Abstract:China is the world’s largest producer and consumer of edible mushrooms. As the scale of China’s edible mushroom industry continues to expand，traditional manual harvesting methods have become insufficient to meet the demands for high efficiency and low production costs. There is an urgent need to adopt intelligent control technologies to address the harvesting process, which represents the most labor-intensive segment of production. Taking the mechanized harvesting of shiitake mushrooms as an example to analyze the requirements for image recognition,machine vision, and flexible robotic arms, based on the physical characteristics of mature shiitake mushrooms ready for harvest and the attributes of shelf-based cultivation environments. It elaborated on the differences among major edible mushroom varieties, including shiitake, oyster, button, and black fungus mushrooms in terms of target recognition, path planning,harvesting methods,and robotic operational space,emphasizing the unique demands of different species on complex operational systems during harvesting. Finally, the limitations of existing edible mushroom harvesting technologies regarding recognition accuracy,damage minimization, and operational efficiency were discussed, and future development directions for mushroom harvesting robots were proposed. The research result can provide a valuable reference for the advancement of mushroom harvesting robot technology, thereby supporting the full-chain intelligent production of China’s edible mushroom industry.

Abstract:The unmanned operation of agricultural machinery is inseparable from autonomous navigation technology. With the development of sensors and the improvement of computer vision technology, the autonomous visual navigation operation of agricultural robots in greenhouses has gradually become possible. Research on the visual navigation control method for ridge-surface operation of strawberry picking robots planted in the field was conducted. It analyzed the agricultural techniques of field-grown strawberries and acquired the features of strawberry ridges based on the YOLO v8 instance segmentation algorithm. The Canny edge detection algorithm was employed to extract the edge information of the ridge surface. Two straight lines with slopes of1 and-1 were used to traverse the ridge surface, and the intercept information was statistically obtained to acquire the upper and lower endpoints of the ridge surface. The center point coordinates of the upper and lower endpoints on the ridge surface were then obtained. By connecting the upper and lower center points of the ridge surface into a straight line, the corresponding navigation line of the ridge can be obtained. An image dataset of the ridge surface of field-grown strawberries in the greenhouse environment was collected. After testing, the extraction accuracy of the navigation path was 96%, and the algorithm took 30 ms. The algorithm was deployed to the strawberry picking robot with a four-wheel Ackerman steering chassis. Combined with the preview point tracking algorithm, a navigation test was carried out on the simulated strawberry ridge. After testing，the extraction accuracy of the navigation path was 94%，and the algorithm took 30 ms. When the driving speed was 0.2 m/s，the maximum lateral offset was 32.69 mm，the average value was 22.12 mm, and the root mean square error（RMSE）was 5.37 mm, meeting the requirements for autonomous navigation control of the strawberry picking robot on the ridge surface. This control method, in conjunction with the autonomous picking function of the picking robot, can enable the unmanned autonomous operation of the strawberry picking robot.

Abstract:The development of intelligent agriculture is the future trend in the agricultural field，and the development of intelligent harvesting equipment is a key issue in promoting the transformation and upgrading of the farming industry. Given the complexity of the tomato picking environment, small mobile space, and other issues, an autonomous tomato picking robot suitable for high-efficiency picking under the wide trench and narrow ridge greenhouse planting mode was designed. The actuator of the picking robot consisted of a four-degree-of-freedom telescopic robotic arm, a multi-positional wrist joint, and a three-finger twisting picking end hand. By analyzing the growth of tomatoes and the operating environment，a rope-row type of retractable mobile joint was designed to reduce the size of the retractable mechanism. For the actual tomato picking action, a three-finger twist picking end- effector was used, and a multi-position wrist joint was added to achieve multi-position multi- directional twist picking. The picking control system was based on ROS-integrated picking, planning, and other strategies to control the robotic arm to complete the picking function. Based on the movable space of tomato greenhouses under the planting mode of wide trench and narrow ridge, a four-rotation and four-wheel-drive mobile chassis was designed, which can realize the movement and steering between tomato planting rows. Finally, a prototype tomato-picking robot was developed, and a field picking test was carried out in a greenhouse, and the fruit-picking success rate reached more than 85%, and the picking cycle time was13.4s, which had a high picking operation efficiency and picking success rate, and met the requirements of tomato picking in greenhouses.

Abstract:According to different planting modes, strawberries can be divided into two types：ridge planting and elevated planting. Compared with elevated planting, ridge planting had lower costs and occupied a larger proportion in China. To adapt to the agricultural practices of planting strawberries in the field, strawberry picking in the field was achieved, and problems such as labor shortage and rising costs, a dual-arm strawberry picking robot suitabT for the field planting mode. This robot can travel between strawberry ridges and automatically recognize mature strawberries to complete picking and collection. The design used the Arduino Nano V3.0 development board as the main controller which was developed based on Ubuntu 20.04. With the NVIDIA edge computing platform Jetson Xavier NX as the core, the mobile platform of the robot usesd a four-wheel steering chassis with high clearance, the real sense L515 as the recognition device for mature strawberries, the target detection frame and key point information of strawberry fruits through YOLO v8-Pose network was obtained, and the acquisition of strawberry handle posture and the positioning of picking points in combination with key points and point cloud processing. Two sets of robotic arms were installed with integrated end effectors for cutting and clamping strawberry stalks. The entire picking system was driven by the Arduino Nano V3.0 development board, and both sides of the robotic arm were equipped with L515 cameras. Through the recognition and capture of the cameras, the coordinate data of the strawberry fruit was transmitted to Jetson Xavier NX through a serial bus to drive the end of the robotic arm and achieve strawberry picking. Finally, a picking experiment was conducted in a strawberry orchard on site. The experimental results showed that the success rate of picking without obstruction at the stem was 85.4%, and the success rate with partial obstruction was 75.5%. The average time for picking a strawberry was12.5 s, and the damage rate was18.5%.

Abstract:In the process of hybrid rice seed production，the staggered transplanting of male parent seedlings, as one of the crucial strategies to ensure the success of seed production, poses stringent requirements on time sensitivity and spatial accuracy. The widespread application of visual navigation brought unprecedented potential to this delicate operation process. However, challenges arise from the morphological differences of female parent seedlings at various seedlings ages, instances of missing seedlings within rows, and poor row linearity. To address these issues, an efficient and precise real-time guideline extraction method was proposed and validated through comprehensive experimentation. Firstly, a staggered transplanting dataset was created, incorporating different seedling ages to meet the needs of various rice varieties. Utilizing this dataset, the BiSeNet V2（a dual-branch segmentation network）was trained to extract the female parent row masks. The distance transformation of pixels within these masks was then used to extract the crop row centerlines, accurately representing the row positions. The nearest left and right row centerlines to the male parent area were extracted by using a segmented filtering method. The feature points of these centerlines were paired by using a rotational scanning method, and the midpoints of the paired feature points were used as the navigation line feature points. Finally，B-spline curves were employed to fit these guideline feature points, forming the final transplanting guideline. Semantic segmentation experiments demonstrated that the BiSeNet V2 achieved an average pixel accuracy, mean intersection over union（mIoU）, and inference speed of 88.73%, 57.47%, and 143.32 frames per second（f/s）, respectively. Guideline extraction experiments showed an average deviation of 4.66 pixels, a standard deviation of 2.73 pixels, and an extraction speed of 12.52 f/s. Field experiments further verified the effectiveness of the proposed method, showing an average deviation of 64.93 mm between the automatic navigation transplanting path and the manually marked optimal path, with a standard deviation of 51.96 mm and over 80% of positioning points having a deviation of less than 83.26 mm. In summary, the proposed guideline extraction method for male parent transplanting in hybrid rice seed production significantly enhanced the real-time, accuracy, and robustness of guideline extraction. This was achieved through the comprehensive preparation of the dataset, efficient segmentation of male parent rows, accurate extraction of crop row centerlines, correct pairing of feature points, and precise fitting of B-spline curves. The research result can provide a significant reference for the automatic navigation of male parent transplanting in hybrid rice seed production.

Abstract:Aiming at the problem of poor positioning accuracy of intelligent equipment, such as weak satellite navigation signal and phase locking caused by tree occlusion in Panax notoginseng planting area, a global navigation satellite system（GNSS）positioning algorithm for the shading environment of Panax notoginseng combine harvester based on pseudorange and Doppler double-difference positioning algorithm was proposed. Firstly, based on the difference of the influencing factors of pseudorange measurement and Doppler frequency shift measurement, the pseudorange double difference and Doppler frequency shift double difference were taken as inputs, and the carrier irritability ratio was used as the weight, and the measured values were fused through Kalman filter, so as to reduce the estimation error and correct the pseudorange and Doppler frequency shift measurement. Secondly, the Bayesian information criterion was used to select the regularization parameters, and the reweighted least squares problem was solved by Lasso regression to achieve the sparsity of the model and obtain the improved positioning results. Finally, the u-blox ZED-F9P high- precision GNSS receiver was used to collect the messages in RINEX format. Under three working conditions：open environment, shade shelter and tree shade shielding, the positioning accuracy test of the real vehicle was carried out. Compared with the traditional pseudo-distance positioning algorithm, open environment, shade shelter environment, and tree shade shade environment the position error was reduced by 13.43%, 56.08% and 46.35%, respectively, and the root mean square error of the positioning deviation was reduced by 75.64%, 62.31% and 50.21%, respectively. Under dynamic conditions, the positioning error was reduced by 36.97%, 52.14% and 62.37%, respectively, and the root mean square error of positioning deviation was reduced by 45.34%, 60.24% and 65.81%, respectively. The proposed method effectively reduced the positioning error caused by GNSS satellite signal difference and phase lock, and effectively improved the positioning accuracy and positioning credibility, The research result can provide theoretical and technical support for the problem of poor positioning accuracy of intelligent equipment due to tree shading in hilly and mountainous areas.

Abstract:The standardized and precise identification and detection of seedlings and seeds in rice fields is a prerequisite for achieving the quality detection of mechanical rice planting operations. To address the issues of complex rice field backgrounds, high machinery operation speeds, and difficulty in extracting morphological features during the research on rice planting image recognition, which resulted in low recognition accuracy rates, a lightweight quality detection method based on the improved YOLO v8s was proposed. Firstly, an image acquisition platform for operation quality detection was established through a rice planting quality detection device developed from the Inaka PZ60 type rice transplanter. Images of operation quality were captured to form the ImageSets dataset, and quality detection evaluation indicators were formulated in accordance with relevant national standards. Then by introducing the lightweight GhostNet module, the operational parameters of the network model were reduced. Simultaneously, to enhance the detection performance of the convolutional neural network, the CPCA attention module was incorporated into the detection algorithm, effectively strengthening the feature extraction for the quality of rice planting operations, suppressing the complex background information of the rice field, accurately obtaining the key features of the operation images, and significantly improving the detection effect of numerous small targets such as seedlings and seeds. Secondly, the CIoU loss function in the YOLO v8s model was replaced with the EIoU loss function, enabling the model to have a fast and good convergence speed and localization effect, and achieving precise identification of operation quality. The experimental results indicated that when evaluated using the average precision as the main indicator, the average precision of the improved YOLO v8s model on the test set was 92.41%, with an accuracy of 92.11%, a recall of 92.04%, and an mAP improvement of 7.91, 7.71, 4.28, and 1.03 percentage points, respectively, compared with the YOLO v5s, YOLO v7, YOLO v8s, and Faster R-CNN network models. The detection speed and memory occupancy of the improved model were 88 f/s and 19.2 MB, respectively, which were12.8% and10.7% lower than those of the YOLO v8s model. After tests in the planting environment, it can determine whether the operation quality was qualified, fulfilling the role of quality detection. The improved YOLO v8s network model demonstrated rapid and accurate recognition capabilities for the quality detection of rice field operations, exhibited good robustness, and had remarkable effects in the aspect of rice planting quality detection, providing a detection method for the quality detection of mechanical rice planting.

Abstract:The precise online classification and identification of wheat maturity stages will offer valuable support for the intelligent control of combine harvesters. An online classification method was proposed for wheat maturity stages that combined vehicle-mounted cameras with deep learning techniques. By using real-time images captured by vehicle-mounted cameras, along with additional images from drones, a dataset of 4400 images was constructed, which included various wheat maturity stages, including milk ripening-early wax ripening stage, late wax ripening-early full ripening stage, late full ripening-dry ripening stage and harvested area. To address challenges such as complex harvesting environments and blurry wheat images, the MobileNetV2 was employed as the foundational network structure. Additionally, a convolutional block attention module（CBAM）was incorporated after feature extraction to enhance the adaptive capability of image feature extraction. To assess the credibility of the model, visualization techniques were employed to examine the areas of interest identified by the model in the images. The performance of the MobileNetV2 - CBAM model was compared with other classification models. Results indicated that the MobileNetV2 - CBAM model achieved a classification accuracy of 99.5% on the test set, which was 0.7 percentage points higher than that of MobileNetV2. When compared with ResNet and Swin Transformer models, the MobileNetV2 - CBAM model demonstrated similar classification accuracy but with a significantly smaller model memory usage（8.73 MB）—only 1/8 and1/11 of the memory usage of ResNet and Swin Transformer, respectively. Field experiments further validated the model’s practical application：at vehicle speeds of 4 km/h to 6 km/h, the system recognized an image every second with a maturity classification accuracy of 96.8%, meeting the accuracy and real-time requirements for online wheat maturity classification in mechanical harvesting scenarios.

Abstract:Aiming at the problem of high density of rice planting and large amount of straw in the black soil rice area of Northeast China, the uneven straw spreading during the return process affects the subsequent soil preparation and rice transplanting. An active centrifugal rice straw spreading device installed on a combined harvester was designed. By establishing the kinematic model and theoretical analysis of the three processes of deflector guide straw dropping plate, the spreading plate centrifugal spreading straw, and straw spreading in the air, the key components such as the deflector unit and the centrifugal spreading unit were designed. EDEM discrete element was used to conduct a single-factor simulation test to clarify the influence of speed of spreading disc, the declination angle of fan blade, inclination angle of deflector and returning rate on the variation coefficient of lateral uniformity of the spreading, and further narrow down the range of paraments. Taking the variation coefficient of lateral uniformity and the spreading width as the test indicators, the Box-Behnken parameter optimization and verification test was carried out by using the active centrifugal spreading test bench, and the optimization results were verified in the field. At the same time, the results were compared with a guide straw spreading device. The bench test results showed that the variation coefficient of the lateral uniformity of the spreading was 16.98%, the spreading width was 4.56 m when the speed of the spreading disc was 255 r/min, the deflection angle of the spreading fan blade was 7°, the inclination angle of the deflector was 35°, and the straw returning rate was 3.5 kg/s. The error with the predicted value was less than5%, meeting the design requirements. Through the performance comparison experiment with the original diversion straw spreading device of a combined harvester, the variation coefficient of lateral uniformity was reduced by 16.74 %, and the spreading width was increased by 0.42 m, which concluds that the active centrifugal rice straw spreading device developed had better spreading effect.

Abstract:In response to the interference of various complex environments in images, this paper proposes a straw target segmentation model based on white balance feature enhancement, taking into account the color advantage of straw on black soil in Northeast China. The DLv3+/CPM/SEM model adopts an encoder decoder structure, which integrates the color perception module CPM and spatial enhancement module SEM on the basis of the DLv3+ model. The white balance technology is used to improve the contrast of straw targets in the image, so that they can still maintain the accuracy of straw target detection under the influence of various interference factors. The encoding part utilized a residual network to form a dual branch feature extraction structure, which enhances the color features of straw through total reflection algorithm while eliminating the interference of light conditions on the color display of the image. The dual branch features are merged into the color perception module CPM through a cascaded perception method to enhance the color features of straw with severe color cast in the image at multiple levels in the form of reinforced complementary colors, thereby extracting accurate straw feature expressions. The decoding part incorporates the integrated features into the decoding model with ASPP, and adds a spatial enhancement module SEM to improve the discrimination between straw and farmland background, optimizing the performance of the straw target segmentation model. Through experimental verification, the improved DLv3+/CPM/SEM model proposed in this paper has higher accuracy and overall evaluation indicators of MloU than other comparative model models. lt has good segmentation effects under different light source conditions, straw length, ridge depth, and soil block size interference conditions. At the same time, combined with the distance segmentation results, the coverage calculation accuracy of straw monitoring images with nonsingle farmland backgrounds is more accurate.

Abstract:The field crop canopy contains a wealth of information that can provide important data for the study of crop phenotype and crop row perception methods. At present, the information collection process usually faces problems such as uneven crop height change and large differences in crop performance in different growth periods, which causes great difficulties to the adaptability and operation efficiency of ground acquisition equipment. Therefore, a distributed electric wheeled platform was designed with adjustable wheel base and ground clearance, manual remote control and automatic navigation dual-mode control. Firstly, the agronomic traits of typical field crops were analyzed, the boundary parameters of the platform structure were determined, and the whole structure design was carried out. The chassis control system was developed, a motion control system with STM32F103 chip as the core controller was established, which can support multiple steering modes such as front-wheel Ackerman steering, four-wheel Ackerman steering and in-place steering. A navigation control system with Xavier as the decision controller and GNSS/INS integrated navigation and positioning was constructed. The chassis motion performance test showed that the straight-line alignment performance of the built platform was stable and the steering was flexible, and the average lateral deviation was 0.019 m, the standard deviation was 0.017 m, the average heading deviation was 1.67 ° and standard deviation was 1.03 °. For the steering performance test, the average steering accuracy of the front wheel Ackerman was 96.57%, the average steering accuracy of the four- wheel Ackerman was 96.64%, and the average deviation of in-situ steering was 0.034 m. Taking maize as a representative crop, the crop canopy image acquisition experiment was carried out to verify the effectiveness of the built platform, and the results showed that the built platform could automatically track the planned route for row driving, ground head reversal, and cross-row alignment, and complete the non-destructive collection of canopy images. The average lateral deviation of the opposite driving was not more than 0.052 m, the standard deviation was not more than 0.029 m, the average value of the course deviation was not more than 5.653 °, and the standard deviation was not more than 3.843 °.

Abstract:In order to solve the problems of poor stability, low efficiency and poor driving safety and comfort of agricultural machinery operating in complex terrain such as large slope and small plot, a design scheme of automatic leveling system for the chassis of small agricultural machinery in hilly and mountainous areas was put forward, which integrated the driving system, control system and leveling actuator. A self-balancing three-point leveling system was designed. The front leveling mechanism used passive damping technology to deal with high-frequency disturbance, and the rear used double guide post servo cylinder to ensure the accuracy and stability of motion trajectory. The control system based on single chip microcomputer was developed, and the electronic control technology based on servo cylinder was used to realize the automatic adjustment of the tilting angle of the working chassis, so as to improve the stability and working efficiency of the agricultural machinery in the hilly and mountainous terrain. In order to meet the requirements of load bearing and stability of agricultural machinery in complex terrain, the leveling strategy of "fixed set point leveling method" was adopted and its movement process was analyzed. Finally, the static test verified that the system can adjust the tilt state of different initial pitch angle and roll angle to the set state of-0.2°~ 0.2 °. The dynamic test verified that the chassis tilt angle can be adjusted to-1.2° ~1.2 ° and the standard deviation was about 0.8 ° in actual work, reaching the design expectation.

Abstract:To enhance the stability and safety of the self-propelled wide-span operation platform during its walking operation, an adaptive omnidirectional leveling system based on a four-point hydraulic active suspension was designed, using the self-propelled platform as the research subject. The system employs LUDV load-sensing technology to improve hydraulic control performance and achieve synchronous control under multi-load parallel conditions in the four-point suspension hydraulic system. A multi-sensor setup was used to detect the platform′s posture in real-time, and through the integration of "following leveling" and "anti-false leg" control strategies, a dual-loop PID algorithm with anti-saturation integration was employed to compute and output control signals. These signals were cross-validated with the results from the suspension cylinder protection logic and anti- false leg logic algorithms to realize real-time omnidirectional posture adjustment of the platform by controlling the suspension cylinders. To validate the effectiveness of the LUDV load-sensing technology in the four-point hydraulic active suspension, a simulation model of the suspension system was developed in AMESim, followed by experimental testing. The test results indicated that under varying loads with identical openings, the maximum stroke deviation among the cylinders was 19.51 mm, with a maximum deviation rate of 6.27%. Furthermore, to demonstrate that the flow rate of each actuator was independent of load size, tests under load ratios of 1：1.35：1.71：2.07 with proportional control signals showed a motion stroke ratio deviation of 1：1.35：1.71：1.92, confirming both the independence of flow from load size and good synchronization. This verified the effectiveness of the LUDV load-sensing technology for the four-point hydraulic active suspension. In real vehicle tests, the static test results showed that the system could converge the vehicle′s body tilt angle within 0.5 °. Dynamic tests revealed that the adaptive leveling system reduced the maximum body tilt angle by 58.0% during lateral movements and 55.4% during longitudinal movements, while preventing the occurrence of false leg phenomena, effectively improving the platform’s stability and safety during operation.

Abstract:Aiming at the practical problems of large soil clods, uneven surface and uneven mixing of straw and soil in seed bed after operation in autumn, 1ZLZ-300 fullwidth compound seed bed preparation machine with the function of seed bed land leveling and straw stubble combing was designed. According to the technical requirements of spring land preparation and the characteristics of viscous soil, the overall design scheme of seed bed land preparation machine was determined. The self-excited soil horizontal shear unit and the elastic carding unit were studied respectively. By means of EDEM discrete element simulation technology, a simulation model of straw mixed buried soil in northeast black soil area was established based on field measurement data and related literature. The working mechanism of each embedded component and its effect on soil disturbance were obtained through the simulation of the operation process of seed bed preparation machine. The Box - Behnken orthogonal test was carried out with the depth of the shovel, the depth of the spring tooth and the angle of the spring tooth as the test factors, and the straw coverage rate of the surface and the surface flatness of the soil as the evaluation indexes. The optimum working parameters of 1ZLZ-300 fullwidth compound seed bed preparation machine at the working speed of 10 km/h were determined to be the depth of 10.0 cm, the depth of 8.0 cm and the angle of 0 ° of the spring teeth. Under the above conditions, the average straw coverage rate was 50.7%, the average surface flatness of the soil was 3.2 cm, the average soil compactness of 0~10 cm depth seed bed was 73.3 kPa, the average soil compactness of 10~20 cm depth seed bed was 690.2 kPa, the average soil breaking rate was 89.7%. The test results showed that all the performance indexes of the machine met the technical requirements of spring land preparation in overlying tillage. The development of1ZLZ-300 full width compound seed bed preparation machine provided the equipment support for the popularization of black soil conservation tillage corn straw mulching and mixed returning technology in central and eastern Jilin Province.

Abstract:To improve the mechanized improvement effect of compacted Leymus chinensis grassland, a mechanized improvement process for compacted Leymus chinensis grassland was proposed and a grassland root-cutting and soil loosening compactor was invented. By exploring the physical structure characteristics of the root-soil composite layer of Leymus chinensis grassland and the analysis of the integration of agronomy and agricultural machinery, a technology for the improvement of the root-cutting and soil-loosening of Leymus chinensis grassland was proposed. An oblique-handled root-cutting knife, a folding- wing loosening shovel, and a V-shaped compacting roller specially used for the improvement of compacted grassland were designed. A grassland root-cutting, loosening and compacting machine was integrated and created, and its operating performance was verified through grassland tests. The test results showed that the firmness of grassland soil after loosening operation and root-cutting and loosening operation were decreased by 59.99% and 59.77% respectively, the bulk density was decreased by 23.62% and 22.99% respectively, and the porosity was increased by 20.71% and 20.16% respectively, and the groove gap width, soil uplift height, ridge contour area, soil looseness, tillage rate and surface flatness after root-cutting and loosening operations were all smaller than those after loosening operations. The operating depth stability coefficients of loosening operation and root-cutting and loosening operation were no less than 95.17%, but at the same operating speed, the operating depth stability coefficient of root-cutting and loosening operations was always higher than that of loosening operations, and root-cutting and loosening operations were closer to the expected operating depth（15 cm）than loosening operations. The suppression operation can fill the groove gap generated by the root-cutting and loosening operation appropriately.

Abstract:Numerical simulations utilizing the discrete element method（DEM）represent a robust approach for investigating the dynamic properties and displacement behaviors of subsoil seeds during the process of post-sowing suppression. The parameters associated with the soil-seed discrete element model played a crucial role in determining the accuracy of the simulation outcomes. However, the wet clay prevalent in the middle and lower reaches of the Yangtze River exhibited elevated water content and significant viscosity. Consequently, conventional soil parameters failed to adequately represent the physical characteristics of this wet clay, resulting in a lack of precise parameters for the discrete element model of wet clay-wheat interactions. The contact parameters of the wet clay-wheat interaction model during post-sowing suppression were calibrated. Initially, based on physical tests and significance analysis, the initial range of parameter values was determined, followed by the identification of parameters that exerted a significant influence on the natural accumulation slope coefficient. Subsequently, a regression model was developed to establish the relationship between each key parameter and the slope coefficient by using response surface methodology. This process yielded multiple superior combinations of soil-soil and soil-wheat parameters. Thereafter, the discrepancies in the force-displacement curves between actual compression tests and simulation tests were analyzed to select the optimal parameter combination. Finally, the accuracy of the parameter model was assessed by comparing the simulated values with the actual results from quasi-static compression tests, rolling compression tests, and field tests. The results indicated that the soil-soil restitution coefficient（CR1）, soil-soil rolling friction coefficient（RF1）, and soil-soil interfacial surface energy（SE1）significantly influenced the natural accumulation slope coefficient of wet clay（CS1）. Moreover, the soil-wheat static friction coefficient（SF2）, soil- wheat rolling friction coefficient（RF2）, and soil-wheat interfacial surface energy（SE2 ）also had a significant impact on the natural accumulation slope coefficient of wet clay-wheat mixture（CS2）. The optimal parameter combination was identified as follows：CR1 was 0.12, RF1 was 0.35, SE1 was3.90 J/m2, SF2 was 0.48, RF2 was 0.29, and SE2 was1.69 J/m2. Additionally, the relative errors for each validation test under the optimized model were 7.16% for the quasi-static compression test, 10.41% for the rolling compression test, and14.42% for the field test.

Abstract:In order tosolve the problem that the operation process of the membrane hybrid winnowing machine is unstable and the numerical simulation lacks parameters which is difficult to simulate, the collision model between the rhizome of peanut seedling and the mechanical working parts was constructed through single factor and multi-factor experimental research, and the accurate determination of the recovery coefficient was realized. Based on the differences in mechanical properties of peanut seedling rhizomes, the collision material, collision angle, falling height and moisture content were selected as the test factors, the influence of the test factors on the recovery coefficient of peanut seedling roots and stems was studied, the regression model between the test factors and the recovery coefficient was established, and the regression analysis of the test factors was carried out. The results of single factor test showed that the recovery coefficient of peanut seedling roots was greater than that of peanut seedling stems, and the recovery coefficients between peanut seedling roots and stems and No.45 steel, acrylic plate, peanut seedlings, rubber sheet and plastic film decreased in turn, and the deviation of the recovery coefficient was decreased with an average deviation of 0.068 4. The average deviation was 0.092 6, the average deviation of the recovery coefficient fluctuated up and down with the increase of falling height, and the average deviation was 0.087 8, and the average deviation of the recovery coefficient was decreased sharply with the increase of the moisture content, the moisture content reached 40%, and the deviation of the recovery coefficient was decreased sharply, with an average deviation of 0.082 7, and the determination coefficient of the regression equation was greater than 0.97. The orthogonal test results showed that the order of the factors affecting the recovery coefficient was as follows：collision material, collision angle, falling height and moisture content. The results of the verification test showed that the average relative errors of peanut seedling roots and stems were 3.928% and 4.146%, respectively. The rsearch result can provide a reference for the setting of numerical simulation parameters of seedling film separation, membrane miscellaneous winnowing machine and other equipment.

Abstract:Existing wheat wide seedling belt seeding is mainly based on the traditional grooved wheel seed metering with the seed leveling device at the end of seed guide tube, there are problems of seed flow disorder, unstable displacement, and seed supply pulse, which affect sowing quality. A pneumatic cone-disc type precision seed metering device for wheat wide seed belt was designed based on the principles of negative pressure seed suction and self-weight seeding. A mechanical model of wheat seed filling was constructed under the action of a cone-disc, and the structural parameters of the main components of the cone disc seeding device were determined. Mechanical analysis showed that the critical negative pressure value required for seed filling and carrying was related to the taper of the seeding disk, seed mass, triaxial size, hole size, working speed and hole graduation circle diameter. EDEM simulation was used to comparatively analyze the impact of different tapers of the cone-disk on the dispersion of wheat populations, and analyze the movement patterns of seeds in the driving layer, stationary layer and drag layer populations. The results showed that when the taper of the seeding disk was 45 °, it can take into account the advantages of good population disturbance and low migration resistance of adsorbed seeds. For different types of holes in the cone disk, seeding performance tests were carried out by using the coefficient of variation of the consistency of the seeding amount in each row, the coefficient of variation of the stability of the total seeding amount, and the coefficient of variation of the seeding uniformity as evaluation indicators. The test results showed that the evaluation of vertical holes with a size of 1.8 mm was better than that of oval holes. The impact of the working parameters of the seed metering device such as seed suction negative pressure and working speed on the seeding performance was explored. The results showed that the seeding amount showed a linear increase trend with the increase of negative pressure and speed. The working speed was 30~40 r/min. When the pore adsorption negative pressure was 5.2~5.6 kPa, the seeding rate can reach 220~290 g/min. the best combination of working parameters was obtained by orthogonal test as working speed of 30 r/min, negative pressure of adsorption of 5.4 kPa, and the evaluation indexes of each test were 1.93%, 1.26% and 9.52%, respectively. Field comparison tests showed that the sowing uniformity of the wide seedling belt of the pneumatic cone-disc seed metering device was improved compared with the wide seedling belt of the outer grooved wheel seed metering device, and can meet the requirements for sowing small amounts of wheat concentrate.

Abstract:To address the installation challenges of conventional photoelectric sensors in hill - drop planters widely used in Xinjiang, which lack seed guiding tubes, a seeding sensor was explored based on weak capacitive signal acquisition and analysis. By analyzing the seed extraction process within the seeder, the optimal detection position and external dimensions of the electrode plates were established. The primary objectives were to enhance signal significance and detection accuracy while ensuring reliability as a critical boundary constraint. To optimize the electrode structure parameters, a response surface methodology was employed, utilizing three factors at three levels. Additionally, the sensitive unit of the sensor was shielded to concentrate the electric field. Following enhancement, the thickness of the sensor ’s sensitive unit was determined to be 0.64 mm, and a capacitance variation of 0.16 pF, effectively enhancing signal significance and detection accuracy. A capacitive signal acquisition module, centered around the AD7745 capacitive conversion chip, was designed. The upper computer processed the collected signals using adaptive threshold orthogonal wavelet transform filtering and employed the second derivative peak detection to obtain real-time seed sowing rates. Experimental results indicated that with a sampling period of 11 ms and a seeder rotation speed of 20~25 r/min, the sensor achieved high detection accuracy, with relative errors between the actual and measured sowing rates ranged from -2.604% to 1.836%, which were all less than 5%. The designed seeding sensor provided an effective solution to the sowing detection challenges associated with disc-type seeding equipment and played a significant role in advancing precision sowing technology.

Abstract:At present, the planting and production process of Polygonatum sibiricum is still dominated by manual operations, especially the manual operation mode of transplanting Polygonatum sibiricum has problems such as high work intensity, low efficiency, poor pass rate, and high seedling damage rate. In order to improve the mechanization level of transplanting of Polygonatum sibiricum, a transverse staggered transplanting machine was designed. The transplanting machine was mainly composed of conveying components, transmission electronic control components, ditching components, covering components, suppression components, ground wheel components, underframes, etc. Based on the agronomic requirements and morphological and mechanical characteristics parameters of transplanting root seedlings of Polygonatum sibiricum tuber, the conveying mode was designed as flexible belt conveying, and the structural parameters such as conveyor belt, seedling trough and furrow opener and the layout form of seedling trough were determined. The results of discrete element simulation showed that the combination of ditching depth and forward speed affected the effect and efficiency of transplanting operation, and the optimal operation parameter interval of response surface optimization was as follows：trenching depth was 85~110 mm, and forward speed was 0.3~0.5 km/h. The experimental prototype was designed and verified, and the average seedling injury rate was 0.52%, the average bud head orientation pass rate was 88.6%, the average plant spacing was（10.99±1.75）cm, the plant spacing variation coefficient was 15.93%, the average transplanting depth was（9.525±0.48）cm, and the transplanting depth variation coefficient was 5.04%. The test results showed that the structure design of the whole machine was reasonable, the operation effect was reliable and stable, and it met the agronomic requirements of transplanting root seedlings of Polygonatum sibiricum tuber.

Abstract:To improve seedling pickup performance，an elastic penetration-type seedling claw was designed. The mechanical properties of seedling pots were experimentally measured，and the Hertz - Mindlin with JKR model in EDEM，using 65 Mn material，was selected for the simulation. The rolling distance served as the response value to calibrate contact parameters between seedling pot particles and the material. Dynamic modeling of the seedling claw was completed in RecurDyn，and coupled EDEM -RecurDyn simulations were conducted to investigate the effects of penetration depth and speed on seedling pickup. The analysis indicated that a penetration depth of 33 mm and a speed of 150 mm/s resulted in optimal pickup performance. A test bench was then constructed to validate these findings，confirming the simulation results with a 100% success rate in seedling pickup and less than 5% damage to the seedling pot matrix，demonstrating excellent effectiveness.

Abstract:In response to the demand for precise conveying and positioning for seedling trays in top-clamping seedling-taking devices, an accurate positioning control method based on fuzzy PID and dual sensors was proposed, which obtained the position information of seedling trays by a laser sensor and got corresponding angle information by an angle sensor. Moreover, a fuzzy PID control model based on a two-phase hybrid stepper motor was established to convey the seedling trays accurately. Taking the standard 128-cell seedling tray as the conveying object, the positioning accuracy of the seedling tray was analyzed, which showed that the positioning error of the seedling tray should be less than 2.13 mm, corresponding to the angle control error of less than 2.03 °. Subsequently, the control system for seedling tray positioning was analyzed and established based on the working principle of the top-clamping seedling-taking device. The simulation results showed that under the optimal PID parameters, KP=40, KI=76, KD =3.2, the adjustment time of fuzzy PID control was 0.18 s, the recovery time after disturbance was 0.31 s, and the maximum response variation was 0.94 °, which was less than 2.03 °. The fuzzy PID had a better dynamic and steady-state performance than the classical PID, meeting the control requirements. The positioning control results showed that the fuzzy PID achieved an average positioning error of 0.32 mm, an average relative positioning error of 0.92%, and a maximum positioning error of 0.53 mm, which was less than 2.13 mm. This method can meet the requirements for precise positioning of seedling tray conveying, enhancing the system′s anti- interference capability and providing a reference for the key technology upgrade of automatic vegetable transplanters.

Abstract:In order to solve the problems caused by large soil disturbance, low return flow, poor hole size regularity, poor hole forming effect, high dew rate and low upright degree, a multi-leaf duck bill planting mechanism was designed based on duck bill shape. The composition and working principle of the whole structure were described, and the structure and working parameters of the key components were preliminarily designed through theoretical analysis and the establishment of the mathematical model of motion. The factors affecting the soil cavitation effect were analyzed. With the number of duckbill leaves, the opening and closing angle of duckbill, and the opening and closing speed of duck bill as test factors, and the soil disturbance amount, soil return flow, and the regularity of the cavity size as evaluation indexes, a three-factor and three-level orthogonal combination experiment was designed. A regression mathematical model of the evaluation indexes was established by using Design-Expert software. The response surface of the test results was compared, and the significance of the three factors on the evaluation index was compared. According to the multi-objective parameter optimization of the regression model, when the number of duck bill leaves was4, the opening and closing angle of duckbill was 22 °, and the opening and closing speed of duckbill was 70 r/min, the regularity of soil disturbance amount, soil return flow rate and hole size were 0.559, 0.788 and 7.136, respectively, the soil cavitation effect was the best at this time. According to the optimal test results, the design parameters of the key components were optimized and determined, and the field planting experiment was carried out. The results showed that the average error of evaluation index between planting operation test and coupling simulation test was 2.2%, 2.3% and1.8%, respectively, which met the requirement of error between simulation test and actual test. At the same time, the dew rate and orthostatic qualification rate after planting operation were evaluated, the dew rate was 3.6%, and the orthostatic qualification rate was 96.7%, which met the requirements of the machinery industry standard JB/T 10291—2013《Dryland Planting Machinery》 with the dew rate not higher than 5% and orthostatic degree not less than 93%. The correctness of the design and theoretical analysis of the multi-leaf duckbill planting mechanism was verified.

Abstract:Floating tray seedling cultivation enables the delivery of water, nutrients, and pesticides through the bottom holes of the trays, offering advantages such as easy management and low production costs, and it has been widely adopted in the southwestern regions of China. A clamp-type elastic seedling picking gripper specifically for floating-tray seedlings was designed. Based on ejection and compression tests of the seedling pots, the mechanical properties of Guofu910 pepper seedling pots were determined. Using ADAMS rigid-flexible coupling simulation experiments, the trajectory of the gripper tip entering the pot was established, validating the rationality of the seedling extraction gripper. A mechanical model of the seedling extraction process was developed, providing a comprehensive analysis of the key factors affecting the success rate of seedling extraction. An orthogonal experiment was conducted by using a single seedling extraction gripper for pepper seedlings, with factors included pot moisture content, gripper width, and extraction depth, to determine the optimal combination of levels for seedling extraction. The results showed that at an extraction depth of 40 mm, gripper width of 5 mm, and substrate moisture content of 55%~65%, the seedling extraction success rate reached 99%, with a substrate loss rate of 3.18%. In adaptability tests across different crops, the extraction success rates for Chama cabbage and tobacco seedlings K326 were no less than 98%, while the success rate for Yunyan 87 tobacco seedlings was 94%, demonstrating good adaptability. This seeding picking gripper demonstrates universally excellent performance, offering raluable reference for row-wise seeding picking in vegetable transplanting machines.

Abstract:Addressing the relatively slow progress in research on mechanized transplanting technology for Panax notoginseng seedlings in China, as well as the difficulty of traditional automatic transplanting devices in achieving precise posture adjustment and orientation for these seedlings, a Panax notoginseng seedling oriented transplanting device based on the moment imbalance effect is proposed. High speed photography is utilized to analyze the changes in seedling posture under different release attitudes. Experimental results demonstrate that, under the influence of the moment imbalance effect, the cutting edge of the seedlings tends to face downwards. A force analysis of the seedling′s falling process is conducted, revealing the influence pattern of the moment imbalance effect on the seedlings′ posture changes. Through theoretical calculations and simulation analyses, the key components of the orientation device are designed, and a dynamic model of the seedling posture adjustment and orientation process is established, uncovering the mechanism of seedling orientation. To optimize the performance of the orientation device, single factor experiments and Box- Behnken experiments are conducted, with conveying speed, horizontal spacing, and vertical height as the experimental factors, and the orientation qualification index as the experimental indicator. The experimental results indicate that the primary and secondary order of influence on the orientation qualification index is conveying speed, horizontal spacing, and vertical height. When the conveying speed ranges from 90.564 mm/s to 110.468 mm/s, the horizontal spacing was between 24.931 mm and 27.701 mm, and the vertical height was 8.5 mm, the orientation qualification index exceeds 85%. Parameter optimization is then carried out, and the optimized results meet the requirements for Panax notoginseng seedling transplanting.

Abstract:To address the issues of low detection accuracy and speed in apple tree trunk recognition, this paper proposes a precise apple tree trunk recognition method based on an improved YOLO v8 model. First, a depth-sensing camera is used to capture images of apple tree trunks, and YOLO v8 is adopted as the baseline model. The convolutional layers are replaced with re-parameterized convolution structures to enhance the model′s feature learning capability. Second, the feature fusion unit is optimized by introducing a dynamic head detection mechanism, which improves both detection speed and accuracy. Finally, field experiments were conducted using traditional YOLO v8, Fast R-CNN, and other models as baselines, with average recognition accuracy and frame rate as evaluation metrics. The results show that the improved model is capable of accurately recognizing apple tree trunks, achieving an average recognition accuracy of 95.07% and a detection speed of 112.53 f/s, and the model parameters amount to 4.512×107. Compared with the traditional YOLO v8 model, the average recognition accuracy increased by4.98 percentage points, and detection speed increased by 3.24 f/s. Compared with mainstream object detection models such as Fast R-CNN, YOLO v7, YOLO v5, and YOLO v3, the improved model outperformed them in average recognition accuracy by 15.26, 6.33, 9.59, and 13.41 percentage points, respectively, and in detection speed by 96.81, 75.27, 2.23, and 57.10 f/s, respectively. Additionally, the model′s parameter count was reduced by 9.198× 107, 1.93×106, and 1.641×107 compared to Fast R-CNN, YOLO v5, and YOLO v3, respectively. This research provides technical and methodological support for autonomous navigation and intelligent operations in apple orchards.

Abstract:Aiming at solving the the problems of excessive time consumption and low registration efficiency caused by the4PCS algorithm when registrating the point cloud data, a improved4PCS coarse registration method based on the3D-SIFT feature point was proposed. The point cloud data of the cherry tree was collected from four directions by DK depth camera. Firstly, a point cloud denoising framework was designed by using traight-through filtering and statistical filtering to screen high-quality three-dimensional point cloud. Secondly, the SIFT algorithm was applied to extract features from cherry tree point cloud, which reduced data dimensions and enhanced feature stability. Thirdly, the obtained set of points about source feature and target feature were used as initial data of the 4PCS algorithm, and the coarse registration was carried out. Finally, after obtaining the precise pose, the ICP algorithm was used for precision registration until the best matching state was achieved. Taking cherry tree point cloud data of different types as the experimental objects to registration experiments, the time consuming and the root maen square error indexes were introduced to evaluate the experiments. In the coarse registration stage, the results showed that the registration time of the proposed registration method was 4.16 s and 4.33 s, respectively. The root mean square error was 0.953 cm and 1.810 cm, respectively, which effectively reduced the registration error and shortened the registration time. The results of multiple precision registration experiments demonstrated that both the overall point cloud registration time and registration error achieved optimal values based on the fusion of the proposed method and the ICP algorithm in the precision registration. The whole registration time was 4.84 s and the root mean square error was 0.845 cm.

Abstract:In response to the low accuracy of cherry tree image segmentation and diameter calculation in orchard environments, a dormant cherry branch diameter calculation method was proposed based on improved UNet. Firstly, the main trunk and branches of cherry trees were classified and grided to increase the training capacity of UNet on branch data. Secondly, VGG16 with strong universality was selected to replace the backbone feature extraction network of UNet, and a SAM module was added after the pooling layer to overcome the influence of complex backgrounds and branch structures. Again, using a weighted cross entropy loss function, assigning different weights to various targets to solve the problem of imbalanced pixel categories. Finally, the maximum inscribed circle was generated in the branch mask image obtained by UNet, and the actual diameter of the branch was calculated based on the maximum inscribed circle diameter. The experimental results showed that the improved UNet model achieved an MPA and MIoU of 85.79% and 77.97% for detecting dormant cherry trees, respectively, which were 0.52 percent points and 4.49 percent points higher than that of the original UNet model. Linear regression analysis was conducted between the described method and the field measurement method, and the determination coefficients of the branch diameter calculation results were all no less than 0.915 7, with root mean square errors no more than 0.86 mm. This indicated that the method proposed can accurately segment cherry tree branch images, calculate branch diameters, and provide effective technical support for automated pruning of cherry trees.

Abstract:In order to achieve the point cloud acquisition of cucumber plants in greenhouse tall crops, a dense map building algorithm was proposed. The algorithm was based on the ORB-SLAM3 algorithm architecture. Firstly, by improving the extraction process of feature points, the quadtree extraction method was used to make the distribution of feature points more uniform and improve the quality of key points. Secondly, it added dense map building thread, octree map thread and raster map thread. The dense mapping thread usually recovered single-frame point clouds and combined statistical filtering and voxel filtering, and then transferred the cucumber point clouds from the camera coordinate system to the world coordinate system for alignment and fusion according to the camera poses on both sides of the cucumber plants. Compared with the traditional rotary multi-view alignment method, it solved the problem of missing alignment information of the point clouds on both sides of the ridge, and successfully achieved the automatic alignment and fusion of the point clouds on both sides of the ridge, and finally obtained a high-accuracy greenhouse point cloud. The algorithm solved the problem of missing information in the point cloud on both sides of the ridge, and successfully achieved the automatic alignment of cucumber point clouds on both sides of the ridge. In order to verify the practicality, the TUM dataset and the real scene were tested, and the results showed that the enhanced ORB-SLAM3 algorithm was more accurate in running trajectory, and its absolute error was reduced by 21.4% on average. The research achieved three-dimensional point cloud acquisition of tall fescue crops and provided basic data for the subsequent analysis of phenotypic data.

Abstract:In order to solve the problems of low accuracy, slow processing speed, easy to be disturbed by the background environment and difficult to detect target diseases of the existing wheat disease detection algorithms, a wheat disease detection system based on cloud architecture was designed by combining advanced smart phone hardware, convenient WeChat mini program application and efficient cloud service platform. The system mainly included cloud server module and WeChat mini program module. The cloud server side was mainly used for image receiving and model processing. Using CSS and Java Script language to develop WeChat mini program for data upload, information feedback and information display. In order to ensure the feasibility of the model deployment in cloud server, an improved wheat disease detection model based on YOLO v8n（C2f- Faster-Slim-Neck-YOLO v8n, CS-YOLO）was proposed. Combining with FasterNet ’s advantages of lightweight, this model proposed to replace C2f Bottleneck module with FasterNet Block, which reduced the model size and improved the model ’s feature fusion ability and detection accuracy. In the Neck network, GSConv and VoV-GSCSP module in Slim-Neck design paradigm were used to improve the neck of YOLO v8n, reducing the calculation amount of the model and improving the detection accuracy of the model. The test results showed that for the wheat disease data set collected in the field environment, the floating point computation and model memory occupation of the improved model were reduced by 24.4% and 17.5% respectively compared with the baseline model of YOLO v8n, and the average accuracy was increased by 1.2 percentage points compared with the original model. It was superior to YOLO v3-tiny, YOLO v5, YOLO v6, YOLO v7, and YOLO v7-tiny algorithms. Finally, the lightweight detection model CS-YOLO was deployed on the cloud server and the detection function was transformed into an API interface. The applet called the server connection by requesting its interface. After receiving the request, the server passed the data to the model deployed on the cloud server. By using the WeChat mini program to invoke the detection model for disease image type recognition and disease location detection, the mean average precision was 89.2%, which can provide technical support for wheat disease type recognition and disease location detection.

Abstract:Hyperspectral imaging （ HSI ） is an increasingly utilized non-destructive testing technology that simultaneously captures spatial and spectral information of samples, making it suitable for characterizing the spatial distribution of material properties or quickly obtaining the properties of highly heterogeneous samples. However, due to the limitations imposed by sensor and optical material performance and cost, a single hyperspectral camera can only cover a limited spectral range, while the material property information is often distributed across different spectral bands. This limits the types and accuracy of material property monitoring when using a single camera. A push-broom dual-camera HSI system was designed and constructed. The system achieved a minimum spatial resolution of 140.31 μm and 222.72 μm in the spectral ranges of 400~1 000 nm and 1 000~2500 nm, respectively, with spectral resolutions of 2.8 nm and 12 nm, covering a total of464 working bands. A user-friendly data acquisition software, MySpec HSI, was developed by using C# and XAML to facilitate convenient dual-camera HSI data collection. To evaluate the performance of the constructed push-broom dual-camera HSI system, it was used to image the canopy of maize samples, and partial least squares regression models were established for monitoring biomass, chlorophyll, and total nitrogen content in maize canopy leaves. The R values of the biomass, chlorophyll, and total nitrogen content monitoring models based on a visible-near-infrared（VNIR） single camera were 0.567, 0.773, and 0.653, respectively, with RMSEP values of 0.52 g, 2.5, and 0.301%. For the shortwave-infrared（SWIR ）single camera, the R values were 0.566, 0.719, and 0.652, with RMSEP values of 0.53 g, 2.8, and 0.309%. Except for a slight advantage in chlorophyll monitoring by the VNIR band, the monitoring accuracy of the other properties was comparable between the two bands, indicating that either single-camera HSI can achieve biomass, chlorophyll, and nitrogen content monitoring of maize canopy leaves. However, the dual-camera model demonstrated superior performance, with R values for biomass, chlorophyll, and total nitrogen content reaching 0.670, 0.822, and 0.683, respectively, representing improvements of up to 18%, 14%, and 5% compared with that of the single-camera models. The RMSEP values were decreased to 0.46 g, 2.0, and 0.258%, respectively, showing reductions of up to 13%, 27%, and 17% compared with that of the single-camera models, indicating that integrating dual-camera HSI data effectively enhanced the accuracy of monitoring maize canopy leaf properties.

Abstract:Solanum rostratum Dunal（ SrD ）is a globally harmful invasive weed, that has spread widely in many countries, and poses a serious threat to local agriculture and ecosystem security. A deep learning network model, TrackSolunam, was designed to realize real-time detection, localization, and counting for SrD. The TrackSolanum network model consisted of three parts ：a detection module, a tracking module, and a localization and counting module. The main body of the detection module consisted of YOLO v8 with the added EMA attention mechanism, which can detect SrD plants in real time. The main body of the tracking module was based on DeepSort, which enabled multi-object tracking based on the output of the detection module. It can identify the same SrD plant in consecutive video frames, avoiding repeated identification and counting. The localization module located the plants of SrD that were detected by searching for their centroids and can output the specific coordinates of the centroids in each frame, facilitating subsequent removal processes. The counting module avoided the issue of repeated counts by specific processing that the target ID was invalid after it crossed the detection line. The YOLO_EMA model achieved precision, recall, AP and FPS of 93.7%, 93.6%, 97.8% and 91 f/s, respectively, demonstrating its effectiveness in real-time detection tasks for SrD in the field. To further validate the detection performance of the YOLO_EMA network, an ablation study comparing the original YOLO v8, YOLO_EMA and the previously designed YOLO_CBAM was conducted. Additionally, the impact of different growth stages of SrD on detection performance was discussed. During the seedling stage, the TrackSolanum model achieved precision, recall, AP, and FPS of 95.9%, 96.4%, 98.6% and 74 f/s, respectively. In the growth stage, the TrackSolanum model′s precision, recall, AP, and FPS were 96.3%, 95.4%, 97.0% and 71 f/s, respectively, all demonstrating good detection results. The field test results showed that for the video acquired by UAV flight at 2 m height, the precision and recall of the TrackSolanum model reached 94.2% and 96.5%, respectively, and the MOTA and IDF1 reached 80.6%and 95.4%, respectively, with the counting error rate of only 3.215%. The TrackSolanum model can be used for real-time detection of SrD in the field, providing crucial technical support for hazard assessment and precise management of SrD invasion.

Abstract:The greenhouse space is closed, and auxiliary pollination is needed to ensure the fruit setting rate and yield of tomatoes. Physical assisted pollination methods are green and healthy, with high fruit setting rate, and are the main pollination methods developed. Aiming at the problems of the existing physical assisted pollination methods, such as easy stem damage, large range of action and poor vibration effect, a pulse cluster pollination method was proposed and the corresponding pollination device was built. Through CFD simulation, the velocity distribution of nozzle flow field with single and double outlet number was obtained. With the range and fluid injection angle as evaluation indexes, the nozzle scheme with two outlet number and1 mm diameter of single hole was determined. Based on the gas-solid two-phase flow model, a CFD-DEM coupled simulation model was established to simulate the pollination process of tomato by observing the flowering morphology of tomato flower and analyzing its oscillation rule under the action of pulsed air flow. The orthogonal simulation of three factors and three levels was carried out with air blowing angle, air blowing frequency and air blowing distance as test factors and effective adhesion as evaluation index. The simulation results showed that the effect of various factors on the effective adhesion of pollen was in the order of blowing distance, blowing frequency and blowing angle. When the blowing distance was 182 mm, the blowing frequency was 24 Hz and the blowing angle was 76 °, the effective adhesion of pollen was the highest. At the same time, the simulation results were analyzed, and the pollination process of tomato flower under the action of pulse cluster method was obtained. The optimal parameter combination was used to test in a greenhouse. Compared with the test results, the maximum error of stigma pollen coverage was 5.54% and the average error was1.93%. Six weeks after pollination, the results of tomato fruit statistics showed that the fruit setting rate, single fruit weight and abnormal fruit rate were 82.81%, 229.60 g and 8.27%, which were better than that of other auxiliary pollination methods. The results showed that the coupling simulation model established was accurate and reliable, and it can provide a theoretical basis for the application of pulse cluster pollination method in intelligent devices. At the same time, it was verified that the pollination method can improve the fruit setting rate and yield of tomato, and meet the agronomic requirements of tomato pollination in greenhouse.

Abstract:Aiming at the complex road environment in greenhouse and the problem that greenhouse mobile robots cannot use GNSS for localization, research and experiments on greenhouse localization were carried out based on ORB-SLAM2. Firstly, the color image and depth information of greenhouse acquired by the depth camera Realsense D455 were preprocessed, and the scale and rotation invariance of ORB features was achieved by the image pyramid and grayscale center-of-mass method to complete accurate and effective feature point matching. Secondly, coarse localization was done by using tracking thread reference key frame tracking, homogeneous model tracking, and repositioning tracking, and then fine localization was done by using local map tracking to achieve an accurate solution for the camera pose. Thirdly, combining with the local map building thread, applying the common-view method to build up the map points based on the completion of the key frame insertion, the recent map point screening, the new map point screening, the new map point reconstruction, the local BA optimization, and the local key frame screening. Finally, combined with the closed-loop thread, the full map was corrected by loopback correction through the candidate loopback, computation of similarity transformation, loopback fusion, and position map optimization, so as to realize the greenhouse in the real-time localization and map building. Three greenhouses with different crop growth conditions in the early, middle and maturity stages of pepper growth were selected for real-machine testing, and the trajectories generated by the algorithm basically matched the actual trajectories, with the root-mean-square errors on the X-axis of 0.6862 m, 0.355 0 m, 0.4925 m, and the average absolute errors of 0.5883 m, 0.293 7 m, and 0.4554 m, respectively, and on the Z-axis of 0.149 7 m, 0.071 8 m, 0.3686 m, and the average absolute errors of 0.0986 m, 0.0464 m, and 0.2825 m, respectively. The experimental results showed that the method could provide technical support for the localization and navigation of greenhouse mobile robots.

Abstract:Coconut coir is a cultivation substrate that is gradually gaining widespread application;however, the dynamic changes of its internal water content under drip irrigation across temporal and spatial dimensions, especially at a fine scale, remain insufficiently studied. To address this issue, a sensor array to monitor long-term water content variations within coconut coir was employed. The water movement rate, morphology of wetted body, and spatiotemporal distribution characteristics of the wetted body’s water content were investigated. Experimental results indicated that during drip irrigation, the vertical movement rate of the wetting front significantly exceeded the horizontal movement rate. The movement distance and vertical movement rate of the wetting front exhibited a power function relationship with infiltration time. The water content within the wetted body was increased in an S-shaped curve over time, reaching a relatively stable state after 20 hours of irrigation. As infiltration time increased, the shape of the wetted body transformed from an inverted cone to a barrel shape. Post-irrigation, the internal water content of the wetted body changed in two stages：a rapid short-term decline following a power function relationship, and a medium- to long-term decline following an exponential decay function, with the wetted body gradually shrinking in a conical form.

Abstract:When using visible/near-infrared diffuse reflectance spectroscopy for the detection of soluble solids content（SSC）in apples, the distance from the spectral acquisition probe to the sample surface varies randomly and uncontrollably, resulting in a reduction of detection accuracy. Moreover, when using characteristic wavelengths to establish the prediction models, the contribution of non- characteristic wavelengths to the prediction results is often neglected, resulting in the loss of spectral information. Therefore, a distance correction（DC）method was proposed by exploring the law of the influence of detection distance on diffuse reflectance spectra and establishing prediction models for apple SSC by combining the modeling method of fusion of characteristic wavelength and non-characteristic wavelength data. The results showed that DC could more effectively improve the prediction performance of the PLSR model;the use of the competitive adaptive reweighted sampling （CARS ） algorithm for characteristic wavelength screening based on DC preprocessing could effectively simplify the model and improve the model prediction performance; and the fusion modeling results of characteristic and non-characteristic wavelength data of the CARS algorithm had the best prediction performance, with the correlation coefficient of calibration（ Rc）, root mean square error of calibration（RMSEC）, the correlation coefficient of prediction（Rp）, root mean square error of prediction（RMSEP）and relative percentage difference（RPD ）of 0.981, 0.297%, 0.957, 0.469% and 3.424, respectively.

Abstract:Aiming at 3D laser scanning to achieve irregular raw meat contour imaging, there are problems such as incomplete scanning contours, missing data, and low volume estimation accuracy. In light of these limitations, a 3D visual imaging system was presented based on the contour shaping unit. This system was designed to address the morphological characteristics of irregular raw meat, with the aim of optimizing the imaging performance of irregular raw meat. The operational methodology of the contour shaping apparatus was delineated, and the essential hardware modules, including the sample driving and transmission unit, scanning external trigger control unit, and imaging detection platform. Additionally, the relationship between the rotational orientation of the hinge bolt in the shaping apparatus, the number of motor rotations, and the desired contour angle of the raw material meat was determined. A 3D visualization software was ultimately developed on the Halcon platform by utilizing the C# language. The point cloud processing model reconstruction algorithm and gray dilation hole compensation algorithm were employed to facilitate the acquisition of information, analysis of data, and comparison of volume estimation accuracy before and after contour shaping of irregular raw meat. This was done in order to validate contour shaping to optimize the imaging performance of meat. A total of 120 pieces of chilled and frozen pork（hind shank and loin） was employed to substantiate the enhanced functionality of the shaping unit for the imaging of raw meat contours. The results demonstrated that the post-scanning imaging accuracies of the meat pieces at 90 °, 180 °, 270 ° and360 ° relative to the transmission direction were greater than 90%, and the coefficients of variation were no more than 3%. The optimal angle for shaping ranged from 30 ° to 50 ° for chilled meat and from 40 ° to 60 ° for frozen meat. The accuracy of volume estimation was improved from 90% to over 94%, and 97%, respectively. Following the shaping process, the contour of chilled and frozen meat morphology can be maintained for over 6 s, with a maximum compression ratio of hole height below 0.77. The research result demonstrated that the imaging performance of irregular raw meat can be significantly enhanced through the application of a contour shaping unit. This finding provided a valuable foundation for subsequent research and development efforts aimed at advancing quantitative slitting technology based on contour imaging for irregular raw meat.

Abstract:Through analyzing the temperature rise, radio frequency（RF）output power, and water loss during the RF drying of scallion stem under the different experimental conditions, the impacts of form of scallion stem to be heated and its parts, container material and its placement position, scallion layer height, electrode gap, and target temperature on the RF drying characteristics of scallion stem were clarified. The results could provide reasonable and efficient experimental parameters and factors’ scopes for the subsequent research of scallion stem RF drying. It was found that the temperature rise rate of chopped scallion stem was about three times that of scallion stem slice, and the drying period required for scallion core was twice that for scallion white under the same RF parameters. Therefore, scallion white and scallion core should be RF dried separately to avoid excessive drying of scallion white. Compared with containers made of polytetrafluoroethylene（PTFE） and polyethylene（PE）material, the temperature rise rate of chopped scallion stem in polypropylene （PP）container was higher. Therefore, PP container was more suitable to be used for scallion stem RF drying. Using the selected PP container, the chopped scallion white with the minimum layer height of 14 mm and mass of 650 g could achieve a fast temperature rise rate. The range of electrode gap between 77 mm and 87 mm could ensure that the scallion white of the maximum layer height of 28 mm avoided overcurrent and burnt under the minimum electrode gap, and that the scallion white of the minimum layer height of 14 mm reached the target temperature of 75 ℃ quickly under the maximum electrode gap. In addition, the scallion white with layer heights of 14 mm and 28 mm were subjected to the same RF drying at conditions of raising container 11 mm and no raise, respectively. It was found that RF power variations and water loss changing tendency under these two conditions for the scallion white with different layer heights were different. Moreover, the scallion white of 14 mm layer height with no container raise improved drying efficiency about 8% compared with that with container raise, while the scallion white of 28 mm layer height obtained the same RF drying periods under those two conditions. Therefore, the scallion white or its container without raise of 11 mm was more conducive to its RF drying research due to the different effects of container raise on the drying characteristics of scallion white with different layer heights under the same RF drying parameters.

Abstract:To explore the effects of different drying processes on the drying characteristics and quality of paddy, a self-made paddy drum hot-air drying device was used for hot-air drying experiments with drying temperature, initial moisture content, and drum speed as influencing factors, and paddy cracking rate, protein content, fatty acid value, and taste value as evaluation indicators. Single factor and orthogonal experimental methods were used to explore the effects of different factors on the drying characteristics and quality of paddy, and the optimal drying process for paddy was analyzed. The applicability of six drying mathematical models in hot-air drying was compared. The results showed that drying temperature had the greatest impact on the drying characteristics and quality of paddy, followed by initial moisture content and drum speed. As the drying temperature was increased, the drying rate of paddy was increased, and the cracking rate, protein content, fatty acid value, and taste value of paddy were increased, while the taste value was decreased. The moisture content of paddy was reduced to 18% by natural drying method, and the drying quality of paddy was the best at drying temperature of 40 ℃ and rotating speed of roller of 30 r/min. The optimal drying mathematical model was Wang and Singh model. As the drying temperature was increased, the effective diffusion coefficient of paddy moisture was also increased. When the drying temperature was increased from 40 ℃ to 60 ℃, its effective diffusion coefficient of paddy moisture was increased from 9.433×10-11 to 1.885×10-10, and the drying activation energy of paddy was 30.153 kJ.

Abstract:When the tractor tows the tillage equipment for rotary tillage and ridge forming operations, uneven terrain and inconsistent soil compaction lead to unpredictable cultivation resistance, resulting in lateral torque that affects the vehicle’s travel posture and ultimately reduces the accuracy of navigation tracking. To achieve precise autonomous navigation for rotary tillage and ridge forming in complex agricultural environments, a control method was designed based on detecting lateral torque for autonomous navigation of the rotary tillage riding tractor. Firstly, based on the three-point hitch structure of the tractor and the operating scenario, the impact of forces at the tool suspension points on the navigation vehicle was analyzed. A two-dimensional axle pin sensor was developed to build a lateral torque collection system for detecting the lateral torque generated by the soil relative to the vehicle. The experiments demonstrated that lateral torque increased the lag in lateral errors during navigation operations. Secondly, the lateral moment of force was introduced into the kinematic model of the rotary tillage ridging tractor as an external random disturbance. A separation control strategy and linear feedback mechanism were used to calculate and predict state errors in real time, leading to the design of a robust model predictive control（ RMPC ）algorithm controller. Finally, a navigation control system was developed based on the ROS framework, and the software and hardware were integrated and deployed on a rotary tillage ridging tractor. Field operation experiments were conducted in a cabbage field during a single stubble treatment. The results indicated that the average absolute value of the lateral navigation error was 0.022 m, the average absolute value of the heading error was 0.034 rad, and the average linearity of the ridges was 4.4 cm, demonstrating that the overall operation quality met crop planting requirements.

Abstract:Reduced speed and stalling in small electric tractors, caused by sudden increases in traction resistance and insufficient instantaneous power under complex environmental conditions, significantly impact operational quality and efficiency. A novel method for peak driving power compensation and gyro energy recovery was proposed based on a single gimbal control moment gyro （CMG）anti-rollover system. An energy flow and power conversion model for the tractor-CMG system was developed, incorporating the effects of gyroscopic precession and energy storage. Building on this model, a time-varying traction power demand model was created for scenarios with insufficient power. Subsequently, a rule-based multi-source energy management strategy was designed to regulate the CMG system’s energy flow and power output, addressing instantaneous power compensation, energy recovery, and rollover control. By combining the state of charge of the tractor’s power battery pack and the gyro system, the overall energy management was optimized. When the basic output power of the battery pack was insufficient to meet the instantaneous power demand caused by traction resistance, the gyro rotor decelerated to release energy, compensating for the tractor ’s peak driving power. Experiments on a scaled model platform, focusing on obstacle disturbances and climbing, demonstrated that the CMG system significantly improved the tractor’s direct current bus voltage and compensated for transient power deficits. Furthermore, gyro energy recovery tests following rollover control indicated that the CMG system can effectively perform multiple functions. These included rollover prevention, peak power compensation, and energy recovery from gyro rotor unloading, thus improving overall system utilization and energy efficiency.

Abstract:At present, remote human-computer interaction terminals are mainly displayed in a stacked manner through video images and numerical icons. However, the lack of human factors design makes it difficult for monitors to understand information and has a high psychological load when dealing with faults, which affects the readability and accuracy of emergency response. Aiming to address the problem of insufficient human factors design in the remote terminal stacking interface information acquisition of unmanned electric tractors in facility greenhouses, an interface attention collection system was developed, and based on attention efficiency indicators such as reaction time and image recognition accuracy, attention efficiency experiments were conducted on the stacking interface when there were no faults and sudden single/two/three factor faults. The response law of attention efficiency to the stacking emergency interface was explored, and the evolution law of the distribution field of human factors attention on the interface was analyzed. Human factors engineering interface layout optimization design was carried out, the optimized distributed interface and centralized interface were verified through experiments, and the improvement effect of interface attention readability and accuracy was comprehensively evaluated. The research results showed that the average response time for single factor faults in stacked interfaces, distributed interfaces, and clustered interfaces were 1 096 ms, 1294 ms and 1 097 ms, respectively. The average response time for two factor faults was 1 123 ms, 1142 ms and 1293 ms, respectively. The fastest detection and warning response times for three factor faults were 820 ms, 1108 ms and 749 ms, respectively. The attention distribution fields exhibited distribution patterns of increasing modularity, increasing contrast, and centering, evolving towards change points and significant points, respectively. The interface optimization plan ultimately selected a clustered interface, which decreased the average response time of emergency response by 3.4% compared with that of a stacked interface, increased the average accuracy rate by 11.66% within 2 seconds and 34.94% within4 seconds, and reduced the psychological load on the monitor by 11.99%.

Abstract:To address the issues of poor stability, low steering control resolution, and severe soil damage caused by unilateral braking steering in the automatic steering control of a single hydro static transmission（HST）tracked tractor, a differential steering control system based on state feedback was proposed. Firstly, the mechanism of differential steering using a single HST was analyzed. Then, based on the kinematic model of the tracked tractor, a differential steering control method using pulse width modulation（PWM）was designed. This method improved steering control resolution and stability by precisely adjusting the stroke of the steering hydraulic cylinder. Next, a control system was developed with an STM32F4 microcontroller as the core, which integrated both linear path planning and steering control, completing the design of the onboard controller. Finally, field tests were conducted under three speed conditions on both cement and field surfaces. The test results showed that at speed of 2 km/h, 3 km/h and5 km/h, the mean absolute errors of straight-line tracking on the cement surface were 1.6 cm, 2.2 cm and 3.1 cm, respectively, with standard deviations of 2.7 cm, 2.9 cm and 3.6 cm. On the field surface, the mean absolute errors were 1.7 cm, 1.9 cm and 2.9 cm, with standard deviations of 2.2 cm, 2.1 cm and 3.4 cm, respectively. These results demonstrated that the system outperformed traditional unilateral braking steering in various environments, significantly improving steering accuracy and stability.

Abstract:As piston pin worn features are susceptible to environmental vibration disturbance during diesel engine operation, an effective vibration signal decomposition and noise reduction process is a promising way to enhance the disturbed signals, which is essential to build a reliable and precise binary classifier model to identify piston pin worn. To solve the problem of vibration signal decomposition and noise reduction, a feature extraction algorithm based on orthogonal empirical mode decomposition（ OEMD ）combined with continuous wavelet transform（ CWT ）and principal component analysis（PCA）was proposed. The orthogonal sensor layout was used to collect the vibration signal of the piston pin of the diesel engine in actual operation, and OEMD was used to decompose the orthogonal fusion vibration signal into multiple intrinsic mode functions（IMF）, and then the first four IMF components with 85% energy were selected for CWT processing to obtain the wavelet coefficient matrix. Finally, the optimal score matrix after PCA operation was input into the K-means clustering algorithm for classification. The actual experimental data verified the effectiveness of the proposed method, and the orthogonal fusion results integrated the overall trend and extreme value distribution, so it was more reliable than a single sensor, thus avoiding the interference or feature loss caused by inappropriate sensor installation position. Compared with EMD combined with AR spectrum algorithm and VMD algorithm, the proposed method had stronger noise reduction and feature extraction capabilities, and the classification effect was more obvious in K-means algorithm, which laid a foundation for two-classifier modeling and identification of piston pin wear.

Abstract:In order to improve the friction and wear performance of agricultural machinery wheel molds in low-speed and heavy-load operating environments, WC coatings were prepared on GCr15 substrates by using electro-spark deposition technology and ultrasonic vibration composite electro- spark deposition technology, respectively, with GCr15 used in agricultural machinery wheel molds as the object of study. The microhardness, surface roughness, friction and wear performance of the WC coatings were analyzed and tested by using microhardness tester, surface roughness tester, friction and wear tester and three-dimensional white light interference profiler. The results showed that the surface roughness of the ultrasonic vibration composite WC coating was 4.641 μm, which was lower than that of the electro-spark deposition WC coating by about 62%. The microhardness was 1114.6 HV0.025, which was improved by about 15% compared with that of the electro-spark deposition WC coating, and improved by about 67% compared with that of the substrate. The results of the friction and wear test showed that the average coefficient of friction and the wear of the ultrasonic vibration composite WC coating and the electro-spark deposition WC coating were lower than that of the substrate, which indicated that the preparation of the WC coating can effectively improve the friction reduction and wear resistance of the mold. The average coefficient of friction, wear amount and depth of abrasion of the ultrasonic vibration composite WC coating were lower than that of the electro-spark deposition WC coating, which indicated that the ultrasonic vibration composite WC coating had the best friction and abrasion resistance under the condition of heavy load and low speed. The wear amount of the specimen was in a negative correlation with its microhardness, and in a positive correlation with the average coefficient of friction and coating surface roughness.