Abstract:After a long period of exploration and accumulation, the global productization process of harvesting robot is accelerating. With the increasingly irreplaceable demand for robotic harvesting in agricultural production in China, many enterprises have entered the market and gradually opened up the production and sales. However, insufficient work efficiency has become the core bottleneck for its comprehensive industrialization and production applications. Based on the analysis of the efficiency status of harvesting robots in China, the technology modes and the multi-arm approach of efficiency improvement were summarized. And with the rapid development of agricultural harvesting technology in the future, multi-arm high-speed simultaneous harvesting would become a global trend. On the basis of summarizing the development status of arm design, vision technology, task and motion planning technology of multi-arm harvesting robots, the development trend and goals of the industrialization of harvesting robots were proposed. It was believed that the technological direction for the industrialization of harvesting robots would shift from hand-eye high-precision positioning to strong comprehensive tolerance capability, from specialized customized technology model to universal modular technology solution, and from complex human involvement in professional equipment to an easy-to-use technology system for household products.

Abstract:In the facility tomato planting environment, the accuracy of automatic fruit picking can be affected by overlapping and occlusion of fruits. An instance segmentation model was proposed based on YOLACT to address this issue. Firstly, the categories of fruit overlap and occlusion were subdivided, and the dataset of this type was increased to simulate real picking scenes and improve recognition accuracy in picking decisions. Secondly, the Simple Copy-Paste data enhancement method was employed to enhance the model’s generalization ability and reduce the interference of environmental factors on instance segmentation. Next, based on YOLACT, multiscale feature extraction technology was used to overcome the limitation of single-scale feature extraction and reduce the complexity of the model. Finally, the Swin-S attention mechanism in Swin Transformer was incorporated to optimize the detailed feature extraction effect for tomato instance segmentation. Experimental results demonstrated that this model can alleviate the problems of missed detection and false detection in segmentation results to a certain extent. It achieved an average target detection accuracy of 93.9%, which was an improvement of 10.4, 4.5, 16.3, and 3.9 percentage points compared with that of YOLACT, YOLO v8-x, Mask R-CNN and InstaBoost, respectively. Additionally, the average segmentation accuracy was 80.6%, which was 4.8, 1.5, 7.3, and 4.3 percentage points higher than that of the aforementioned models, respectively. The inference speed of this model was 25.6f/s. Overall, this model exhibited stronger robustness and real-time performance in terms of comprehensive performance, effectively addressing both accuracy and speed requirements. It can serve as a valuable reference for tomato picking robots in performing visual tasks.

Abstract:To solve the problem that the existing contact fruit diameter measurement process is tedious and time-consuming, as well as the susceptibility to record errors influenced by the subjective judgments of experimenters during prolonged measurement, a handheld apple tree fruit diameter measurement system was designed. The system incorporated technologies such as mechanical design, serial communication, and automatic control. It consisted of a handheld fruit diameter measurement tool and a corresponding data-receiving mobile APP. During orchard operations for measuring tree fruit diameter, experimenters held the grip of the fruit diameter measurement tool. By pressing the handle, the transmission module was activated, causing the clamps to open, allowing them to grip and measure the diameter of the tested fruit surface. The data was then transmitted to the mobile APP via Bluetooth for operations such as saving, editing, binding location information, and batch exporting. To assess the precision of the handheld fruit diameter measurement tool, metric block gauge measurements and apple diameter measurement experiments were conducted. Results of the metric block gauge measurement experiment demonstrated that the proposed measurement tool achieved sub-millimeter-level precision. Apple diameter measurement experiment results indicated that the proposed measurement tool achieved a more satisfactory accuracy in measuring apple tree diameters compared with vernier caliper. Even when measuring the diameters of mature fruit, the majority of the measured fruit diameters had mean square error (MAE) and root mean squared error (RMSE) controlled below 0.45mm, meeting the precision requirements for apple fruit diameter measurement.

Abstract:Addressing the issues with existing orchard navigation methods, which are susceptible to canopy density, lighting conditions, irregular planting, and uneven ground, leading to low stability in tree row direction estimation and row end identification methods used for autonomous navigation, a 3D point cloud-based method for pathway planning was introduced. The method comprised three primary components, a tree row identification technique, a scene recognition method, and a pathway planning strategy. These formed a system that enabled robots to autonomously shuttle between densely planted rows. Initially, a trunk point cloud extraction method using a semantic segmentation network was developed for tree row identification and positioning. Subsequently, a convolutional neural network-based method was established for location scene recognition to identify various scenarios within rows. Finally, an inter-row movement strategy managed by a finite state machine and a pathway planning method based on RS curves were designed for continuous walking through multiple rows. The trunk point cloud segmentation achieved an average IoU of 88.3%, with tree localization errors of 2.04% in the x-direction and 1.54% in the y-direction. The average error in tree row direction estimation was 1.11°.The row-end recognition accuracy reached 96%,and the average deviation of in-line centerline walking was 0.08m. These results showed that the proposed methods met the accuracy requirements for tree row positioning and scene recognition in outdoor orchards, which effectively planed pathways between rows and served as a reliable reference for autonomous laser-guided navigation.

Abstract:Aiming at the problem that in the automatic root cutting process of factory bottle-planted enokis, the stroke of the clamping end was fixed due to the structural design, which affected the gripping effect and even the quality of root cutting, an improved enoki-pick_region-YOLO v8 based on the you only look once (YOLO v8) was constructed, realized accurate positioning and contour extraction of the whole bottle-planted enoki as well as the optimal stress area (the key picking region). The accurate localization and contour extraction of the whole bottle-planted enoki and the best stress region guaranteed the reliability of the grasping parameters. On the basis of network optimization, a mask attribution and judgment model based on the minimum Euclidean distance (ED) was constructed, the parentchild relationship between the enoki body mask and the key region mask was clarified, and they were merged for optimization. By analyzing the relative position encoding of the key region before and after the merger, the grasping parameters were determined and converted into coordinates, which provided the basis for establishing the end control mapping model to realize the precise control of the end manipulator’s motion stroke. The experimental results showed that the algorithm achieved a mask recognition rate of up to 99.3% for the enoki body and 99.6% for the key picking region. At the same time, it was found that the quality of the mask was improved, and the error between the width of the picking area and the actual width of the acquired parameters was only 0.7%, and the grasping parameters basically satisfied the conditions of grasping, which effectively realized the accurate identification and localization of the optimal grasping position.

Abstract:To further improve the recognition accuracy and speed of truss-harvested cherry tomatoes, targeting the scenario of automated tomato harvesting in facility environments, a lightweight cherry tomato truss recognition model was proposed based on an improved transformer. Firstly, a cherry tomato dataset encompassing various lighting conditions and harvesting postures was constructed, and the postures of cherry tomato trusses were categorized. Then a lightweight trussharvested cherry tomato recognition model based on an improved RE-DETR was proposed. This model introduced a lightweight backbone network, EfficientViT, to replace the original backbone of RT-DETR, which significantly reduced model parameters and computational complexity. Additionally, an adaptive detail fusion module was designed to efficiently process and merge feature maps of different scales while further lowered computational complexity. Finally, a weighted function sliding mechanism and exponential moving average concept were introduced to optimize the loss function, which addressed uncertainties in sample classification. Experimental results demonstrated that this lightweight model achieved high recognition accuracy (90.00%) while enabled fast detection (41.2f/s) and low computational cost (8.7×109 FLOPs). Compared with that of the original network model, Faster R-CNN, and Swin Transformer, the average recognition accuracy was improved by 1.24%~15.38%, the frames processed per second (FPS) was increased by 25.61%~255.17%, while simultaneously achieved a substantial reduction of 69.37%~92.37% in floating-point operations. The model exhibited strong robustness in overall performance, balancing accuracy and speed, and can serve as a reference for tomato harvesting robots in completing visual tasks.

Abstract:To address the issue of low accuracy in picking pose establishment caused by overlapping fruit and challenging lighting conditions that introduced difficult-to-filter point cloud noise in orchard environments, an accurate method for establishing picking poses based on point cloud denoising using the random sample consensus (RANSAC) algorithm was proposed. Multiple potential spheres were detected from the pre-processed fruit point clouds by using the RANSAC algorithm. The sphere center with the shortest vertical distance to the point cloud capturing device was used to set a distance threshold, which facilitated further noise filtering from the target fruit point clouds and enhanced pose establishment accuracy. Subsequently, the denoised point clouds were spherefitted by using the least squares method to obtain the sphere center coordinates, which defined the precise picking position. Furthermore, by integrating the centroid coordinates from the corresponding binary mask image generated via an instance segmentation algorithm, an approach vector was constructed to determine the harvesting orientation, completing the pose establishment process. Experimental results on overlapping fruit point cloud denoising demonstrated that the proposed method effectively removed challenging point cloud noise from the target fruits. Pose establishment evaluations in an outdoor simulated orchard showed that the proposed method achieved a positioning accuracy of 15.0mm, enhancing the direct RANSAC fitting approach by up to 28.1% in accuracy and 76.0% in stability. Comparative harvesting trials in the orchard confirmed a successful positioning rate of 70.2% by using the proposed approach, which represented an increase of 23.4% over existing methods and a 38.4% improvement in harvesting success. The proposed method offered a robust solution for accurate fruit pose establishment in complex orchard environments.

Abstract:In order to solve the problems of low efficiency and poor picking success rate in the path planning of picking robotic arm in unstructured environment, a four-way search RRTstar algorithm combined with artificial potential field method was proposed. Firstly, the space was segmented by the artificial potential field method to obtain the spatial segmentation point x-split for four-way search;secondly, the random sampling was guided by the artificial potential field method to improve the quality of the sampled nodes;then, based on the node history expansion information, the information factor was added to achieve the adaptive dynamic step size expansion. Finally, the generation path was optimized by greedy backtracking. The effectiveness of the proposed algorithm was verified by two-dimensional simulation experiments, simulation experiments under 6-degree-of-freedom robotic arm and picking experiments. The 2D simulation comparison experiment showed that compared with the RRTstar algorithm, the path cost of the improved algorithm was shortened by 2.01%, the time cost was reduced by 98.81%, and the sampling nodes were reduced by 92.49%. Simulation experiments under 6-degree-of-freedom robotic arm in robot operating system (ROS) showed that compared with RRTstar algorithm, the planning time was reduced by 93.17%, the path cost was reduced by 36.62%, and the number of expansion nodes was reduced by 97.00%. Finally, the picking experiment was carried out with a 6-degree-of-freedom robotic arm, and the experimental results showed that the algorithm effectively improved the picking success rate, which reached 85%, and after combining the attitude constraint method, the picking success rate reached 95%. The proposed path planning method had certain advantages in path planning time, and the picking test proved that the algorithm improved the success rate of lychee picking, and contributed to the development of lychee picking robot.

Abstract:Through research, it was found that octopus has multiple tentacles and more than 200 suction cups on each tentacle, which can effectively avoid the prey from breaking free by the combination of tentacle grasping and suction cup adsorption without knowing the shape of the prey. In order to pick the end of the robot to meet the different shapes, sizes and growth conditions of the apple picking work, the octopus prey way was drawn on, according to the general characteristics of the Shaanxi Fuji apple parameters, the picking robot adsorption mechanism, gripping mechanism, power transmission device and other key parts of the design optimization and its size parameters to determine the design of a set of suction cups adsorption and finger gripping as a combination of pneumatic apple picking robot were completed. A composite pneumatic apple picking manipulator integrating suction cup adsorption and finger gripping was designed. According to the three-dimensional configuration of the manipulator, through the kinematics and dynamics analysis of the suction cup adsorption and finger gripping, the kinematics and dynamics simulation models were established and simulation tests were carried out, and it can be seen from the simulation results that the movement trends of the end of the adsorption mechanism and the end of the gripping mechanism had a better consistency, and the relative slippage between the manipulator and apples was within the range of 0.19~2.28mm. The above simulation results showed that the manipulator had good flexibility of movement and stability of adsorption and grasping. The experimental platform of the six-armed apple harvesting robot was utilized to conduct field trials in an orchard, focusing on the picking of apples. The test classified the apples available for harvesting by the robot into four categories: vertically grown and unobstructed, vertically grown with minimal obstruction, non-vertically grown and unobstructed, and non-vertically grown with minimal obstruction. A comprehensive evaluation was performed based on these categories. The results of the experiment demonstrate that the harvesting hand achieved an overall success rate of 84.7% and a damage rate of only 0.88%, showcasing its capability to accomplish apple picking tasks in intricate orchard environments.

Abstract:Aiming at the difficulty of citrus automatic picking, a terminal actuator using spherical closure for occlusion and shearing of citrus fruit stalks was designed. Firstly, the shearing mechanical properties of citrus fruit stalks were studied, and the effects of five factors on the peak shear force were analyzed, i.e., stalk diameter (2~4 mm), shear speed (20~200 mm/min), stalk angle (0~75°), number of leaves blocked (0~3 pieces), and blade gap (0.5~2 mm). The test results showed that the peak shear force required to shear citrus fruit stalks was increased with the increase of stalk diameter. With the increase of shear speed, the peak shear force was decreased, but not significantly. The stalk angle was one of the factors affecting the peak shear force. Between 0° and 30°, the stalk angle had little effect on the peak shear force, but when it was between 30° and 70°, the peak shear force was increased significantly with the increase of the stalk angle. The blockage of a few leaves had little effect on the peak shear force. Blade clearance was one of the main factors affecting the peak shear force. Large blade clearance had a great impact on shearing thin stalks, which may be difficult to shear off. Controlling the blade clearance within 1 mm was critical for the success rate of the end effector picking. The geometry modeling of the end-effector was carried out, and the parameter optimization model was established with the main constraint condition of controlling the blade gap and the optimization goal of increasing the fruit positioning tolerance. The fruit positioning tolerance was increased from the original 9.2%~48.8% to 26.0%~71.7%. The static analysis of the model was carried out and the selection of the electric push rod was completed. At the same time, the finite element simulation of the model was carried out, and the influence results of the spherical shell deformation on the blade gap were obtained, and the strength of the structure was checked. Finally, the prototype of the citrus picking end-effector was developed, and the field picking and shearing performance test was carried out in the citrus orchard. For the fruit stalk with a diameter less than or equal to 4 mm, the success rate of the fruit stalk breaking was 95%, meeting the requirements of picking citrus.

Abstract:In order to promote the mechanization level of pineapple harvesting and improve its efficiency, a pineapple picking mechanism was designed. The mechanism fed fruit by using a chain feeding, removed fruit by using a pair of rollers and a rotational cylinder which had several flexible rods mounted on the cylindrical surface. Firstly, guiding mechanism was a pair of chain mechanisms with chain claws. Several key parameters that affected the picking efficiency of this mechanism, including the radius, length, mounting height, rotational speed of that pair of horizontal rollers, rational gap value between that pair of rollers were analyzed, and their computing methods were put forward. The radius calculation method of those flexible rods which were mounted on the rotational cylinder was purposed by using a pseudo-rigid body theory, and a suitable range of the rotational speed of those flexibles rod was determined. An experimental machine was designed and manufactured to test feasibility of the harvesting mechanism. That experimental machine consisted of a tractor, a diesel engine, and a harvesting mechanism. The field experiment results showed that the optimal parameter combination for this mechanism was that the forward speed was 0.6 m/s, the radius, installation height and rotational speed of the horizontal roller was 35 mm, 200 mm and 12 rad/s, respectively, and the radius and rotational speed of the flexible rod was 20 mm and 10 rad/s, respectively. Under the optimal parameter combination, the harvesting rate, damage rate and comprehensive evaluation index was 82%, 34.14%, and 77.16%, respectively. The average harvesting time request for each pineapple was less than 1 s. In addition, those pineapple plants after harvesting could maintain good growing condition. The research results indicated that this harvesting mechanism could achieve the goal of rapid picking of pineapple fruits, which can provide a solution for the mechanization of pineapple picking.

Abstract:The end effector is an important part of the harvesting robot. A fresh-eating pepper harvesting end effector was designed by using the cutting-off harvesting method for the growth characteristics of the single-growing pepper fruit. Firstly, the biological characteristics of Capsicum frutescens were tested. The physical parameters such as fruit length, fruit maximum diameter, stem diameter and stem length were measured. The maximum cutting force of Capsicum frutescens stem under different cutting methods and different cutting angles was tested. Secondly, according to the biological characteristics of Capsicum frutescens, the design of the end effector was carried out, and the kinematics and dynamic static mathematical model of the end shearing mechanism was established. Based on the results of dynamic static analysis, the genetic algorithm was used to optimize the rod size of the end actuator shearing mechanism. The length of the crank was 33 mm, the length of the connecting rod was 60 mm, and the length of the connecting rod extension rod was 54 mm. In order to realize the stem cutting of Capsicum frutescens, the shearing mechanism needed 0.94 N·m external driving torque. Therefore, the power source selected the steering gear with a load of 10 kg. Experiments showed that the overall harvesting success rate of the end effector was 91.3%, which verified the harvesting performance of the end effector of the capsicum, and had good harvesting performance, which provided an option for agricultural robots to harvest capsicum.

Abstract:Aiming at the problems of complex structure, low efficiency of single workstation work, or oxidation and redness of cut edges in the existing picking effector of famous tea, a simple structure, high work efficiency, and good picking quality rotary two workstations effector for harvesting and collection was designed. The two effectors of the soft picking fingers were in the picking and collecting station through a forward and reverse cycle, achieving the picking and collecting functions. By analyzing and pre-testing the effector harvesting action, the factors that affected the success rate of effector harvesting were obtained as follows: servo arm angle, installation plate speed, and silicone thickness. Using Box-Behnken to study the influence of various factors on the success rate of tea harvesting, a quadratic regression model was established with the success rate as the response value. The main and secondary order of significance was obtained as follows: installation plate speed, servo arm angle, and silicone thickness. Using Design-Expert software, with the goal of harvesting success rate, various factors were optimized to obtain the best parameters: installation plate angular velocity ω=265.329(°)/s, servo arm angle Δθ=40°, silicone thickness d=4.986 mm. The optimized parameters were rounded and experiments were conducted, with a relative error of less than 5% between the experimental and predicted values. The integrated effector of the rotary two workstations famous tea picking and collection system can achieve high-quality and efficient picking and collection.

Abstract:To address the issue of uneven seeding rates across rows during curved operations of wheat seeders in rice stubble fields in the middle and lower reaches of the Yangtze River agricultural zone, a multi-row uniform seeding control system was designed for wide-row wheat seeders. This system employed a multi-rate GNSS/IMU integrated navigation system to acquire the motion data of the seeder unit. Using the rigid body kinematics model, the ground speed of each seeding unit’s uniform seeder was calculated. The metering device’s drive motor speed was then controlled to meet the agronomic requirements of wide-row sowing. The STM32 microcontroller, combined with CAN communication and PID controllers, adjusted the seeding axis speed to achieve multi-row uniform seeding. Road experiment results indicated superior accuracy when the IMU sensor sampling rate was set at 20 Hz and the RTK-GNSS sampling rate was set at 10 Hz, with an average relative error of 4.18% in seeder ground velocity estimation and an average relative error of 11.26% in the angle between the velocity direction and the heading of the seeder, and the average coefficient of variation of seeding uniformity in each row of the system was 7.68%. Field experiment demonstrated that each row’s seeding quantity index in the multi-row uniform seeding control system for wide-row wheat seeder, under different driving paths, was consistently not less than 94.90%, with a coefficient of variation for each row not exceeding 0.97%. This system can meet the agronomic requirements for wheat wide-row seeding in the agricultural region of the middle and lower reaches of the Yangtze River.

Abstract:Aiming at the problems of unstable operation of the forward speed compensation mechanism of existing direct insertion planter in the northwest dry zone and large size of the running space, a non-circular gear-driven forward speed compensation mechanism was proposed, based on which a type of maize hole seeder with vertical insertion was developed. The structural composition, working principle and technical parameters of the whole machine were described; the forward speed compensation mechanism and the hole seeder were analyzed in depth theoretically. When the hole spacing was 330 mm, the forward speed was 0.5 m/s and the sowing depth was 45 mm, the transmission ratio and the center distance of the forward speed compensation mechanism were optimized, based on which the non-circular gear-driven forward speed compensation mechanism was generated. Combined with the size of maize seeds to determine the size range of the shaped eye structure on the seed picker wheel, and the optimized result showed that when the width of the shaped eye was 11.79 mm, the height of the shaped eye was 7.23 mm, and the opening angle of the shaped eye was 15°, the effect of seed discharge was better. From the field validation experiment, it can be seen that the hole seeder empty hole rate, hole number qualified rate, sowing depth qualified rate average value were 2.0%, 90.1% and 92.3%, respectively; the membrane holes the device broke were smaller, the membrane holes misalignment rate was 4.3%;the device disturbed average amount of soil disturbance in the horizontal direction and the vertical direction was 15 g and 25 g, respectively. The results can meet the actual requirements of maize direct sowing in dry region of Northwest China.

Abstract:Aiming at the uniformity of current corn wide and delta-row narrow planting mode under high speed (10~16 km/h) operation of precision seed-metering device and the delta-row sowing effect of lack of targeted evaluation indexes of the problem, a double-cavity double-disc staggered synchronous rotary pneumatic high-speed precision planter was designed and the zigzag qualification index of the “delta-row” evaluation of the effect was put forward. The effect of “delta-row” was evaluated. The basic structure and working principle of the seed discharger were described, the key structural parameters of the seed discharging disc were determined by theoretical analysis and numerical simulation, the mechanical model of the seed charging process was established, and it was determined that the charging height, operating speed, and the positive pressure value of the air cavity were the main factors affecting the performance of the seed discharger. The three-factor, five-level Central Composite test was conducted with seed filling height, operating speed, air cavity pressure value as the influencing factors, and the “delta-row” qualification index and the maize seeds, namely “WAYO 187”, “DECA C2235”，“AOYO RED A9” and “FARLEY 1439”, were used to conduct the sowing test of the optimal parameter combinations of seeders. The results showed that the optimal parameter combination of the seed dispenser was 40.598 mm of seed filling height, 3.763 kPa of positive air cavity pressure and 11.358 km/h of operation speed, and the delta-row qualification index W of the parameter combination was 94% and the coefficient of variation of the two-row spacing W1 was 6.11%, which was in line with the optimization results, and it can satisfy the requirements of high-speed precision sowing of maize in the delta-row dense-planting mode.

Abstract:Aiming at the problem that it is difficult to monitor the negative pressure in the air distribution pipeline in real time and it is easy to lead to the decline of seeding performance in the process of precise seed adsorption of rapeseed rape pneumatic precision seed metering device, a synchronised monitoring system for the negative pressure in rapeseed rape multiline precision seeding pipeline based on virtual instrumentation technology was designed. Pressure sensor as a measuring element for real-time collection of negative pressure of main and branch pipelines, NI-9205 digital acquisition card as a data acquisition module, the electric valve was used to control the negative pressure of main pipeline, and the LabVIEW software was used for the conversion, real-time display, analysis, preservation, fault alarms of the pressure value and so on. The accuracy of the pipeline negative pressure monitoring system was calibrated, and the results showed that under the condition that the rotational speed of the seeding plate was 20~60 r/min, and the valve opening was 18.3%~33.5% (the static negative pressure of the main pipeline was 1000~3000 Pa), the monitoring value of the monitoring system and the measured value of the wind manometer were not more than 3.63%, and the coefficient of variation of the stability of the negative pressure was not more than 2.97%. Bench test showed that under the conditions of 20~60 r/min rotational speed of the seeding plate and 18.3%~33.5% valve opening (1 000~3 000 Pa), the negative pressure loss rate of the short-distance air distribution system with the cone-column and cone-column air distribution mechanism was not more than 18.99%, the coefficient of variation of the uniformity of the negative pressure of the branch was not more than 2.3%, and the relative deviation of the coefficient of variation of each row did not exceed 4.27%. The field test showed that when the rotational speed of the seeding plate of the seeding operation was 23.1 r/min, 34.2 r/min, and 44.5 r/min, respectively, the negative pressure loss rate was not more than 18.75%, the coefficient of variation of the uniformity of negative pressure in the branches was not more than 2.95%, and the relative deviation of the coefficient of variation of each row did not exceed 4.52%. The research can provide technical support for analyzing the mechanism of precise seed adsorption and stable transport in pneumatic rapeseed precision seed metering devices, as well as for optimizing and improving the layout of the air distribution pipeline.

Abstract:Considering the problems of soil in mid-lower reaches of the Yangtze River is sticky and the amount of straw is large, the utilization of conventional rotary tillage rapeseed direct-seeding machines often leads to the plough layer is shallow and the straw coverage rate is low, the longitudinal size of the existing combination of plowing and rotary tillage rapeseed seeder is generally larger, the rapeseed direct-seeding agronomic requirement and the requirement were combined to build reliable plough layer, a parallel mounted buckling plough group was designed, a combined tillage operation scheme of “disc plow blade cutting and pressing stubble, parallel plow group turning over soil and burying stubble, the rotary tiller knife roll breaks up the clod and equalizes the soil” was proposed. The plough surface of the buckling plough was designed by using the method of straight ruled surface, the parametric analytical equation of plough surface was established, a simulation model for the interaction between tillage tool, soil, and straw was established based on the discrete element method, and the quadratic orthogonal rotation test was carried out to analyze the buckling performance of the buckling plough by using EDEM software. The simulation results showed that when the deflection angle θ was 12° and the change value Δθ was 11°, the buckling effect of the plough body was the best. Field experiments showed that the average soil buckling rate was 91.3% when the tilling depth was 230 mm and the forward velocity was 5.15 km/h, and the buckling plough had a good stubble burying effect. The parallel buckling plough group had good through performance under high crop straw conditions, with an average straw coverage rate of over 82.5%, an average tillage depth of over 218.6 mm, and a stability coefficient of over 90.2% for tillage depth. Field comparison experiment showed that under the condition of average stubble height of 513 mm after rice harvest, the average depth of rapeseed seed bed preparation device combined with parallel plow group turnbuckle and rotary tillage was 229.7 mm, the straw coverage rate was 87.5%, the soil breaking rate was 89.7%, and the planter bed surface flatness was 31.8 mm, compared with the operation effect of conventional rotary tillage, the average ploughing depth was increased by 90.9 mm, the straw coverage rate and soil breaking rate were increased by 7.2 percentage points and 6.5 percentage points, respectively, and the operation quality met the requirements of rapeseed direct-seeding, and met the requirements of reasonable cultivation layer construction of rapeseed seed bed of “deep turning and stubble burying, upper looseness and lower tightness”.

Abstract:Aiming at the problem that the population is divided on both sides of the ladle chamber during the operation of the traditional burrow planter, the ladle chamber was not fully filled when the amount of seed in the seeding drum was small. Based on the physical characteristics of flax seeds and the requirements of planting and agronomy, a precise point planter for flax seed was designed. By analyzing the working principle of the burrow planter, the composition, chute structure and structural parameters of the direct current scooping ladle were determined. The motion analysis of the ladle scooping process and the clearing process was carried out, and the angular velocity range of the burrow planter was determined. Using the depth of direct current scooping ladle, the angle of bridge slot and the angular velocity of the burrow planter as test factors, and the pass rate, miss rate and replay rate of the burrow planter as test indexes, the quadratic rotation orthogonal combination test was carried out by using EDEM discrete element simulation software. The results showed that the best performance was obtained when the depth of direct current scooping ladle was 2.75 mm, the angle of bridge groove was 56.56°, and the velocity of burrow planter was 2.71 rad/s, the qualified rate of seed expulsion was 94.27%, the missed rate was 5.67%, and the replay rate was 0.06%. The average pass rate, miss rate and replay rate were 89.00%, 8.33% and 2.67%, respectively. The results of bench test were basically consistent with those of simulation test. The results showed that the seed discharge effect of direct current scooping ladle was better than that of tongue scoop. The qualified rate of seed placement can be increased by 3.39 percentage points, the missed rate can be decreased by 2.68 percentage points, and the replay rate can be decreased by 2 percentage points.

Abstract:An electronic fruit system specifically designed for walnuts, integrating multi-point vibration parameter collection with simulated fruit abscission functions was developed to investigate the conditions under which walnuts detached from the tree. The development process involved creating a dynamic model that described the interaction between walnuts and the tree, establishing criteria for walnut abscission, and designing a device along with an indoor experimental platform to test the system’s capabilities. The reliability and functionality of the system were confirmed through indoor experiments, which demonstrated its effectiveness in accurately collecting and analyzing vibration data from walnuts. Subsequent outdoor field tests were conducted to evaluate the system’s performance in a real-world setting. During these tests, the electronic fruit system collected synchronized multi-point vibration data, providing insights into the dynamics of walnut detachment. The data analysis revealed that during vibration-assisted harvesting, walnuts predominantly experienced normal vibrations, with the normal force exerted on the walnuts being the primary factor leading to their detachment from the tree. The system’s integration of multi-point synchronous measurement and simulated abscission functionality offered significant advantages for studying walnut drop conditions. Moreover, this technology showed promise for application in automated harvesting of other crops, potentially revolutionizing agricultural practices by enhancing efficiency and effectiveness in crop management. By providing detailed and actionable data, the electronic fruit system not only advanced understanding of walnut abscission but also contributed to the broader goal of improving harvesting techniques and practices across various agricultural sectors, making it a valuable tool with extensive future applications in the industry.

Abstract:In order to address the challenges posed by limited access of large ditching-fertilizing machine in mountain orchards and insufficient power of small ditching-fertilizing machine to meet the required ditching depth, as well as the limited functionality of most small ditching-fertilizing machine, a compact vertical spiral ditching-fertilizing machine was developed for efficient integration of ditching, fertilization, and soil covering functions in hilly orchards with 25° slope. Through theoretical analysis, the key components such as ditching device, fertilizing device and soil covering device were designed. The Hertz-Mindlin with Bonding contact model was selected to establish the ditching simulation model, taking into account the soil compaction characteristics in hilly orchards. Using the ditching rotation power consumption, forward power consumption and specific power consumption as indexes, the Box-Behnken simulation test was conducted to optimize the structure parameters of spiral ditching opener. The optimization results were as follows: the optimal spiral angle, number of teeth, edge angle and tooth length of the opener were 17.6°, 10, 30° and 19.76 mm, respectively, and the rotational power consumption, forward power consumption and specific power of the opener with optimal parameters were 6.74 kW, 0.132 kW and 0.165 kW·h/kg, respectively. The opener with optimal parameters was fabricated and the experiment was conducted to validate the simulation. The results showed that the relative error of the ditching rotation power between experiment and simulation was 5.6%, indicating that the opener optimization results were correct. Field test results of the machine showed that the stability coefficient of ditching depth, consistency coefficient of ditching bottom width, uniformity coefficient of fertilization and soil coving rate were 96.91%，98.16%，97.08% and 77.38%, respectively, and all indexes of the ditching-fertilizing machine met the agronomic requirements.

Abstract:A device with a wing-shaped curved surface was designed to separate impurities based on the principle of the Coanda effect. The device improved the operational efficiency and separation quality of the second-stage impurity separation device of a corn breeding plot harvester. Moreover, the device solved some problems of conventional devices for cleaning and separating impurities, such as large size, poor flow field stability, and long time to stabilize the flow field. The structural parameters of the device were determined by the theoretical analysis of the gas flow field and the simulation tests by using computational fluid dynamics and discrete element method. Particles in the flow field experienced complex forces, among which the trailing force was the most effective for separating small particles. The magnitude of particle trailing acceleration was mainly influenced by particle density and diameter. Therefore, two sets of single-factor tests were conducted to investigate the separation effect of the device on particle mixtures with different densities and diameters, respectively. By analyzing the experimental results of the single-factor tests, the relationship equations between the density particles and their streamline deflection angles and the relationship equations between the diameter particles and their streamline deflection angles were obtained. Finally, the bench test results showed that the wing-shaped curved surface impurity separation device had a feasible separation function for the light impurities in the mixture after corn threshing. The bench test data indicated that the impurity content of corn kernels was as low as 1.014%, and the operational performance index of this device met the expected design requirements.

Abstract:Cavitation is a complex multi-phase flow phenomenon. In the development of cavitation, the transient phase transition between liquid and steam results in multi-scale vortex motion. The transient cavitation dynamics is closely related to the evolution of cavitation vortex structure. Two vortex identification methods, Q criterion and Omega discriminant method, were used to explore the cavitation and vortex characteristics of centrifugal pump impeller region and their effects on pressure pulsation under different cavitation degrees at rated flow Qd=0.321 m3/s. Based on Schnerr-Sauer cavitation model, the full-channel flow field of vertical single-stage single-suction volute centrifugal pump at four different cavitation degrees, including the initial stage of cavitation, the development stage of cavitation, the transition stage of cavitation and the deterioration stage of cavitation was numerically simulated. The results showed that the flow in the impeller region was complex under cavitation conditions, and the cavitation morphology and vortex formation affected each other, and both influenced the pressure pulsation in the impeller region. The Omega method can emotionally capture the reflux vortex at the inlet of the impeller, the passage vortex in the impeller channel and the wake vortex at the trailing edge of the impeller. In the early stage of cavitation, due to the influence of large-area channel vortices and volute tongue in the passage, the size of the bubbles above each blade was different. When cavitation was severe, high-speed vorticity of gas-liquid mixture appeared under the influence of cavitation loss, resulting in the increase of low-frequency pressure pulsation signal, and the energy released by the cavitation moving to the high-pressure area led to a significant increase in outlet pressure pulsation.

Abstract:To address the class imbalance in sweet cherry data, a novel image enhancement method based on sweet cherry generative adversarial network, SCGAN was proposed. The generator incorporated multi-scale residual blocks (MSRB) and the convolutional block attention module (CBAM), enhancing the model’s feature representation and the quality of generated images. These blocks captured features at various scales, and CBAM focused on channel and spatial information, improving image quality. In the discriminator, spectral normalization and the Wasserstein distance with a gradient penalty loss function were applied. This combination controled the discriminator’s power, prevented overfitting, and boosted training stability and speed. Experimental results showed that SCGAN produced higher quality defective sweet cherry images compared with traditional GANs, with Fréchet inception distance (FID) scores of 64.36 and 59.97 for two types of defects. After data augmentation with SCGAN, classification accuracy for VGG19 and MobileNetV3 was increased by 16.44 percentage points and 13.94 percentage points, respectively. The data augmentation method presented held significant potential in addressing data imbalance issues within the agricultural and food sectors. It not only improved the generalization capability of models but also provided a more reliable data foundation for practical applications. Through this approach, it was possible to more effectively tackle long-tail class imbalance issues, which enhanced the accuracy and efficiency of agricultural and food detection systems.

Abstract:In response to the poor adaptability of a single sensor in facing complex field environments, a maize crop row detection method was proposed based on the fusion of solid-state LiDAR and RGB camera. Firstly, a joint calibration method for solid-state LiDAR and RGB camera was studied to simultaneously acquire maize crop row images and point cloud data for data preprocessing. Next, the preprocessed image data and point cloud data were fused to achieve point cloud “coloring”, and a clustering algorithm based on point cloud “coloring” for detecting regions of interest was proposed. The clustering was done by using the “colored” point cloud, and the availability of both point cloud and color information was separately validated based on crop planting agronomic standards (row spacing) to cluster the regions of interest effectively. Finally, by dividing the point cloud into horizontal strips, the feature points of the target point cloud were clustered to identify crop row feature points, and a crop row detection line was fitted by using the least squares method. By adjusting only the row spacing parameter, the algorithm can achieve crop row detection throughout the crop lifecycle. The algorithm’s performance was verified by using data from maize seedling, early, mid, and late stages under normal conditions, with average centerline error not more than 1.781°, accuracy not less than 92.69%, and average processing time not more than 102.7 ms. Furthermore, to test the algorithm’s robustness, crop row detections under four challenging conditions, including high weed background, missing rows, weed height similar to maize height, and completely closed rows, were conducted in complex agricultural field backgrounds. The algorithm showed an average error of not more than 1.935°, accuracy not less than 91.94%, and an average processing time not more than 108.3 ms. Discussions highlighted the superiority of using point cloud “coloring” for extracting crop row centerline, providing a reliable approach for detecting crop row centerlines.

Abstract:The interrow path of maize in the middle and late stages is interfered by factors such as insufficient light and occlusion, which is not favorable to the detection of navigation lines during autonomous operation of agricultural robots. To address this problem, an algorithm based on the improved Fast-SCNN semantic segmentation model for detecting the navigation lines in the interrow path of maize in the mid-late stage was proposed. Firstly, to address the problem that the current path semantic segmentation model was not accurate enough for edge segmentation in the mid-late maize environment, an Edge-FastSCNN model was proposed, and the edge extraction module (EEM) proposed was introduced in the model branch to obtain accurate path boundary information, and spatial pyramid pooling was introduced into the model. Atrous spatial pyramid pooling (ASPP) module was introduced in the model to fuse the image boundary information and deep features. Then based on the interline path mask predicted by the model, the left and right boundary points of the path mask were detected by pixel scanning method, and the midpoint of the path mask was obtained by weighted average method. Finally, the least squares method was used to fit the navigation lines to achieve the detection of the mid- and late-stage maize interline path navigation lines. In order to verify the performance of the proposed method, model performance comparison experiments and navigation line detection experiments were conducted based on five environments such as normal light without shade, insufficient light, shadows, weeds shade, and leaf shade of maize in the middle and late stages. The experimental results showed that the average intersection and merger ratio of the model was 97.90%, the average pixel accuracy was 98.84%, the accuracy rate was 99.39%, and the inference speed was 63.0 f/s;the average intersection and merger ratio of the model in the five environments mentioned above was ranged from 96.93% to 98.01%, and the average pixel accuracy was ranged from 98.33% to 99.03%, and the accuracy rate was from 98.53% to 99.12%;the average value of heading angle deviation between the predicted navigation line and the real navigation line in the above five environments was 1.15°~3.16°, and the average pixel lateral distance was 1.89~ 3.41 pixels;the average processing time for a single-frame image of the navigation line detection algorithm was 90.04 ms. Therefore, the navigation line detection algorithm proposed met the mid- and late-stage maize interline path navigation task’s accuracy and real-time requirements.

Abstract:The detection of young grape cluster fruits is challenging due to the influence of background color, occlusion, and lighting variations. To achieve robust detection of young grape cluster fruits for the varying conditions, an improved YOLO v8n model that integrated shuffle attention (SA) mechanism was proposed in the work. By incorporating SA mechanism into the Neck network of the YOLO v8n model, the multi-scale feature fusion ability of the network was enhanced, the feature information representation of the detection target was improved, and other irrelevant information was suppressed, improving the accuracy of the detection network, which achieved efficient and accurate detection of young grape cluster fruits without significantly increasing network depth and memory overhead. Wise intersection over union loss (Wise-IoU Loss) with the dynamic nonmonotonic focusing mechanism was taken as the bounding box regression loss function, to accelerate the network convergence for the better detection accuracy of the model. Herein, a Grape dataset was constructed, which comprised 3 780 images of young grape cluster fruits in complex scenarios along with corresponding annotation files. Training and testing results of the SAW-YOLO v8n model on this dataset showed that the precision (P), recall (R), mean average precision (mAP), and F1 score of the young grape cluster fruit detection algorithm based on SAW-YOLO v8n were 92.80%, 91.30%, 96.10%, and 92.04%, respectively, where the detection speed was 140.85 f/s, and the model size was only 6.20 MB. Compared with that of SSD, YOLO v5s, YOLO v6n, YOLO v7-tiny, and YOLO v8n, the mAP was increased by 16.06%, 1.05%, 1.48%, 0.84% and 0.73%, respectively, and F1 scores were increased by 24.85%, 1.43%, 1.43%, 1.09% and 1.60%, respectively, and the model weights were reduced by 93.16%, 56.94%, 37.63%, 47.00%, and 0, respectively, which was the smallest among all models and had obvious advantages in lightweight and high accuracy. Moreover, the young grape cluster fruits detection with different degrees of occlusion and lighting conditions were also explored, and the result showed that the young grape cluster fruit detection method based on SAW-YOLO v8n can adapt to different occlusion and lighting changes, and had good robustness. In summary, SAW-YOLO v8n not only met the requirements of high-precision, high-speed, and lightweight detection of young grape cluster fruits, but also had strong robustness and real-time performance.

Abstract:Visible light imaging is becoming an effective tool for high-throughput plant phenotyping and genetic research due to its advantages of rapidity, economy and non-destructiveness. However, the evaluation of yield phenotypic characteristics that are invisible to the naked eye based on visible light images remains to be solved. A technical method for evaluating sorghum aboveground biomass by fusing multi-class features with multi-view images was proposed to address the problem of limited image data accuracy due to overlapping plant leaf occlusion and single variable scale. A two-factor (water and nutrient) and two-level (high and low) experiment was conducted on 300 sorghum plants of 15 germplasm genes. Based on a rotating platform, totally ten side-view images and one top-view image were automatically collected at equal angles for each sorghum plant by using a visible light camera. The morphological characteristics (top-view and side-view projection area), color characteristics (RGB pixel values) and texture characteristics (mean, covariance, homogeneity, etc.) of each sorghum plant were extracted through plant mask images. The information from multiple perspectives was averaged, and 16 color vegetation indices were constructed based on the image R, G, and B pixel values. The results showed that compared with considering image information of a single type of variable and a single perspective, the fusion of morphological, texture and color features based on multi-perspective average image information can significantly increase the ability to obtain the aboveground biomass phenotype of sorghum. The SVR, RF and BPNN algorithms were used to fuse 21 sets of optimized image data variables to construct a regression model for aboveground biomass of sorghum. The RF algorithm model with the highest accuracy had a test set determination coefficient (R2) of 0.881, a root mean square error (RMSE) of 60.714 g/m2, and a mean absolute error (MAE) of 42.364 g/m2. In order to further optimize the parameters of the RF algorithm model, GA, GS and SSA were selected to optimize the hyperparameters of the RF algorithm model. The results showed that the test set R2 of the SSA-RF optimization model was increased to 0.902, the RMSE was 48.706 g/m2, and the MAE was 39.877 g/m2. Based on the fusion of multi-view image morphology, color and texture features, more effective information can be derived from limited information for estimating the aboveground biomass of sorghum, thereby providing a theoretical basis and technical support for sorghum growth monitoring, stress detection, precise application of water and fertilizer, and rapid screening of improved varieties.

Abstract:In order to solve the problems of large size, single recognition scene and high hardware requirements for deploying application of existing pig behavior recognition models, a lightweight multi-scene group pig behavior recognition model YOLO v5n for pig behavior recognition (YOLO v5n-PBR) was proposed. Firstly, a multi-scene group pig behavior dataset was constructed by shooting and collecting group pig behavior data from different breeding scenes, different pig numbers and different angles, and based on the characteristics of pig behavior objectives in the dataset, the transfer learning method and the optimal transport assignment label assignment method were introduced to train the YOLO v5n model, which accelerated the model convergence speed and improved the model accuracy, and a high-precision multi-scene group pig behavior recognition model was constructed. Then the L1-norm pruning algorithm was used to screen and delete the unimportant channels in the model to remove the redundant parameters. Finally, the performance degradation caused by pruning was removed by fine-tuning training and intermediate feature knowledge distillation, so that the lightweight multi-scene group pig behavior recognition model YOLO v5n-PBR was obtained and deployed as embedded devices. Experimental results showed that the mean average precision (mAP) of the YOLO v5n-PBR model was 96.9%, with parameters, amount of computation, and memory footprint being 4.700×105, 1.20×109, and 1.2 MB, respectively. The deploy real-time recognition frame rates on embedded devices with different systems and hardware configurations were 12.2 frames/s and 66.3 frames/s. Compared with that of the original YOLO v5n model, the mAP was improved by 1.1 percentage points, and parameters, amount of computation, and memory footprint were decreased by 73.3%, 70.7%, and 68.4%, respectively. The deploy real-time recognition frame rates were increased by 74.3% and 83.1%. In addition, the YOLO v5n-PBR model trained based on the multi-scene group pig behavior dataset can reach 98.1% of mAP on four single-scene or dual-scene group pig behavior datasets, and the statistical results of embedded device deployment recognition of six pig behavior videos in two different breeding scenes were similar to those of manual statistics, with an average accuracy and average recall rate of 95.3%, which achieved strong generalization with fewer parameters. The YOLO v5n-PBR model proposed had the advantages of high accuracy, small size, fast speed, and strong generalization, which can meet the deployment requirements of embedded devices and provide a technical basis for real-time and accurate monitoring of pig behavior and the deploying application of pig behavior recognition model.

Abstract:To solve the challenges of detecting and counting Camellia oleifera fruits with multiple occlusions in complex environments, a detection model was proposed based on a dual-backbone network and a consecutive attention feature fusion module (CAFF). The dual-backbone network combined the advantages of two different backbone networks to achieve efficient extraction of different features. In addition, a dual-input single-output CAFF module was designed. This CAFF module replaced the traditional concat operation and optimizes the fusion strategy for multi-scale feature information. In order to strike a balance between model precision and size, the ghost convolution (Ghostconv) module was used, and the spatial pyramid pooling fast (SPPF) layer was removed. It accelerated training time and reduced the number of parameters. The improved YOLO dual-backbone & consecutive attention feature fusion & lightweight (YOLO-DCL) model performed well on all kinds of occlusion detection tasks, with a mean average precision (mAP) of 92.7%, precision of 90.7%, and recall of 84.9%, while the model size was only 5.7 MB. Compared with the YOLO v8n model, it increased 4.0 percentage points of mAP, 8.6 percentage points of precision, and 2.3 percentage points of recall. At the same time, the model size was decreased by 9.5%. Besides, the model incorporated the ability to automatically count Camellia oleifera fruits with occlusion categories, which can reduce labor costs and improve the accuracy of yield estimation. It was very suitable for deployment in complex environments.

Abstract:Aiming to study the temporal and spatial distribution of flash drought in the Huang-Huai-Hai Plain and its impacts on vegetation photosynthesis, the spatiotemporal variations of climate variables and flash drought events of the Huang-Huai-Hai Plain in 2001—2020 were firstly identified based on the reanalysis datasets of ERA5 and SMCI1.0. Daily soil moisture data for each layer were used to calculate the pentad-averaged (5 d) values and further converted into soil moisture percentile. A flash drought event was recognized when the soil moisture percentile declined from 40% to 20% within four pentads. In this way, temporal-spatial distributions were analyzed for the times and duration of flash drought in the Huang-Huai-Hai Plain.Meanwhile, solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were used to represent growth statues of vegetation. Variations in SIF, NDVI, and EVI were compared along with the dynamics of soil moisture changes. The correlation coefficient between SIF and the times of flash droughts was also used to study the photosynthesis of different vegetation types responding to flash drought. The results indicated a warming and humidifying environment of the Huang-Huai-Hai Plain in 2001—2020. Flash drought was more severe in the northwest and southeast of this region. Most areas experienced more than 20 times of flash drought events with a cumulative duration exceeding 90 pentads in 2001—2020. Meanwhile, increasing trends were found for both times and duration of flash drought for most areas in the Huang-Huai-Hai Plain, with rising rates of 0.6 times per 10 a and 2.2 pentads per 10 a, respectively. Notably, the areas with increasing trends were mainly located in the areas with frequent flash drought events. Temperature (significant positive correlation), wind speed (negative correlation), and precipitation (negative correlation) were the three most related meteorological variables with flash drought. Compared with the NDVI and the EVI, SIF varied more synchronously with soil moisture during flash drought events. Furthermore, responses of photosynthesis to flash drought were different among the four vegetation types. Shrubland photosynthesis had the highest sensitivity to flash drought, followed by farmland, forest, and grassland. However, the largest correlation coefficients were similar among the four vegetation types in the Huang-Huai-Hai Plain.

Abstract:Aiming at the problem that the current sprinkler flow control is not fine enough, the precise variable adjustment of the irrigation flow of the Senninger i-Wob2 sprinkler was realized by installing a V-type flow control valve in front of the sprinkler as an actuator. Through the hydraulic performance test of the valve body, the mathematical relationship between the pressure before the valve, the opening of the valve body and the flow of the nozzle was established. According to the mathematical relationship, the overall structure of the variable irrigation control was designed, and the specific operation steps of the control structure were clarified, so as to realize the precise adjustment of the irrigation flow. The results showed that the flow calculation formula and the calculation method of the corresponding parameters were established, and the characteristic flow coefficient KL of the mathematical model of pressure-opening-flow was 20.40, the pressure prediction coefficient KP after the valve was 0.97, and the flow prediction coefficient Kφ was 7.08 through the hydraulic performance test data of the valve body. The range and flow rate of the nozzle changed obviously with the valve opening before the opening of 60%. However, after the opening was 60%, the range and flow rate changed with the valve opening was getting smaller and smaller;the steady-state relative deviation of the flow regulation of the control valve did not exceed 6.67%, the adjustment time did not exceed 42 s, and the measurement accuracy was above 96%. The research result realized the accurate regulation and control of sprinkler irrigation flow through the flow control valve, and provided strong data support for the application of flow control valve in variable irrigation.

Abstract:In arid and semi-arid areas with shallow groundwater depths, where the distribution of cultivated and wasteland is more dispersed and broken, dry drainage and salt control are important management tool for reducing soil salinization. Dry drainage salt control makes the non-irrigated wasteland an evaporative salt sink area, receiving water and salt from neighboring irrigated farmland. A typical dry drainage unit (wasteland and adjacent cultivated land of different crops) in Hetao Irrigation District was used as an example, and Darcy’s law was utilized to calculate and reveal the water and salt distribution and migration law between the wasteland and adjacent cultivated land. The results showed that there were significant differences in ET among different farmland types, and the average ET of corn farmland was 23.47% and 88.97% higher than that of sunflower farmland and wasteland, respectively, and the ET of sunflower farmland was 53.06% higher than that of wasteland;there were obvious differences in the salinity of soil in the root zone (0~100 cm) of different crops. The salinity in sunflower farmland was 2.10~2.47 times higher than that of corn farmland, and the average soil salinity of wasteland was 2.81~6.95 times that of cultivated land. In addition, during the spring irrigation and crop growth stages, irrigation and precipitation caused the groundwater depth of farmland to rise maximum of 157 cm, which promoted the migration of water and salts from the farmland to unirrigated wasteland, alleviating the salinity in the root system of the cultivated land, and sustaining the growth of the crop. During the spring irrigation period, the irrigation water of the sunflower field leaked and quickly drained to the unirrigated land, resulting in the maximum water and salt fluxes, with the average water transport amount of 0.045 cm/d and salt transport of 0.013 mg/(d·cm2). Wasteland, as the drainage area of cultivated land, had the function of maintaining water-salt balance, and the water-salt transport law among cultivated and wasteland was quantitatively analyzed, which can provide a theoretical basis for the water-salt balance law in arid areas.

Abstract:The south bank of the Yellow River in Dalate Banner, Ordos City, Inner Mongolia Autonomous Region, is characterized by arid climate, low precipitation, annual evaporation much larger than annual precipitation, and the proximity to the Yellow River leads to a high water table, which leads to prominent soil salinization. Taking the saline soil along the south bank of the Yellow River in Dalate Banner as the research object, based on the multi-source data of Sentinel-1, Sentinel-2, Landsat-8 and SRTM DEM, correlation analysis and continuous variable projection combined with Lasso regression (Lasso), random forest regression (RF), light gradient boosting machine model (LightGBM), extreme gradient boosting (XGBoost), one dimensional convolutional neural networks (1DCNNs), and deep neural network (DNN) were used to estimate soil salinity during spring bare soil period and vegetation cover period. The results showed that the XGBoost model had the highest accuracy, and the coefficients of determination (R2) of the test sets were 0.76 and 0.58 for the spring bare soil period and vegetation cover period, the root mean square errors (RMSE) were 5.76 g/kg and 7.22 g/kg, and the mean absolute errors (MAE) were 3.38 g/kg and 4.33 g/kg. The combination of multi-source remote sensing data and the variable screening method by using the XGBoost model revealed that the soil salinity spatial distribution in different seasons in the study area was the most effective, and the results of salinity inversion basically coincided with the results of the actual field investigation and analysis. The variable importance analysis showed that the important inversion factors in the spring bare soil period and vegetation cover period were salinity index (48.3%) and topography factor (33.8%), vegetation index (22%) and topography factor (47.9%), respectively. The research result can provide an effective method for remote sensing inversion of saline and alkaline land on the south bank of the Yellow River in Dalat Banner, and provide a theoretical basis for monitoring and preventing salinized soil in the spring bare soil period and vegetation cover period.

Abstract:Soil type mapping reveals the geographical distribution and characteristics of soils and provides a scientific basis for the use, protection and management of soil resources. The digital soil mapping method based on the soil-environment relationship is an important means for quickly acquiring high-precision and high-resolution soil spatial distribution information. However, its applicability and mapping accuracy for different topographical units require further investigation. Taking Pinggu District of Beijing, China as the research area, and dividing it into two terrain units: mountainous and hilly regions and plain regions. The random forest algorithm was used to establish a model linking soil types to environmental variables, upon which digital mapping of soil types was conducted for both terrain units, with mapping accuracy being compared subsequently. The results showed that the overall accuracy (OA) of digital mapping for soil group, subgroup, soil genus and soil species in mountainous and hilly regions was 100%, 93.1%, 89.7% and 75.9%, respectively, whereas the OA of soil group, subgroup, soil genus and soil species in plain regions was 73.7%, 55.3%, 52.6% and 23.7%, respectively. These results indicated that digital mapping of soil types using environmental variables performed well in mountainous and hilly regions, but its efficacy noticeably diminished in plain regions. As the classification units of soil types became more granular, transitioning from soil groups to soil species, the accuracy of soil type mapping supported by environmental variables was gradually decreased. It was recommended to make full use of readily available environmental variable data to enhance the accuracy of soil type mapping in mountainous and hilly regions. In plain regions, it was necessary to appropriately increase the number of soil type profiles to improve mapping accuracy. The research result can provide practical examples and technical support for digital soil type mapping in other regions.

Abstract:In order to improve the accuracy of spatial interpolation of soil nutrients in farmland and accurately grasp the spatial distribution characteristics of soil nutrients, variable screening were performed by using Pearson correlation coefficient, variance inflation factor and extreme gradient boosting algorithms. Then, decision tree, random forest, radial basis function and long short-term memory were used with ordinary Kriging to interpolation the content of soil nutrients in the farmland. The results showed that the soil organic matter, total nitrogen, available phosphorus, and available potassium contents in the study area ranged from 0.226 g/kg to 32.275 g/kg, 0.117 g/kg to 1.272 g/kg, 3.159 mg/kg to 53.884 mg/kg, and 81.510 mg/kg to 488.422 mg/kg, respectively, with moderate variability. PCC, VIF and XGBoost variable screening all showed that soil organic matter, total nitrogen, available phosphorus and available potassium had some correlation among them and can be used as environmental variables for the spatial interpolation of target attributes. XGBoost method can more effectively screen out the environmental variables that were important to the spatial interpolation results, and the accuracy of the model built after screening variables based on this method was significantly better than the accuracy of the model built after screening variables by PCC and VIF. Moreover, the accuracy of the machine learning model with the synergistic environmental variables was generally better than the accuracy of the OK model without environmental variables, and the accuracy of the spatial interpolation model for the same soil nutrient content showed the following order: RF>LSTM>RBF>DT>OK. Using the RF model to invert soil nutrients in the study area, it was found that the soil organic matter and total nitrogen higher content was mainly concentrated in the southern and eastern regions of the study area, the available phosphorus and available potassium lower content in the southeastern and north-central regions. In summary, the XGBoost variable screening method combined with RF model can better realize the spatial interpolation of soil nutrients, and can be used as an effective method for the spatial interpolation of soil nutrients.

Abstract:Nitrogen is a key factor that affects crop growth. The basis for the implementation of various agricultural water and fertilizer management technologies is the accurate determination of soil nitrogen content. Soil nitrogen content could be detected quickly by the visible-near-infrared spectroscopy technology. The bottleneck that limits the application of spectral technology in soil nitrogen test is the accuracy and generalizability of predictive models. In order to improve the prediction accuracy and generalization ability, a soil nitrogen content prediction model was proposed based on sparse self-attention and visible-near-infrared spectroscopy, which was called VNIRSformer. The model consisted of input layer, embedding layer, encoder, decoder, prediction layer and output layer. The land use/cover area frame statistical survey dataset (LUCAS) was used to train model to improve its generalization ability. The performance of VNIRSformer was tested at 15 different spectral wavelength intervals, and the result showed that as the wavelength interval was increased, the model prediction accuracy was firstly increased and then decreased, and the model size was reduced. The model prediction accuracy was the lowest at the wavelength interval of 1 nm, where the RMSE was 0.47 g/kg and the R2 was 0.78. The highest predictive accuracy of the model was for the 5 nm wavelength interval, of which the RMSE was 0.35 g/kg and the R2 was 0.89. The greatest reduction in model size was observed when the wavelength interval was increased from 0.5 nm to 1 nm, which was decreased by 72%. The model size was decreased uniformly at a rate of 5% as the wavelength interval was increased from 1 nm to 5 nm. Considering the model size and performance, the optimal wavelength interval was set to be 5 nm. When compared with six different prediction models (two convolutional neural networks, traditional self-attention model，partial least squares regression, support vector machine regression, and K-nearest neighbor regression), the VNIRSformer model had the best performance, with RMSE of 0.35 g/kg, R2 of 0.89 and RPD was 2.95. To test the adaptability of VNIRSformer to predict the soil nitrogen content at different grades, it was found that VNIRSformer had high prediction accuracy for soil nitrogen content below 5 g/kg. VNIRSformer was directly applied to self-collected datasets to verify the model’s generalization ability. R2 was decreased by 0.17, indicating that VNIRSformer had a certain generalization ability. The research results indicated that spectral data with a wavelength interval of 5 nm was selected as input of VNIRSformer, which had the best prediction performance and moderate scale. Sparse attention mechanism was able to improve model prediction accuracy and reduce model training time. The VNIRSformer model had a certain generalization ability. The results could provide support for the practical application of field soil nitrogen content prediction based on visible-near-infrared spectroscopy technology.

Abstract:Monitoring decomposition process of crop straw is essential for both soil improvement and carbon sequestration, but it is challenging due to the complexity of straw decomposition products. To address this issue, CO2 emission was used as an indicator to reflect the dynamic characteristics of straw decomposition. In addition, considering the high cost of commercially available CO2 sensors, a monitoring system was designed and developed based on small, low-cost non-dispersive infrared (NDIR) CO2 sensors, environmental sensors, and Arduino. Using the commercial CO2 recorder TR-76Ui as a reference, each low-cost NDIR CO2 sensor was tested and calibrated. The linear regression model of 1 829 data points yielded coefficients of determination (R2) between 0.97 and 0.99, and RMSE between 14.56 μL/L and 56.36 μL/L, indicating good detection accuracy and stability of the low-cost NDIR CO2 sensors. The CO2 concentration inside the straw pile exhibited a periodic variation pattern, which was consistent with temperature variations, and the amplitude of vibration was gradually weakened as the straw dried. This phenomenon reflected the biological rhythm of microbial activity, revealing that the CO2 changes within the straw pile can indicate the decomposition behavior of the straw. The analysis showed that the rate of straw decomposition was positively correlated with temperature and moisture content, with R2 values of 0.8137 and 0.892, respectively. After adjusting the straw moisture content to over 40%, the decomposition rate rapidly declined and stabilized by the fourth day. When microbial inoculants were added, the decomposition rate was significantly decreased on the seventh day and stabilized by the twelfth day. Throughout the monitoring period, the sensors exhibited no significant drift and maintained good stability. The detection accuracy met the analytical needs of straw decomposition experiments, and the small, low-cost hardware showed great potential for widespread application in dynamic straw decomposition monitoring.

Abstract:In response to the practical issues of labor-intensive and low automation levels in particle feed size detection, a vibratory rectification-based particle feed size detection device was designed by using image processing technology. A simulation model of the interaction between the particle feed and the chute-type rectification plate was established by using the discrete element method software Rocky Dem. The amplitude, vibration frequency, installation angle, and chute deflection coefficient of the rectification plate were considered as experimental factors, while the average slip velocity and coefficient of slip velocity variation of the particle feed were used as evaluation indicators. Through single-factor simulation experiments, the slip motion characteristics of the particle feed within the chute-type rectification plate were analyzed. The results showed that the main factors influencing the particle feed’s slip motion characteristics were the amplitude, vibration frequency, and installation angle. Orthogonal combination simulation experiments were conducted to establish mathematical models between the experimental factors and evaluation indicators, followed by parameter optimization of the models. The optimal configuration was found to be an installation angle of 6.33°, a vibration frequency of 101.49 Hz, and an amplitude of 0.50 mm. Under these conditions, the particle feed achieved the best overall performance with an average slip velocity of 0.31 m/s and a coefficient of slip velocity variation of 4.10%. Particle feed samples with aperture sizes of 3 mm, 4 mm, and 5 mm were collected to validate the measurement accuracy of the detection device. Compared with manual measurements, the average absolute errors (MAE) of the automatic diameter measurements were 0.048 mm, 0.020 mm, and 0.012 mm, respectively. The MAE of the automatic length measurements were 0.164 mm, 0.162 mm, and 0.103 mm, respectively. The research results demonstrated that the designed particle feed size detection device exhibited good accuracy and reliability, meeting the requirements of practical production inspection.

Abstract:During the grain drying production, the heat transfer loss was great, and flue gas heat can not be efficiently transferred, resulting in low heat transfer efficiency and other problems. The gas-phase rotary heat exchanger integrating shell-and-shell and shell-and-tube heat exchanger was taken as the research object, and the key components of the spiral grooved tube were optimized based on the field synergy and thermodynamic theory. The influence of spiral grooved tube parameters on heat transfer performance was investigated. Pitch, groove depth and inner/outer diameter ratio were taken as test factors, and nussle number and resistance coefficient were used as evaluation indexes. Three-factor five-level quadratic orthogonal rotary combination experiments were carried out. Multi-objective optimization method was used to determine the optimal parameter combination, and nussle number was 164.637 and resistance coefficient was 0.348 when the pitch was 24.845 mm, the groove depth was 1.753 mm, and the inner/outer diameter ratio was 0.897. Verification experiments were carried out on the optimization results, and the experimental results were basically consistent with the optimization results. The average field synergy angle of the spiral grooved tube was decreased by about 2° compared with that of the circular tube, revealing the distribution characteristics of the field synergy angle in the inlet, outlet and intermediate sections of the spiral grooved tube bundle placed in the shell range, and the field synergy effect was increased in the overall range, which was concluded to be in line with the field synergy principle. The enhanced heat transfer comprehensive performance index was utilized for evaluation, and the results showed that the thermal performance factor of spiral grooved tube heat exchanger was between 1.031 and 1.267, which verified the rationality of spiral grooved tube in heat exchanger application.

Abstract:Blockchain traceability is essential for ensuring food safety, improving the quality of agricultural products, and safeguarding consumer rights. Aiming at the security and protection needs of private data in the agricultural product supply chain, an attribute-based searchable encryption access control method for agricultural product blockchain traceability privacy data was proposed, which allowed the data owners of the traceability supply chain to encrypt access control policies in access control by using attribute-based searchable encryption technology. Traceability supply chain data requestors generated trap gates to match encryption policies to ensure the security of access control, effectively resisted the behavior of malicious nodes to forge information and illegally obtained permissions, hided user identities, avoided policy information leaks, and ensured the security of private data. Using ethereum proof of authority consensus mechanism to build a private chain for simulation experiments, the system test results showed that the generation time of searchable ciphertext was 2.5 ms, the generation time of trap gate was 39.8 ms, and the matching time of searchable ciphertext and trap gate was 8.6 ms. At the same time, the generation time of ciphertext did not increase linearly with the increase of number of attributes, and it had the characteristics of stability. The average time to upload searchable ciphertexts and traps to the blockchain was 2 033 ms, and the time to query for matches was 3.54 ms. Therefore, the access control method proposed can realize the hiding of access control policies, ensure the safe sharing of traceability privacy data, which was suitable for agricultural blockchain traceability.

Abstract:In response to the current lack of full power shift transmissions for heavy duty tractors and the issues of complex shifting operations, high fuel consumption, and poor adaptability in traditional tractors in China, a fully automated shifting transmission was designed for 220 kW tractors. Standard automatic shifting strategies for road transportation mode and plowing operation mode were developed based on the control core of economy and power for different working modes of tractors. These strategies enabled fully automatic shifting on the foundation of a full power shift transmission. To further enhance the adaptability of automatic shifting strategies under different working conditions, intelligent automatic shifting strategies were formulated. In the road transportation automatic shifting strategy, the total fuel consumption was taken as the optimization objective, with shifting point speed and shifting delay time serving as design variables. A genetic algorithm was employed to optimize the control strategy. In the plowing operation automatic shifting strategy, two corrective parameters, namely, slip rate variation and vehicle speed variation, were introduced to dynamically adjust the downshifting point speed by using fuzzy control rules. A tractor simulation model was built by using AMESim and Matlab/Simulink, and simulations of automatic shifting strategies under different working conditions were conducted. The simulation results demonstrated that compared with the standard automatic shifting strategy, the total fuel consumption of the tractor under the road transportation condition was decreased by 6.71%, while the number of shifts was decreased by 36.5% and the total fuel consumption was decreased by 2.85% under plowing operation conditions. Finally, the effectiveness of the road transportation mode automatic shifting strategy was validated through bench tests.

Abstract:To overcome the challenges of slow response, poor control accuracy, and poor anti-interference performance of in-wheel motor, a super-twisting sliding mode control (STSMC) in-wheel motor speed and torque joint control strategy was developed based on online state estimation of speed and angle, which was based on the sliding mode control (SMC). Under the single-lane change condition, the speed of each in-wheel motor was controlled when the vehicle changed lanes at a constant speed, meeting the Ackermann steering requirements. After lane changing, the torque of each in-wheel motor was controlled by using a sinusoidal transition method, allowing the motor to quickly and smoothly output the desired torque and accelerate the vehicle in a straight line. To prevent errors or damage of the speed and angle sensors, the maximum correlation entropy square root generalized high-order cubature Kalman filter (MCSRGHCKF) was used for sensorless estimation of the speed and rotor angle. Based on experimental testing, the rotor angle estimation error was -0.05 rad, and speed error was 0.3 r/min, both of which met the motor control requirements. From the start of the motor to the uniform speed operation stage, the STSMC algorithm was used for control, with speed overshoot of 6.33%, maximum output torque of 0.35 N·m, response time of 0.22 s, steady-state speed ripple of ±0.5 r/min, and torque ripple of ±0.01 N·m. Compared with PID and SMC algorithms, the control effect was better. In the speed switching torque control, the torque can smoothly transition according to the sine function, with maximum overshoot of only 2.86%, and the motor ran smoothly.

Abstract:The traditional hydraulic system used a sliding spool to control the inlet and outlet oil ways of the hydraulic actuator at the same time, as a result that the system had high consumption, low efficiency, and was prone to cavitation under active load conditions. In order to prevent cavitation phenomenon of hydraulic cylinder under the active load condition, the anti-cavitation characteristics of four hydraulic systems were analyzed theoretically, including the traditional hydraulic system, the independent metering control system, the independent metering control system with inlet differential pressure compensation and the independent metering control system with outlet differential pressure compensation. The relationship between the opening ratio μ of inlet and outlet valves and parameters such as air separation pressure Pm and load force FL was obtained. Four hydraulic system simulation models were established, and the same load force FL, inlet valve opening K1 and other simulation parameters were set, the simulation results showed that in addition to the traditional hydraulic system, the other three independent metering control systems can avoid cavitation phenomenon by changing the opening ratio μ of inlet and outlet valves, and the independent metering control system with outlet differential pressure compensation did not need to test the load force FL. Finally, the feasibility of the simulation results was verified by the experiments.

Abstract:Centralized hydraulic systems such as load-sensing electro-hydraulic control (LS) systems realize flow distribution through multiple valves, resulting in large throttling losses and low energy efficiency. The distributed independent electro-hydraulic control system (DIEHCS) adopted the displacement control mode of single pump and single actuator, which basically eliminated throttling loss and had remarkable energy-saving effect, but there were many problems such as large installed power of independent drive and uncompact distributed installation structure. Therefore, a distributed independent secondary-pressure-regulation electro-hydraulic control system (DIEHCS-SPR) was proposed for a 6 t excavator. The system consisted of three open electro-hydraulic actuators (EHAs) which independently arranged on the manipulator and a constant pressure main pump (arranged in the cabin). Each EHA achieved four-quadrant operation according to the expected actuator speed and load direction. The simulation results of 6 t excavator virtual prototype show that proposed DIEHCS-SPR was compared with LS system, the energy saving rate was up to 42%~46% under the same working cycle. Compared with the existing DIEHCS, the proposed system can reduce the peak output power of each EHA by up to 70%~74%, resulting in a significant reduction in individual EHA dimensions and weight, while saving key component (EHA) manufacturing costs and additional energy consumption from self-weight during distributed installation.