Dynamic characteristics of multi-arc thermal plasma in four types of electrode configurations

The enhanced volume of thermal plasma is produced by a multi-arc thermal plasma generator with three pairs of discharge electrodes driven by three directed current power suppliers.Combined with a high-speed camera and an oscilloscope,which acquire optical and electric signals synchronously,the dynamic behavior of different kinds of multi-arc discharge adjusted by the electrode arrangement is investigated.Also,the spatial distributions and instability of the arc discharge are analyzed in four electrode configurations using the gray value statistical method.It is found that the cathodic arcs mainly show a contracting state,while the anodic arcs have a trend of transition from shrinkage to a diffusion-like state with the increase of the discharge current.As a result of the adjustment of the electrode configuration,a high temperature region formed in the center of the discharge region in configurations of adjacent electrodes with opposite flow distribution and opposite electrodes with swirl flow distribution due to severe fluctuation of arcs.The discharge voltage rises with increased discharge current in this novel multi-arc plasma generator.It is also found that anode ablation mainly occurs on the conical surface at the copper electrode tip,while cathode erosion mainly occurs on the surface of the inserted tungsten and the nearby copper.

Maize(Zea mays L.)seedling detection based on the fusion of a modified deep learning model and a novel Lidar points projecting strategy

Accurate crop detection is the prerequisite for the operation of intelligent agricultural machinery.Image recognition usually lacks accurate orientation information,and Lidar point clouds are not easy to distinguish different objects.Fortunately,the fusion of images and Lidar points can complement each other.This research aimed to detect maize(Zea mays L.)seedlings by fusing Lidar data with images.By applying coordinate transformation and time stamps,the images and Lidar points were realized homogeneous in spatial as well as temporal dimensions.Deep learning was used to develop a maize seedling recognition model,then the model recognized maize seedlings by labeling them with bounding boxes.Meanwhile,Lidar points were mapped to the bounding boxes.Only one-third of points that fell into the right middle of bounding boxes were selected for clustering operation,the calculated center of the cluster provided spatial information for target maize seedlings.This study modified the classical single shot multi-box detector(SSD)by merely linking the last feature map to the final output layer,owing to the higher feature maps having the unique advantages of detecting relatively larger objects.In images,maize seedlings were just the largest objects owing to be shot on purpose.This modification enabled the recognition model to finish recognizing an image by only consuming around 60 ms,which saved about 10 ms/image compared with the classical SSD model.The experiment was conducted in a maize field,and the maize was during the elongation stage.Experimental results demonstrated that the standard deviations for maximum distance error and maximum angle error were 1.4 cm and 1.1°,respectively,which can be tolerated under current technical requirements.Since agricultural fields are subject to staple crop-orientated and changeable ambient environment,the fusion of images and Lidar points can derive more precision information,and make agricultural machinery smarter.This study can act as an upstream technology for other researches on intelligent agricultural machinery.

Optimized design and experiment of spiral-type intra-row weeding actuator for maize(Zea mays L.)planting

Mechanical weeding not only avoids crop herbicide residue but also protects the ecological environment.Compared with mechanical inter-row weeding,mechanical intra-row weeding needs to avoid crop plants,which is conducive to causing a higher rate of seedling damage.In order to realize maize(Zea mays L.)intra-row weeding,a maize intra-row weeding mechanism was designed in this study.The mechanism can detect maize seedlings by infrared beam tube,then a sliding-cutting bevel tool moves spirally amid maize seedlings,so as to eradicate intra-row weeds.A field experiment was conducted under the following experimental conditions:the bevel tool rotation speed was 800-1400 r/min,the mechanism forward speed was 4-7 km/h,and the bevel tool depth was 2-14 cm,the experimental results illustrated that the mechanism’s average weeding rate and seedling damage rate were 95.8%and 0.6%,respectively.The variance analysis showed that the primary and secondary factors that affecting the weeding rate and seedling damage rate were the same,which were bevel tool rotation speed,mechanism forward speed,bevel tool depth in soil in a descending order according to the significances.The result of the field experiment may provide a reference for intra-row weeding device design.