Seeding Performance Simulations and Experiments for a Spoon- Wheel Type Precision Cottonseed-Metering Device Based on EDEM

To study the effects of seed metering on seeding performance under different motion parameters,a simulation model for a spoonwheel type seeder was established.A seed meter was tested by using EDEM(Engineering Discrete Element Method)software to simulate its working process at different speeds and tilt angles.The trajectories of individual cottonseeds in the seed-metering device were obtained,concurrently,the stress trend in the grain group was determined as a function of time.The simulation results suggest that at larger speeds,the metering index of the seed meter gradually decreases,while the index and the missing index gradually increase.As the tilt angle increased,the multiples index and missing index gradually decreased,while the multiples index gradually increased.When the seed meter speed reached 50 r/min and the tilt angle was 15°,the seed meter had a relatively good working performance with a seed spacing acceptance index of 92.59%,a multiples index of 1.85%,and a missing rate index of 5.56%.The seed meter was tested on a bench by using a JPS-12 performance-tester bench.At the aforementioned speed and angle,the coefficient of variation for the cottonseed spacing was 2.1%.The field trial results indicated that the multiples and the missing rates were higher than those for the tester bench but still met a passing rate of more than 90%.The coefficient of variation for the seed spacing was less than 10%,suggesting that the design could be used for field sowing.The resulting seeding uniformity was higher under these conditions,which indicates that the seed meter has a better working performance and the bench has a good seeding effect.

Geometrical design of variable ratio tooth profile based on Boolean subtraction operation and a novel modification method

Variable transmission ratio racks show great potential in rice transplanters as a key component of variable transmission ratio steering to balance steering portability and sensitivity.The objective of this study was to develop a novel geometrical design method to achieve quick,high-quality modeling of the free curvilinear tooth profile of a variable transmission ratio rack.First,a discrete envelope motion 3D model was established between the pinion-sector and the variable transmission ratio rack blank based on the mapping relationship between the rotation angle of the pinion-sector and the displacement of the rack,according to the variable transmission ratio function.Based on the loop Boolean subtraction operation,which removed the pinion-sector from the rack blank during all moments of the discrete motion process,the final complex changing tooth shape of the variable transmission ratio rack was enveloped.Then,since Boolean cutting residues made the variable ratio tooth surface fluctuant and eventually affected the precision of the model,this study proposed a modification method for establishing a smooth and continuous tooth profile.First,a novel fitting algorithm used approximate variable ratio tooth profile points extracted from the Boolean cutting marks and generated a series of variable ratio tooth profiles by utilizing B-spline with different orders.Next,based on a transmission stability simulation,the variable ratio tooth profile with optimal dynamic performance was selected as the final design.Finally,tests contrasting the transmission stability of the machining samples of the initial variable ratio tooth profile and the final variable ratio tooth profile were conducted.The results indicated that the final variable ratio tooth profile is more effective than the initial variable ratio tooth profile.Therefore,the proposed variable ratio tooth profile modeling and modification method for eliminating Boolean cutting residues and improving surface accuracy is proved to be feasible.

Design and experiment of automatic garlic seed directing device

Garlic planting requires a single seed with a scaly bud upward and vertically.In this study,an automatic garlic seed directing device was designed for auxiliary garlic seeding by utilizing a seed placed funnel,a correcting mechanism,and an automatic control system.Three factors influenced device performance:characteristics of garlic seeds,limited notch diameter of the seed placed funnel,and funnel taper of the seed placed funnel.The most optimal structural parameters of the seed placed funnel were determined via theoretical analysis.The research samples of Lanling and Jinxiang garlic were divided into three groups according to their respective sizes.3D design software(Solidworks)was used to simulate the above three factors and analyze their rules of action.The obtained simulation results demonstrated that the suitable range of funnel taper and limited notch diameter of seed placed funnel for the three groups of seed were from 40°to 60°and 6-8 mm,respectively.The bench test showed that when the funnel taper was 60°and the limited notch diameter was 10 mm,the success rates of the three groups’seeds in terms of scaly bud directing were 89.8%,91.6%and 96.6%respectively,which satisfied the agricultural requirements of garlic planting.Accordingly,the corresponding results may promote the study and improvement of garlic intelligent planting equipment.

Detection of maize leaf diseases using improved MobileNet V3-small

In order to realize the intelligent identification of maize leaf diseases for accurate prevention and control,this study proposed a maize disease detection method based on improved MobileNet V3-small,using a UAV to collect maize disease images and establish a maize disease dataset in a complex context,and explored the effects of data expansion and migration learning on model recognition accuracy,recall rate,and F1-score instructive evaluative indexes,and the results show that the two approaches of data expansion and migration learning effectively improved the accuracy of the model.The structured compression of MobileNet V3-small bneck layer retains only 6 layers,the expansion multiplier of each layer was redesigned,32-fold fast downsampling was used in the first layer,and the location of the SE module was optimized.The improved model had an average accuracy of 79.52%in the test set,a recall of 77.91%,an F1-score of 78.62%,a model size of 2.36 MB,and a single image detection speed of 9.02 ms.The detection accuracy and speed of the model can meet the requirements of mobile or embedded devices.This study provides technical support for realizing the intelligent detection of maize leaf diseases.

Fast-growing forest pruning robot structure design and climbing control

According to the requirement of fast-growing forest pruning operation,the pruning robot was developed.The structure and control system of pruning robot were described,the work flow of pruning robot was expounded.The type and structure of the driving motor and the compression spring were decided with force-balance analysis.The tilt problem of pruning robot was resolved by ADAMS and Matlab co-simulation,and the control scheme of climbing mechanism was determined.The experiment results of the prototype indicate that pruning robot can climb tree trunk smoothly at a speed of20 mm/s and cross the raised trunk.The pruning saw which is driven by the adjustable speed motor can cut the branches of30 rnm,And the residual amount of branches is less than5 ram.Pruning robot can meet the practical requirements of the fast-growing forest pruning work.

Design of obstacle avoidance controller for agricultural tractor based on ROS

The obstacle avoidance controller is a key autonomous component which involves the control of tractor system dynamics,such as the yaw lateral dynamics,the longitudinal dynamics,and nonlinear constraints including the speed and steering angles limits during the path-tracking process.To achieve the obstacle avoidance ability of control accuracy,an independent path re-planning controller is proposed based on ROS(Robot Operating System)nonlinear model prediction in this paper.In the design process,the obstacle avoidance function and an objective function are introduced.Based on these functions,the obstacle avoidance maneuvering performance is transformed into a nonlinear quadratic optimization problem with vehicle dynamic constraints.Moreover,the tractor dynamics maneuvering performance can be effectively adjusted through the proposed objective function.To validate the proposed algorithm,a ROS based tractor dynamics model and the SLAM(Simultaneous Localization and Mapping)are established for numerical simulations under different speed.The maximum obstacle avoidance deviation in the simulation is 0.242 m at 10 m/s,and 0.416 m at 30 m/s.The front-wheel rotation angle and lateral velocity are within the constraint range during the whole tracking process.The numerical results show that the designed controller can achieve the tractor obstacle avoidance ability with good accuracy under different conditions.

Trajectory tracking control of agricultural vehicles based on disturbance test

To improve the trajectory tracking robust stability of agricultural vehicles,a path tracking control method combined with the characteristics of agricultural vehicles and nonlinear model predictive control was presented.Through the proposed method,the path tracking problem can be divided into two problems with speed and steering angle constraints:the trajectory planning problem,and the trajectory tracking optimization problem.Firstly,the nonlinear kinematics model of the agricultural vehicle was discretized,then the derived model was inferred and regarded as the prediction function plant for the designed controller.Second,the objective function characterizing the tracking performance was put forward based on system variables and control inputs.Therefore,the objective function optimization problem,based on the proposed prediction equation plant,can be regarded as the nonlinear constrained optimization problem.What’s more,to enhance the robust stability of the system,a real-time feedback and rolling adjustment strategy was adopted to achieve optimal control.To validate the theoretical analysis before,the Matlab simulation was performed to investigate the path tracking performance.The simulation results show that the controller can realize effective trajectory tracking and possesses good robust stability.Meanwhile,the corresponding experiments were conducted.When the test vehicle tracked the reference track with a speed of 3 m/s,the maximum lateral deviation was 13.36 cm,and the maximum longitudinal deviation was 34.61 cm.When the added horizontal deviation disturbance Yr was less than 1.5 m,the controller could adjust the vehicle quickly to make the test car return to the reference track and continue to drive.Finally,to better highlight the controller proposed in this paper,a comparison experiment with a linear model predictive controller was performed.Compared to the conventional linear model predictive controller,the horizontal off-track distance reduced by 36.8%and the longitudinal deviation reduced by 32.98%when performing circular path tracking at a speed of 3 m/s.

Recognition of cotton growth period for precise spraying based on convolution neural network

Dynamic acquisition of crop morphology is beneficial to real-time variable decision of precise spraying operations in fields.However,the existing spraying quantity regulation has high tolerance on the statistical characteristics of regional morphology,so expensive LiDAR and ultrasonic radar can’t make full use of their high accuracy,and can reduce decision speed because of too much detail of branches and leaves.Therefore,designing a novel recognition system embedded machine learning with low-cost monocular vision is more feasible,especially in China,where the agricultural implements are medium sizes and cost-sensitive.In addition,we found that the growth period of crops is an important reference index for guiding spraying.So,taking cotton as a case study,a cotton morphology acquisition by a single camera is established,and a cotton growth period recognition algorithm based on Convolution Neural Network(CNN)is proposed in this paper.Through the optimization process based on confusion matrix and recognition efficiency,an optimized CNN model structure is determined from 9 different model structures,and its reliability was verified by changing training sets and test sets many times based on the idea of kfold test.The accuracy,precision,recall,F1-score and recognition speed of this CNN model are 93.27%,95.39%,94.31%,94.76%and 71.46 ms per image,respectively.In addition,compared with the performance of VGG16 and AlexNet,the convolution neural network model proposed in this paper has better performance.Finally,in order to verify the reliability of the designed recognition system and the feasibility of the spray decision-making algorithm based on CNN,spraying deposition experiments were carried out with 3 different growthperiods of cotton.The experiments’results validate that after the optimal spray parameters were applied at different growth periods respectively,the average optimum index in 3 growth periods was 42.29%,which was increased up to 62.24%than the operations without distinguishing growth periods.

Dynamic detection method for falling ears of maize harvester based on improved YOLO-V4

Traditional maize ear harvesters mainly rely on manual identification of fallen maize ears,which cannot realize real-time detection of ear falling.The improved You Only Look Once-V4(YOLO-V4)algorithm is combined with the channel pruning algorithm to detect the dropped ears of maize harvesters.K-means clustering algorithm is used to obtain a prior box matching the size of the dropped ears,which improves the Intersection Over Union(IOU).Compare the effect of different activation functions on the accuracy of the YOLO-V4 model,and use the Mish activation function as the activation function of this model.Improve the calculation of the regression positioning loss function,and use the CEIOU loss function to balance the accuracy of each category.Use improved Adam optimization function and multi-stage learning optimization technology to improve the accuracy of the YOLO-V4 model.The channel pruning algorithm is used to compress the model and distillation technology is used in the fine-tuning of the model.The final model size was only 10.77%before compression,and the test set mean Average Precision(mAP)was 93.14%.The detection speed was 112 fps,which can meet the need for real-time detection of maize harvester ears in the field.This study can provide technical reference for the detection of the ear loss rate of intelligent maize harvesters.

Fertilizer sphericity measuring device based on equatorial and meridian circles

Fertilizer sphericity is an important assessment index of appearance quality that affects the fertilization effect.A fertilizer sphericity measuring device based on machine vision was designed aimed at low precision and heavy workload of manual fertilizer measurement,and high cost and complicated operation of high precision measuring instruments.A fertilizer sphericity measuring method based on equatorial and meridian circles was proposed.The device works in an intermittent static acquisition mode to simultaneously obtain both top and side images of a single fertilizer.First,the method performs gamma correction on the top and side images of the single fertilizer,and uses the Canny operator to detect the edge of the image to obtain the equatorial and meridian circular contour images of the fertilizer.Second,based on the fertilizer equatorial and meridian circular contour,the Least Squares Circle method was used to evaluate the roundness of the single fertilizer.Finally,the average roundness value of the equatorial and meridian circles was used as the final sphericity value of the fertilizer.The sphericity measurement test was carried out on the same batch of compound,organic and biological fertilizers by using the sphericity measuring device.The fertilizer sphericity data were obtained by different measurement and evaluation methods.The variation coefficient was used to evaluate the difference in fertilizer sphericity measured by different sphericity measurement and evaluation methods.The results show that among the different measurement and evaluation methods,the coefficient of variation of fertilizer sphericity measured by the equatorial and meridian circle method was the smallest,and the coefficient of variation of sphericity measured by the Least Squares Circle method was the smallest and accurate.This study shows that the sphericity measuring device and method can accurately measure the fertilizer sphericity,and has a significant potential to facilitate fertilizer production and quality inspection.

Fabrication of Superhydrophobic–Hydrophilic Patterned Cu@Ag Composite SERS Substrate via Femtosecond Laser

Ultralow concentration molecular detection is critical in various fields,e.g.,food safety,environmental monitoring,and dis-ease diagnosis.Highly sensitive surface-enhanced Raman scattering(SERS)based on ultra-wettable surfaces has attracted attention due to its unique ability to detect trace molecules.However,the complexity and cost associated with the preparation of traditional SERS substrates restrict their practical application.Thus,an efficient SERS substrate preparation with high sensitivity,a simplified process,and controllable cost is required.In this study,a superhydrophobic–hydrophilic patterned Cu@Ag composite SERS substrate was fabricated using femtosecond laser processing technology combined with silver plating and surface modification treatment.By inducing periodic stripe structures through femtosecond laser processing,the developed substrate achieves uniform distribution hotspots.Using the surface wettability difference,the object to be measured can be confined in the hydrophilic region and the edge of the hydrophilic region,where the analyte is enriched by the coffee ring effect,can be quickly located by surface morphology difference of micro-nanostructures;thus,greatly improving detec-tion efficiency.The fabricated SERS substrate can detect Rhodamine 6G(R6G)at an extraordinarily low concentration of 10^(−15)mol/L,corresponding to an enhancement factor of 1.53×10^(8).This substrate has an ultralow detection limit,incurs low processing costs and is simple to prepare;thus,the substrate has significant application potential in the trace analysis field.