groove width was 3. 0 mm, the groove depth was 1. 5 mm and the groove radius was 2. 0 mm, the uniformity of seed drop reached the optimum, and the coefficients of variation for the uniformity of the seed amount and the stability of the seed drop trajectory were 4. 83% and 14.33%, respectively. Based on this, performance verification and comparison tests were carried out. The results showed that the parent seed selection machine with the seed guiding constraint device had the coefficients of variation for the uniformity of the seed amount of 4. 97% and the carry-out ratio of 0. 19, which were reduced by 8. 07 percentage points and 0. 07, respectively, compared with the traditional device. The recognition rate was 97.81% and the rejection rate was 95.25%, which were increased by 4. 13 and 4. 49 percentage points, respectively, compared with the traditional device. The use of the seed guiding constraint device can significantly improve the operation performance of the parent seed selection machine. This research can provide technical support for the design and application of parent seed selection machines.

Abstract:Aiming to address the seed retention problem encountered in the precision grading process of maize parental seeds, a Venturi seed-mixing device was designed. Based on an analysis of the overall structure and working principle, the internal flow-field parameters of the Venturi mixer were calculated, and the particle dynamics of seeds were investigated. The results indicated that key structural parameters, namely the seed-inlet section angle, converging-section angle, mixing-section length, seed-inlet position, and mixing-section diameter, were the primary factors affecting seed flow behavior. Using a coupled CFD - DEM simulation approach, single-factor and multi-factor experiments were performed. The results showed that the significance of factors affecting the outlet seed velocity and mass flow rate decreased in the following order: seed-inlet section angle, mixing-section diameter, and seed-inlet position. Based on Box - Behnken response surface optimization, a regression model was established and the optimal parameter combination was obtained: seed-inlet section angle of 50°, seed-inlet position of 21 mm, and mixing-section diameter of 46 mm. Under this combination, the simulated outlet seed mass flow rate and mean seed velocity were 156.42 g/s and 5.92 m/s, respectively. High-speed imaging experiments were further conducted to validate the simulation results. The measured mean outlet seed velocity was 6.16 m/s with a relative error of 4.09%, and the measured seed mass flow rate was 164.43 g/s with a relative error of 5.12%.

Abstract:High-quality seeds are fundamental to achieving stable crop yields, and seed-production rice plays a critical role in ensuring national food security. Compared with conventional rice, seed-production rice is characterized by a lower seed-setting rate and a higher proportion of unfilled grains, which often leads to increased grain loss and impurity rates during combine harvesting. However, the lack of accurate discrete element method (DEM) parameters for threshed materials results in discrepancies between simulation predictions and actual harvesting performance, thereby restricting the effectiveness of simulation-based cleaning device design and optimization. To address this issue, the contact parameters of threshed materials from seed-production rice combine harvesting were systematically calibrated by using DEM. The intrinsic parameters, contact parameters, and suspension velocity of filled grains, straw segments, and unfilled grains were experimentally determined. Taking the physical repose angle of mixed threshed materials as the calibration response indicator, Plackett - Burman, steepest ascent, and Box - Behnken experimental designs were successively employed to screen and optimize the significant contact parameters. The optimal parameter combination was obtained by using the optimization module of DesignExpert software. The optimal parameter set included a static friction coefficient of 0.87 between filled and unfilled grains, a restitution coefficient of 0.45 between unfilled grains, and a rolling friction coefficient of 0.037 between unfilled grains. The relative error between simulated and physical repose angles was 0.57%, while the relative errors of simulated suspension velocities for filled grains, straw segments, and unfilled grains were 1.37%, 1.95%, and 3.73%, respectively. Based on the calibrated parameters, CFD - DEM coupled simulations of the cleaning process in a seed-production rice combine harvester were conducted and validated by bench tests. The simulated grain loss rate and impurity rate were 0.17% and 0.58%, respectively, with relative errors of 5.56% and 4.92% compared with experimental results. These findings confirmed the accuracy and applicability of the calibrated parameters and provided reliable fundamental parameters for CFD - DEM coupled simulation and optimization of seed-production rice combine harvesting.

Abstract:Detection of incisions on natural rubber trees and identification of rubber tapping lines are of great significance for achieving automated tapping and enhancing the intelligence of operations. In response to the problem that the recognition of incisions and the identification of tapping lines by tapping machines are easily disturbed by the natural environment under complex conditions, leading to incomplete incision recognition and inaccurate tapping line fitting, a method for detecting incisions and identifying tapping lines on rubber trees in complex environments based on an improved UNet was proposed. To address the issue of low recognition efficieney of the UNet model, the VGG16 downsampling module in the original UNet was improved to ChostNet, reducing the model's parameter quantity, improving the model's accuracy and detection efficiency; the improved SegNext attention mechanism was integrated to optimize the model's segmentation effect on smaller features; and an improved feature pyramid network (FPN) decoding architecture was introduced to enhance the model's ability to capture semantic information. Secondly, after detecting the target incision, the Shi - Tomasi algorithm was used, with the minimum eigenvalue of the matrix as the corner response function, and non-maximum suppression was introduced to select the best starting and ending points of the tapping line. Finally, the random sample consensus (RANSAC) algorithm was used for multiple iterations to select the lower edge points of the incision and then fit the precise tapping line. The experimental results showed that the average intersection over union and average pixel accuracy of the improved UNet model vere 92. 86% and 96. 96%, respectively, which were 1.38 and 0. 52 percentage points higher than those of the original UNet model. In addition, the model's memory usage was compressed to 18. 55 MB through structural optimization, a reduction of 80. 46% compared with that of the original UNet. The average positioning error of the starting point by the Shi - Tomasi cormer detection algorithm was 10. 2 pixels, and the average positioning error of the ending point was 8. 99 pixels, which can effectively detect the starting and ending points of the incision. The average inlier mean square error of the fitted line by the RANSAC algorithm was 4. 26 pixels2, and the average inlier root mean square error was 1. 86 pixels. The research results can provide a reference for the detection of incisions and identification of tapping lines in complex environments in rubber plantations using intelligent equipment.

Abstract:Mechanical maize precision seed meters operating at high forward speeds are prone to seed leakage and blockage. To address these problems, a planetary gear driven disturbance-assisted precision seed metering device was developed. The device adopted an NGW-type planetary gear train, and an agitator ring mounted on the planetary shafts imposed reverse differential disturbance on the seed population in the filling zone to enhance the seed-filling process. By establishing a theoretical model and performing dynamic analysis of the seed-filling process, the main influencing factors and structural parameters of the key components affecting filling performance were determined. Based on the Box - Behnken design, a three-factor, three-level response surface experiment was conducted, with agitation length, number of disturbing rods and forward speed as experimental factors, and qualified rate, multiple rate and leakage rate as evaluation indices. Comparative tests on seed-filling performance were also carried out. The results showed that the optimal parameter combination was agitation length of 30 mm, three disturbing rods and forward speed of 12 km/h, under which the qualified rate, multiple rate and leakage rate were 92.61%, 3.95% and 3.46%, respectively. Field validation under the same parameter combination yielded qualified rate of 91.42%, multiple rate of 4. 35% and leakage rate of 4.23%, satisfying the operational requirements for mechanical precision maize seeding. The findings provided theoretical and technical support for the structural design and parameter optimization of highspeed mechanical maize precision seed metering devices.

Abstract:Aiming to address the low efficiency of conventional sowing techniques in soybean breeding, as well as frequent seed-rope loosening/breakage and excessive furrow -opening resistance, a seed guide - ditching system integrating damping control with an exponential guide curve was developed. Through mechanical analysis combined with discrete element method (DEM) simulations, the dynamic regulation mechanism of the damping torque in the seed-rope guiding device was elucidated, and an exponential-function based optimization method for the nose guide curve was proposed. A centrifugal force-amplified, negatively correlated friction damper and a seed-rope furrow opener were designed as the core components. A three-factor, five-level orthogonal rotational combination experiment was conducted with guiding-tube outer diameter, entry angle, and seeding depth as the experimental factors, while EDEM simulations were used to clarify the interactive effects of these factors on draft force and soil disturbance. The results showed that the guiding-tube outer diameter had a highly significant effect on all performance indices (P<0.01). The optimal parameter combination was a 30 mm outer diameter of the guide tube, 32°entry angle, and 45 mm seeding depth, under which the draft force was reduced to 360.1 N and the soil disturbance coefficient was controlled at 23.4%. Field validation confirmed that the optimized device operated with a draft force below 369 N and produced furrows with a soil disturbance coefficient below 29%. With the damping device installed, the seed-rope loosening length remained below 26 mm and no seed-rope breakage occured, meeting the high-precision sowing requirements of soybean breeding.

Abstract:Aiming at the problem that on-film transplanting of sweet potato transplanters failed to meet the agronomic requirements of "small film opening and more buried nodes",based on the agronomic requirements of hull-shaped transplanting for sweet potato seedlings, an idea of pressing and cutting was proposed, and a reciprocating direct-insertion on-film transplanting device for sweet potato seedlings was designed. The overall structure and working principle of the device were elaborated, the structural parameters were determined, and a mathematical model of transplanting motion was established. Taking the transplanting trajectory of the device as research object, with driving speed ratio, installation angle of seedling inserting rod and length of seedling inserting rod as factors, and theoretical film opening area and theoretical length of sweet potato seedlings in ridge as indicators, trajectory simulation tests were carried out. The single-factor experiment determined the influence laws and optimal parameter ranges of each factor, with the driving speed ratio being 1.92~2.40(°)/mm, the seedling insertion mounting angle of 80°~100°, and the seedling insertion length of 180~220 mm. A three-factor three-level bench orthogonal test was conducted under the conditions of transplanting depth of 80 mm and within the optimal parameter ranges, and the optimal parameter combination was obtained: driving speed ratio was 2.24(°)/mm, installation angle of seedling inserting rod was 90°, and length of seedling inserting rod was 204 mm, corresponding to a film opening area of 1580 mm2 and a length of sweet potato seedlings in ridge of 234 mm. Field verification results showed that under the conditions of transplanting depth of 80 mm and the optimal parameter combination, the actual film opening area was 1680 mm2 and the length of sweet potato seedlings in ridge was 221 mm, with relative errors of 6.1% and 5.8% compared with the bench test, respectively. Both errors were within an acceptable range, and the design objectives were achieved.

Abstract:As one of the important vehicles, helicopters play a key role in the forest-protection and pest control task. Compared with unmanned vehicles, helicopter can fly faster, carry more payloads with longer endurance. Avoiding entering the H -V diagram is a key issue for a helicopter's take-off and landing phase, and a significant task is to automatically identify entering the area. A method was developed for helicopters entering the H - V diagram based on support vector machine (SVM) theory, which had significant value for helicopters' safety management and flight evaluation. By selecting some data of a helicopter's H -V diagram as the training and testing groups, and the cross-validation algorithm was used to optimize kermel function's parameters, a prediction model for H - V diagram was developed based on SVM. Both the poly and RBF kernel functions vere adopted for comparing the test results, and also the flight data (height-velocity) around the H -V diagram were identified based on the prediction model. The calculation showed that although the same accuraey (0. 894) was obtained by using the poly and RBF kernel models, the RBF model's predietion accuraey got to 100%,beter than poly kermel model (97.3%), which again showed that the RBF kernel model had enhanced generalization ability. In the future work, the high-speed H - V curve's identification should be emphasized so as to enhance the safety for helicopters in plant protection operations.

Abstract:Aiming to address the uneven distribution of threshed material and unstable threshing quality caused by the green stems and leaves, high straw-to-grain ratio, and high moisture content of soybeans planted under plastic film in Xinjiang during the harvest period, key material parameters were clarified through systematic measurement of the plant's morphological characteristics, moisture content of its components, and frictional properties between the material and threshing components. Based on these parameters, a vertical axial-flow threshing device was designed. Single-factor threshing experiments were conducted with drum speed, feed rate, and threshing clearance as experimental factors to systematically analyze the distribution pattern of the threshed material. The results indicated that the average stem diameter of the soybeans was approximately 9.59 mm, and the tri-axial dimensions of the seeds approximately followed a normal distribution. The lateral compressive foree threshold for seeds did not exceed 73. 7 N. The friction coefficient between stems and steel plate was the highest at 0. 58, and the stem moisture content was the highest at 34. 0%. The mass of threshed material along the drum axis showed a trend of rapid increase followed by a slow decrease, while the radial distribution was characterized by higher mass at both sides and lover in the center. At a drum speed of 500 r/min, the axial and radial coefficients of variation were decreased to 50. 05% and 50. 82%, respectively, indicating optimal distribution uniformity. An increase in feed rate led to a significant rise in the radial coefficient of variation and a decrease in uniformity. The radial distribution was most uniform at a threshing clearance of 25 mm, with a coefficient of variation of 59.73%. The findings can provide a reference for the structural optimization of threshing devices and the setting of operational parameters to adapt to the material characteristics of soybeans under plastic film mulch.

Abstract:Aiming to investigate the energy transfer characteristics and patterns of jujube trees during vibratory harvesting, a visualization analysis method for the structural intensity of jujube trees was proposed. By integrating ANSYS and self-developed Matlab programs, a structural intensity vector field analysis model for jujube trees was established, and the structural intensity vector field of jujube trees under different excitation conditions was solved and visualized. Quantitative analysis based on the vibrational energy flux ratio revealed the following: at low -frequeney vibrations (the Ist and 2nd natural frequencies), energy primarily remains in the trunk; as the frequeney inereases (the 3rd and 4th natural frequencies), energy was uniformly transferred to various structural parts of the jujube tree. Under circular or linear excitation, energy mainly stayed in the trunk, whereas under non-circular or threedimensional excitation, energy was evenly distributed to all structural parts of the jujube tree. As excitation time increased, the energy transfer path gradually changed, but it stabilized after 3 seconds. The energy transfer efficiency was positively correlated with excitation height. Changes in excitation force did not affect the energy transfer path. For side branches at similar heights, energy transfer efficiency was positively correlated with the cross-sectional area at the connection and the length of the side branch, but negatively correlated with the growth angle. For side branches on the same side, as the height of the side branch increased, energy transfer efficiency was negatively correlated with the cross-sectional area at the connection and the length of the side branch. For side branches with similar growth angles, energy transfer efficiency was positively correlated with the height of the connection point but negatively correlated with the cross-sectional area and the length of the side branch. For side branches of similar length, energy transfer efficiency was positively correlated with the height of the connection point and the growth angle but negatively correlated with the cross-sectional area. Vibration experiments verified that the relative error of the analysis model was within 7.3%, indicating high reliability.

Abstract:In order to improve the picking speed and stability of picking manipulator, a continuous two-stage image-based visual servo control method combined monocular camera and laser distance sensor was proposed. The YOLOv5s network was used to detect the target fruit, and then threshold segmentation was performed on the detected target to obtain the target area. In the visual servo process, the center point of the target fruit was taken as the image feature, and the velocity control law of the manipulator joint was established combined with PD control. The visual servo process was divided into alignment stage and approach stage, and the target depth information in the two stages was obtained from the radius estimation of the target in the image and the measurement data of the laser ranging sensor, respectively. In order to ensure the continuity of longitudinal velocity during phase switching, the dynamic weight method was used to smooth the velocity in the transition zone. The joint velocity was calculated by the dynamic damping least squares method to avoid the out-of-control of the manipulator when approaching the singular configuration area. The apple picking experiment was carried out. The results showed that the proposed continuous two-stage control method reduced the movement time of the manipulator from 4.52s to 2.56s, the maximum absolute positioning error without disturbance was 4.00mm, and it could stably pass through the singular configuration area of the manipulator, which verified the high speed and strong robustness of the method.

Abstract:Fruit-tree canopy structure is a key determinant of fruit yield and quality, and its threedimensional (3D) reconstruction can provide essential data support for precision orchard management and the development of smart orchards. Traditional manual measurements are inefficient and error-prone, and are increasingly inadequate for moder orchard management, whereas 3D monitoring systems based on LiDAR, unmanned aerial vehicles (UAVs) and multi-sensor fusion have been widely used for the intelligent detection of canopy volume, leaf-wall area and other structural parameters owing to their advantages in accuracy, automation and repeatability. To this end, this paper focuses on the theme of 3D reconstruction for detecting the structural parameters of fruit-tree canopies. By folloving the "equipmentmethods-workflow" framework, it systematically reviews recent research progress and discusses current challenges and future development trends. Firstly, the functional characteristics and key performance metrics of aerial, ground-based and fixed/ mobile scanning devices and platforms for 3D data acquisition were categorized and described. Secondly, the image-based, point-cloud-based and deep-learningassisted reconstruction methods were comparatively analyzed in terms of their specific advantages, limitations and suitable application scenarios. Furthermore, for the standardized workflow of fruit-tree canopy point-cloud processing (" registration-ground separation-single-tree segmentation-task-oriented reconstruction"), the processing steps and error sources involved in deriving typical canopy structural parameters were clarified, and coverage and other quality-control indicators were used to jointly assess parameter-detection accuracy and the methodological generalizability across orchards and platforms. Finally, key research challenges such as occlusion in complex canopies and muli-platform coordination were summarized, and future development directions for fruit-tree canopy reconstruction and monitoring that were jointly driven by unified standards and task-specific requirements were proposed. This review provided a systematic technical framework for 3D information acquisition and standardized detection of canopy structural parameters in smart orchards, and offered effective support for fine-scale operations such as variable-rate spraying and pruning optimization.

Abstract:When using UAV remote sensing to monitor wheat scab, the size of the object in the captured images decreases as the UAV's flight altitude increases. This makes data annotation more challenging, requiring more time and labor. To address this issue, an unsupervised UAV-based wheat scab detection method was proposed. The method proposed a space-to-depth domain adaptation detection network (SPD - DANet), which learned the knowledge of the source domain data through the adversarial idea and transfered it to the unlabeled target domain thereby realizing unsupervised wheat scab detection. Firstly, to tackle the issue of small scab lesions in UAV -captured wheat images, a spatial-to-depth feature extractor (SPD - FE) that transformed spatial information into depth information was designed. This enabled the network to learn features of small scab objects more effectively. Secondly, using SPD - FE, adversarial-based classification adaptation and bounding box regression adaptation modules for the detection network were constructed. This enabled domain adaptation learning sequentially in classification and bounding box regression steps, leveraging source domain knowledge for unsupervised detection in the target domain. The experimental results showed that the proposed method improved the detection accuracy ( AP50) by 5.3 ~ 20. 5 percentage points compared with other object detection methods such as DETR, YOLO v5, etc., and performed the best on the unsuperrised detection task of wheat scab, and its detection accuracy AP50 was improved by 6.5 percentage points compared with that of the baseline network. The research can provide some support and help for the unsupervised detection of wheat scab.

Abstract:Aiming to address the issues of fragmented domain knowledge, diverse heterogeneous document formats, and the urgent demand for intelligent decision-making in the field of agricultural machinery management, an automated knowledge extraction and fusion method that combined a BERT pretrained language model with domain ontological rules was proposed, with the aim of constructing a high-quality knowledge base for agricultural machinery management. Firstly, a domain ontology covering categories such as agricultural machinery equipment, maintenance activities, fault diagnosis, and policies and regulations was designed. Secondly, with the help of the BERT pretrained model, entities and relations were accurately extracted from multi-source texts, including monographs on agricultural machinery management, academic literature, technical manuals, and policy and regulatory documents, and the extraction results were validated and de-duplicated by using ontological rules. Finally, the entities, relations, and high-quality triples were loaded into a graph database to support intelligent question answering and decision analysis applications. Experimental results showed that the proportion of high-confidence triples produced by the relation extraction model reached up to 88. 9% across different data sources; the intelligent question answering system achieved an accuracy of 90. 9% on 450 typical business test cases, with a hallucination rate as low as 3.1% and fully traceable answers, and its performance was significantly better than that of general-purpose large models such as CPT - 4o. The system attained an average end-to-end response latency of 150 ms, a throughput of 200 req/s, and kept resource utilization within a reasonable range. This method not only enabled automated and efficient integration of knowledge in the field of agricultural machinery management, filling a gap in related research, but also provided a replicable and continuously evolvable technical path for decision support in smart agricultural machinery.

Abstract:Straw coverage on the soill surface after tillage is a key parameter for evaluating the quality of straw return to the field. Existing methods struggle to effectively identify individual rice straw stalks. To address this, a semantic segmentation method that integrated prior embedding with multi-scale feature fusion was proposed to achieve accurate recognition of individual rice straw stalks. A fixed-threshold segmentation method based on color distance was employed to preprocess the original image and generate a prior map, providing prior information input for subsequent recognition tasks while suppressing background interference. An enhanced U - Net model, MRCF - DA - CDPE, was designed. It employed parallel multi-scale convolutions to capture information features across scales--from fragmented straw fragments to large straw clusters-while utilizing channel and spatial attention to select key features and suppress disturbances like soil bright spots. Continuous distance information from preprocessing was embedded into the network as an additional input channel, providing physical guidance for segmentation. Image preprocessing strategies demonstrated that this approach enhanced the average accuracy of each base model by 2 ~ 4 percentage points, with U - Net achieving the highest recognition accuracy. Validation tests of the improved model demonstrated that the MRCF - DA - CDPE model achieved 86.93% average intersection-over-union ratio (I0U), 94.89% average precision, and 0. 850 2 Kappa coefficient, representing improvements of 2. 72, 2.98 percentage points and 0. 056 7 respectively over the baseline U - Net model. This method achieved precise recognition of individual straw stalks, providing technical support for straw return quality inspection and tillage effectiveness evaluation.

Abstract:Against the backdrop of global climate change, the increasing frequency of extreme weather events poses serious threats to agricultural production. As a major maize-producing region in China, Northeast China faces the challenge that key growth stages of maize overlap with the period of frequent regional heavy rainfall, leading to recurrent waterlogging disasters that severely endanger grain security. High-resolution remote sensing technology offers an effective means for rapid disaster monitoring. However, existing methods predominantly rely on water feature extraction from optical and radar imagery, which depends on prior knowledge of surface features and shows limited effectiveness in identifying "implicit" waterlogging areas where the canopy is not submerged. This is particularly evident in the inadequate accuracy and boundary consistency for detecting small-scale affected areas. To address these issues, the first high-resolution semantic segmentation dataset for implicit maize waterlogging disasters was constructed based on 3 m Planet and 4 m State Grid remote sensing images, filling the gap in high -quality labeled data. Furthermore, a DSA - DeepLab semantic segmentation model was proposed, which incorporated a DenseASPP module and an adaptive feature fusion module embedded with selective kernel attention, thereby effectively enhancing the detection capability for small targets and complex boundaries. Experimental results demonstrated that the proposed model achieved mean intersection over union and mean pixel accuracy of 80.70% and 88.60%, respectively, representing improvements of 1.63 percentage points and 1.67 percentage points over the baseline model. Specifically, for implicit waterlogging, the IoU and recall rates were increased by 2. 72 percentage points and 3. 38 percentage points, respectively, outperforming that of several mainstream semantic segmentation methods. Additionally, to address the memory overflow issue caused by large-size image inputs during inference, an overlapping patch extraction-edge ignoring strategy was adopted, effectively suppressing stitching artifacts and boundary errors. This research achieved high-precision automated extraction of implicit maize waterlogging disasters, providing reliable technical support for disaster assessment and agricultural insurance claims.

Abstract:Aiming to address the issue of insufficient detection accuracy for wheat heads in complex field environments caused by plant occlusion and growth stage variations, a detection algorithm named GB YOLO v5su, was proposed which integrated lightweight design with multi-scale feature enhancement. Building upon the YOLO v5su model, the algorithm reconstructed the backbone network by incorporating Ghost modules to form a lightweight GhostNet structure, substantially reducing computational complexity and model parameters while maintaining representational capacity. The original PANet was replaced with a weighted bidirectional feature pyramid network (BiFPN), which enhanced multi-scale feature representation through efficient cross-scale connections and a learnable adaptive weighting mechanism. Subsequently, a P2 detection layer was introduced to construct a four-scale feature pyramid, effectively leveraging high-resolution shallow features to improve detection sensitivity for small-scale wheat heads. Furthermore, the MPDIoU loss function was adopted to replace CIoU, which optimized bounding box regression by directly minimizing the corner point distance, thereby improving localization accuracy. Experimental results on the public GWHD2021 dataset demonstrated that the proposed algorithm reduced the model parameter count to 5.38×10? while achieving an average precision (AP50) of 92.8%, striking an effective balance between accuracy and efficiency. Extensive ablation studies and cross-dataset tests further validated the contribution of each module and confirmed the strong robustness of the algorithm. The research result can provide an efficient, practical, and lightweight deployment solution for achieving realtime and accurate wheat head detection in resource-constrained field edge computing scenarios.

Abstract:The electrical penetration graph (EPG) technique serves as a critical tool for analyzing psyllid feeding behaviors, investigating psyllid-citrus host interactions, and exploring pathogen transmission mechanisms. Nevertheless, the interpretation of EPG signals still heavily relies on manual effort, which remains inefficient and labor-intensive. To address this issue, an automated EPG recognition method was proposed based on deep transfer learning, which integrated a Transformer and a bidirectional gated recurrent unit (BiGRU) to enhance recognition performance under limited sample availability. The proposed method began with variational mode decomposition ( VMD) to suppress noise in raw EPG signals, followed by a hybrid model, combining a Transformer encoder and a BiGRU network. This architecture leveraged the Transformer's global perception and the BiGRU's local temporal modeling capability to collaboratively extract discriminative waveform features. To mitigate the scarcity of labeled psyllid data, the model was firstly pre-trained on a larger aphid EPG dataset and then transferred to the target psyllid dataset via a domain adaptation strategy. Cross-species knowledge transfer was accomplished through a fine-tuning mechanism. Experimental results demonstrated that the model achieved a recognition accuracy of 98. 50% when using 40 samples per category with full-parameter fine-tuning. Under resource-constrained conditions (20 samples per category with only the last two layers fine-tuned), the model still maintained an accuracy of 97.54%. The research result demonstrated the viability of integrating transfer learning with deep learning-based feature extraction for automated analysis of psyllid feeding behavior, while also offering a valuable reference for analyzing EPG signals in other piercingsucking insects.

Abstract:Dendroctonus valens is a harmful forest pest that could cause major economic and environmental damage without effective control. Recently, combining UAV remote sensing with deep learning-based object detection has become an effective way to monitor large forest areas. However, due to the small size and uneven distribution of infected trees, there are issues of missed and false detection during the target detection process. To address these challenges, an SCW-YOLO v8 model based on deep learning which included three improvements was proposed. Firstly, a small object detection layer was added after the second C2f layer in the YOLO v8 Backbone structure to increase the detection accuracy for smaller-sized objects. Then, to reduce the false detection rate and improve detection accuracy, a coordinate attention (CoordAtt) mechanism was integrated into the backbone structure of YOLO v8. Finally, the WIoU loss function was introduced to improve detection accuracy. The dataset was constructed based on the infected forest of Dendroctonus valens, which were photographed at a height range of 190 m to 240 m. In this dataset, most of the infected trees to be detected were small objects. The experiment consisted of four parts. Firstly, a comparative analysis was conducted between YOLO v8n, YOLO v5n, YOLO v9t, YOLO v10n, and YOLO 11n algorithms, through experiment chose the optimal base network model. The experimental results showed that the YOLO v8 model outperformed other object detection algorithms in both mAP and F1-scores, and its model size was relatively suitable. Therefore, the YOLO v8 model was more appropriate to serve as the base object detection model. Then, the detection results of the CoordAtt were compared with that of CBAM, GAM, and SE attention mechanisms, demonstrated the effectiveness of CoordAtt, its mAP and F1 scores were 88.8% and 83.9%. The third part of the experiment focused on the selection of the loss function. Three versions of the WIoU loss function were compared with CIoU, DIoU, and EIoU loss functions. The results showed that WIoUv3 achieved the best mAP (90.0%) and F1-scores (85.1%). Therefore, this loss function was selected as one of the improvements. Finally, ablation experiments were conducted to verify the effectiveness of each improvement method. Among them, adding small object detection layer increased the mAP to 90.4% and the F1 scores to 86.5%. The final SCW-YOLO v8 model, which integrated all improvement methods, achieved a maximum mAP of 91.6% and a maximum F1-scores of 86.8%. The analysis of the detection results demonstrated that small object detection layer could improve the detection accuracy for small objects. On this basis, CoordAtt attention mechanism could reduce false detections of incorrect targets, and the WIoUv3 loss function could further enhance detection accuracy. In summary, the SCW-YOLO v8 model proposed offered an efficient and reliable solution for forest pest detection, especially for the detection of small object Dendroctonus valens infected trees.

Abstract:In modern dairy goat farming, accurate recognition and tracking of estrus behavior is crucial for improving breeding efficiency and management standards. Aiming at the lack of accurate and timely behavior analysis and monitoring methods for the estrus of dairy goats in traditional breeding, an estrus behavior recognition and tracking method of dairy goats was proposed. Firstly, the lightweight SimAM attention mechanism was used to enhance the feature extraction capability of YOLOv8s, and a rotating bounding box model (YOLOv8s_obb) and a conventional rectangular box model (YOLOv8s) were used to detect the mounting and the no mounting dairy goats, respectively. Then, combining the detected behavior category information and bounding box parameter information with the DeepSort tracking algorithm, the matching frame count mechanism was introduced into IoU matching process to enhance the correlation between the mounting and the no mounting dairy goats, and accurately determined the dairy goats in estrus, and obtained stable ID, which can ensure the accurate tracking of estrus behavior in successive image frames. The results showed that the AP of the improved model for detecting mounting and non-mounting behaviors in dairy goats was improved by 0.90 percentage points and 2.64 percentage points compared with YOLOv8s_obb and YOLOv8s, respectively. As for estrus goat tracking performance, compared with DeepSort, BotSort, StrongSort and ByteTrack, the proposed method achieved the highest HOTA and IDF1 scores, reaching 72.4% and 80.3% respectively, which can provide strong support for the reproductive management of dairy goats.

Abstract:The content of soil organic matter (SOM) is an important indicator for evaluating soil fertility and ecological quality. However, the chemical analysis methods prescribed by national standards for detecting SOM content are difficult to meet the demands of rapid detection. To provide a foundation for the development of a rapid SOM content detector, a total of 760 soil samples were collected from apple orchards in ten provinces in northern China with complex soil types and significant spatial differences. According to geographic and climatic characteristics, the samples were grouped into four regions: North China, Northeast, Northwest Arid, and Northwest Frontier. Soil reflectance spectra from 400~2450nm and SOM contents measured following the national standard method were used to examine the effects of SOM levels and regional differences on spectra and the linear correlations between reflectance and SOM. Partial least squares regression (PLSR), support vector regression (SVR), and least-squares support vector machine (LS-SVM) models were then constructed to predict SOM. The Northeast region had the highest mean SOM content (25.443 g/kg), while the Northwest Frontier had the lowest (13.286 g/kg). Soil reflectance showed an overall negative correlation with SOM. LS-SVM achieved the best prediction performance for North China and Northeast samples, with the residual prediction deviation (RPD) values of 2.814 and 2.475. PLSR performed best for the Northwest Arid and Northwest Frontier regions (RPD value of 2.888 and 3.572). For mixed samples from all four regions, LS-SVM provided the highest accuracy (RPD value of 2.864). These results indicated that building a universal SOM prediction model for apple orchard soils in the ten northern provinces of China was feasible, while building region-specific models was able to improve prediction accuracy for most regions.

Abstract:A deep learning detection method integrating graphical features is proposed to address the demand for accurate and rapid monitoring of soil nutrient content. Initially, hyperspectral data of soil samples were collected using a hyperspectrometer, followed by laboratory-based chemical measurements of five key soil properties: alkaline hydrolyzable nitrogen (N), available phosphorus (P), available potassium (K), H?, and organic matter (OM). Prediction models were then developed using partial least squares (PLS) and random forest (RF) algorithms. Through feature selection techniques- including competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA), and least angle regression (LARS)- a total of 17 characteristic spectral bands were identified. Subsequently, a Transformer-based prediction model was constructed by combining deep visual features and spectral reflectance features. Specifically, color images of soil samples were segmented using the DeepLab v3 + network to extract convolutional neural network (CNN) features, which were then fused with fully connected neural network (FCNN) features derived from the reflectance values of the selected characteristic bands. Experimental results demonstrate that the detection accuracies for the five soil parameters reached 91.9%, 82.4%, 91.3%, 97.7%, and 92.3%, respectively. The proposed method achieves comprehensive and accurate prediction of soil nutrient content with a limited number of spectral bands, thereby providing a theoretical foundation for the future development of low-cost, portable, and efficient soil nutrient detection devices.

Abstract:Phenol-rich bio-oils served as an important source of high value-added chemicals and held great potential for a wide range of industrial applications. In order to improve the relative content of phenolic compounds in bio-oil produced from catalytic pyrolysis, pine sawdust was used as the feedstock and prepared carbon-based bimetallic catalyst for catalytic pyrolysis. The catalytic types, pyrolysis temperature, pine sawdust to catalyst ratio and bimetallic loading ratio were selected as experimental variables and the mechanisms of phenolic compound formation over carbon-based bimetallic were investigated. The results showed that the relative content of phenolic compounds in bio-oil was from 14.61% to 69.42%~83.32%. Studies revealed that the optimal conditions for preparation of phenolics were under Fe-Cu catalyst at 550°C, the biomass-to-catalyst ratio of 1:1 and bimetallic loading ratio of 5:5. Under these conditions, the relative content of phenolic compounds in the bio-oil reached 83.32%. The specific surface area of the catalyst significantly increased after metal modification, facilitating the loading of more active sites for enhanced reaction participation. After modification, Fe was successfully anchored onto the carbon carrier, and upon calcination, different metals formed an alloy. The particles on the surface of the monometallic catalyst tended to aggregate more, whereas the bimetallic catalyst exhibited improved dispersion, which was beneficial for catalytic reactions. Cu loaded onto the carbon carrier was transformed into a CuFe/Cu/CuO mixed valence heterostructure, which enhanced the catalytic performance of the catalyst and facilitated the generation of phenolics from pyrolysis intermediates through deoxygenation and rearrangement reactions.

Abstract:Aiming to address the issues of poor temperature uniformity and high energy consumption during agricultural product drying, the computational fluid dynamics (CFD) was employed to optimize both the structure and operational parameters of the air supply system in a drying chamber. A threedimensional model of the drying chamber was established, and comparative analysis of different air supply schemes revealed that the side supply and side return method generated a “C” shaped temperature distribution, reducing the velocity non-uniformity coefficient by 21.0% and the temperature nonuniformity coefficient by 38.3%. Orthogonal experiments demonstrated that the air supply temperature predominantly governed temperature uniformity, with the temperature non-uniformity coefficient decreased by 26.6% under the 260°C condition compared with that under the 60°C condition. The air supply velocity regulated energy efficiency, with the 10m/s medium-speed condition achieving the highest energy utilization coefficient. The parameter combination of 260°C and 10m/s achieved an optimal balance between energy efficiency and temperature uniformity. Experimental validation showed an average relative deviation of 4.57% between simulated and measured temperatures. Further investigation of flow field characteristics under actual load conditions indicated that although the tray rack increased flow resistance, intense turbulence effectively disrupted temperature stratification and improved uniformity. The research proposed an optimized solution centered on the side supply and side return method combined with 260°C and 10m/s parameters, providing important theoretical basis and data support for the design of high-performance, low-energy consumption drying equipment for agricultural products.

Abstract:Aiming at the problems of invasiveness, low efficiency and reliance on single-time-point data in traditional hatching egg sex identification methods, to realize non-destructive and high-precision sex detection of hatching eggs during incubation, taking Jingfen No. 1 breeding eggs as the research object, a method integrating hyperspectral imaging and deep learning was proposed, and a multi-stage temporal dynamic detection system was constructed. A dual-channel hyperspectral acquisition system was independently developed to collect data in the 400~1000nm band on the 4th, 7th, 10th, and 13th days of incubation. The central region of interest (ROI) of the egg body was extracted via ellipse fitting. After preprocessing, including Savitzky-Golay smoothing, principal component analysis (PCA) dimensionality reduction and data augmentation, an improved ResNet-18 model incorporating the squeeze-and-excitation (SE) attention module was constructed, and multi-stage temporal feature fusion was realized by combining the long short-term memory (LSTM) module. The results showed that the 10th day of incubation was the optimal detection window, with the sex identification accuracy of the single-period model reaching 82.99%. The accuracy of the temporal fusion model was further improved to 85.2%, 3.3 percentage points higher than that of the standard ResNet-18. The improved model had only 9.9×10? parameters and an inference time of 47 ms per sample, balancing detection accuracy and efficiency. The proposed method overcame the lag and invasiveness defects of traditional technologies, and provided a reliable technical scheme for the intelligent and green development of poultry breeding.

Abstract:In view of the shortage of portable, nondestructive and real-time monitoring equipment for salmon freshness in the current market, a portable detection system based on perovskite quantum dots (CsPbBr3 QDs) fluorescence detection technology was developed, which mainly included two parts: preparation of high-performance CsPbBr3 QDs fluorescent nanomaterials and integration of portable fluorescence detection devices. For QD synthesis, oleic acid and oleylamine were used as ligands. Cesium oleate precursor was hot-injected into an oleylamine (OLA)-bromobutyric acid (BBA) mixture, followed by ethanol centrifugation and purification to obtain CsPbBr3 QDs. Characterization confirmed successful synthesis, with the QDs exhibiting high sensitivity and selectivity toward NH3 and undergoing static quenching upon contact with NH3. The QD-based fluorescent film retained the quantum dots' fluorescence intensity, showing favorable ammonia response (R^2=0.97) as well as good stability and repeatability. Through hardware-software co-design, a multifunctional portable device was constructed, featuring fluorescence excitation/detection, temperature-humidity monitoring, wireless data transmission, and threshold alarm functions. Real-time detection data were uploaded to a mobile App and upper computer for visualized display and storage. Field tests on salmon samples using the CsPbBr3 QDs film on the self-built portable platform revealed good linear correlations between the detection signal and TVB-N content at 4°C (R^2=0.93) and 25°C (R^2=0.94). This method can provide a convenient, efficient, and visual rapid detection approach for food quality assessment, and offer a reliable tool for fresh food supply chain management.

Abstract:The cross-chain technology addresses the issue of information silos between the blockchains of different enterprises in the aquatic product supply chain. However, there is a risk of privacy leakage during cross-chain transactions. To enhance the security and privacy of cross-chain transactions for aquatic products, based on the business processes of aquatic product transactions and existing cross-chain technologies, a cross-chain transaction model for aquatic products was proposed by using sidechains. The model designed an interoperation process for assets between the main chain and sidechains, and a two-way anchoring of assets between the main chain and sidechains was implemented through smart contracts. Based on the characteristics of aquatic product cross-chain transactions, the model designed the data structure and transaction process for cross-chain data. A three-phase commit protocol was used to ensure data consistency in cross-chain transactions, improve the transaction success rate, and refine the handling process for transaction failures. Additionally, to address the potential double-spending issue introduced by sidechains, a monitoring method was adopted for control. Finally, a prototype system for cross-chain transactions of aquatic products using sidechains was implemented on the Hyperledger Fabric platform. The system architecture was structured with dedicated functional modules to support secure asset transfer, cross-chain data validation, and coordinated transaction management across participating enterprises, ensuring robustness and operational efficiency. Experimental results showed that this model effectively prevented the exposure of private keys when connecting the main chain to cross-chain operations, reducing the risk of privacy leakage. Under high transaction volumes, the average transaction success rate remained consistently above 99%, and the average transaction delay remained below 0.3s. Furthermore, by using sidechains to offload the query pressure on the main chain, parallel query tests with over 10 000 data points demonstrated a query efficiency improvement of approximately 35%.

Abstract:Under the “dual-carbon” strategy, the demand for efficient and low-emission bulk material handling equipment in the agricultural sector has been steadily increasing. Electric wheel loaders, owing to their environmental friendliness and energy efficiency, are gradually adopted in agricultural operations for material handling and field logistics. As the core energy-storage component of electric wheel loaders, the battery is subjected to large charge-discharge currents and frequent cycling under high-intensity and high-duty working conditions. These operating characteristics lead to accelerated battery degradation, reduced driving range, and increased operating costs. Targeting parallel electro-hydraulic hybrid (PEHH) wheel loaders, a coordinated optimization method for travel-speed planning and energy-management control was proposed, aiming to simultaneously mitigate battery aging and reduce overall energy consumption. Firstly, a mathematical model of the hybrid powertrain system and a battery lifetime prediction model were established. Subsequently, a coordinated optimization framework for travel speed and energy-management strategy was formulated, and the offline optimal solution was obtained by using a dynamic programming (DP) algorithm. Based on the structural characteristics and regularities observed in the offline optimal results, a rule-based online coordinated optimization strategy was developed to realize real-time travel-speed planning and electro-hydraulic power-split control. Simulation results demonstrated that the proposed online coordinated optimization algorithm achieved performance in battery life extension and energy consumption reduction that was close to the offline optimal benchmark.