Visualization of MF/Diesel RCCI Combustion Process and Soot Emission

Based on the optical engine,the ignition characteristics,combustion process and soot emission characteristics of diesel under different 2-Methylfuran(MF) atmospheres were investigated by high-speed photography and in-cylinder combustion analysis technology.The results show that at the same main injection timing,the ignition time of reactivity controlled compression ignition(RCCI) combustion mode is earlier than pure diesel combustion,and the ignition point is concentrated near the nozzle.Diesel acts as a spark plug to ignite the mixture,but the flame develops slowly in the early stages and the pressure in the cylinder rises slowly.Compared with pure diesel,RCCI combustion model has smaller peak values of in-cylinder pressure and heat release rate,shorter ignition delay period,earlier combustion phase and shorter combustion duration.At main spray time at 6℃A BTDC and 12℃A BTDC,with the increase of MF premixing ratio from 0 to 0.75,the peak cylinder pressure decreased by 19.6% and 26% respectively.In addition,with the increase of the MF heat value ratio,the area of KL factor> 1.5 in the combustion chamber decreased and the space integral natural luminescence(SINL) peak value decreased by 48.37%,and the soot formation rate and yield decreased significantly.However,when the MF heat value ratio was too large(75% of the total calorific value),the ignition delay period increased,and misfire occurred at the main injection timing of 0℃A BTDC.The RCCI mode of MF/diesel dual fuel has better stability,and better control effect can be obtained at different main inj ection timing.

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.

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.

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.