The discrete element method (DEM) has been widely applied as a vital auxiliary technique in the design and optimization processes of agricultural equipment, especially for simulating the behavior of granular materials. In this study, the focus is placed on accurately calibrating the simulation contact parameters necessary for the V7 potato minituber seed DEM simulation. Firstly, three mechanical tests are conducted, and through a combination of actual tests and simulation tests, the collision recovery coefficient between the seed and rubber material is determined to be 0.469, the static friction coefficient is 0.474, and the rolling friction coefficient is 0.0062. Subsequently, two repose angle tests are carried out by employing the box side plates lifting method and the cylinder lifting method. With the application of the response surface method and a search algorithm based on Matlab 2019, the optimal combination of seed-to-seed contact parameters, namely, the collision recovery coefficient, static friction coefficient, and rolling friction coefficient, is obtained, which are 0.500, 0.476, and 0.043, respectively. Finally, the calibration results are verified by a seed-falling device that combines collisions and accumulation, and it is shown that the relative error between the simulation result and the actual result in the verification test is small. Thus, the calibration results can provide assistance for the design and optimization of the potato minituber seed planter.

This study aims at resolving the problem of anomaly identification for network flows in Internet of Things (IoT), and propose a novel netflow detection approach. Accurate recognition of anomaly is of fundamental importance to numerous network activities including cyberattack monitoring and intrusion prevention. In the past decades, a plethora of techniques have been presented to address this issue. Furthermore, the difference between the anomalous and normal networking activities is trivial in common. Therefore, the machine learning-based algorithms might be incapable of dealing with it. Bearing this in mind, a transformation-based pipeline is introduced to reveal the inconspicuous anomalies. The main idea behind our scheme is to apply various transformations upon data samples would be beneficial to accurate anomaly detection. The comparison experimental results indicate that our work could outperform the accuracy of state-of-the-art techniques.

Accurate path planning was the prerequisite for autonomous navigation of agricultural vehicles. An Unmanned Aerial Vehicle (UAV)-based coverage path planning was developed in this research for automating guidance of agricultural vehicles and reducing the operator maneuver in the creation of navigation maps. High-resolution orthophoto maps of the field were constructed by using low-altitude UAV photogrammetry to obtain spatial information. Travel paths and working paths were automatically generated from anchor points selected by the operator under the image coordinate domain. The navigation path for unmanned agricultural vehicles was generated by Mercator projection-based conversion for the anchor pixel coordinates into latitude and longitude geographic coordinates. A Graphical User Interface (GUI) was developed for path generation, visualization, and performance evaluation, through which the proposed path planning method was implemented for autonomous agricultural vehicle navigation. Calculation accuracy tests demonstrated the mean planar coordinate error was 2.23 cm and the maximum error was 3.37 cm for path planning. Field tests showed that lateral navigation errors remained within ±5.5 cm for the unmanned high-clearance sprayer, which indicated that the developed UAV-based coverage path planning method was feasible and featured high accuracy. It provided an effective solution for achieving fully autonomous agricultural vehicle operations.

In order to simulate and analyze the mechanized seeding process of sesbania seeds by using a discrete element method and improve the reliability of the discrete element simulation test, the contact parameters of sesbania seeds were calibrated in combination with the actual seed drop test and the simulated seed drop test. Through measurements, the intrinsic parameters of the sesbania seed were determined, and the discrete element simulation model of the sesbania seed was established. A measuring device that can simultaneously measure the resting angle and the stacking angle of the population was designed. The rest angle error and the stack angle error of the actual seed drop test and the simulated seed drop test were used as test indicators. The Plackett–Burman test screened out the contact parameters that had a significant effect on the test indicators. The response surface method (RSM) was used to carry out the three-factor quadratic rotation orthogonal combination test, and the mathematical regression model between the significant contact parameters and the test indexes was established. The optimal combination of contact parameters was determined using the multi-objective optimization method as follows: collision recovery coefficient between seeds of 0.463, static friction coefficient between seeds of 0.520, and rolling friction coefficient between seeds of 0.072. The discrete element model and calibration parameters of sesbania seeds were tested and verified using a fluted roller feed mechanism. The results showed that the average relative error between the measured value and the simulated value of the mass flow rate of the sesbania seeds was 2.74%, less than 5%, indicating that the discrete element simulation model of sesbania seeds and the calibration results of the contact parameters had high accuracy and reliability, which could be used for the discrete element simulation test of sesbania seeds.

Development of the electric automatic steering system for agricultural vehicles